The pial arterial vasculature of the cerebrum consists of smaller distal arteries and arterioles that cover the cerebral cortical surface, and connects the branches of the three major supplying arteries of the cerebrum—the anterior, middle, and posterior cerebral arteries—with the penetrating intracortical arterioles, which deliver blood into the cortical grey matter (Cipolla, 2009; Jones, 1970). Notably, the pial arterial vasculature is the only source of blood supply to the neocortex (Mchedlishvili and Kuridze, 1984), and its redundancies are organized in three tiers: global re-routing of blood through the circle of Willis, intermediate anastomoses between branches originating from the three major arteries, and local loops formed by pial anastomoses within the same branch (Blinder et al., 2010; Duvernoy et al., 1981).

Although extensive anatomical studies have described topological properties and the relevant constituents of the pial arterial vasculature (Pfeifer, 1930; Szikla et al., 1977), quantitative data of the human pial arterial vasculature remain scarce (Cassot et al., 2006; Helthuis et al., 2019; Hirsch et al., 2012; Payne, 2017; Schmid et al., 2019). The by far still ‘most comprehensive and influential work’ (Hirsch et al., 2012) is the detailed description of the pial vasculature by Duvernoy et al., 1981, which examined 25 brains using intravascular ink injections. As indispensable as this dataset has been, 3D reconstructions of the vascular network and surrounding anatomy were not provided in this study. A second recent analysis performed by Helthuis et al., 2019, used corrosion casts from four brain specimens and provided valuable insights into the branching pattern of the arterial vasculature. However, only limited information can be obtained in this way about the morphometry of vessels, in particular their position and geometric relationship with the cortex. Further, the elaborate preparation and the limitation to ex vivo samples restrict the applicability of these methods when, for example, one wants to characterize the large variability across individuals in pial arterial morphometry (Alpers et al., 1959; Beevor and Ferrier, 1997; Cilliers and Page, 2017; Gomes et al., 1986; Papantchev et al., 2013; Stefani et al., 2000; van der Zwan et al., 1993) and function (Baumbach and Heistad, 1985; van Laar et al., 2006).

Given the central role of the pial arterial vasculature for healthy human brain function (Hirsch et al., 2012; Iliff et al., 2013; Xie et al., 2013), its impact on functional magnetic resonance imaging (fMRI) signals (Bright et al., 2020; Chen et al., 2020), and its involvement in numerous cerebrovascular diseases (Hetts et al., 2017; McConnell et al., 2016), there is a clear need to be able to image the pial arterial vasculature in individual subjects to characterize its morphometry and topology including arterial diameter and geometric relationship with the cortex (Mut et al., 2014). For example, many intracranial pathologies have selective involvement of superficial vessels (Ginat et al., 2013; Herz et al., 1975; Hetts et al., 2017; McConnell et al., 2016; Song et al., 2010; Uhl et al., 2003), and the outcome of stroke patients is heavily dependent on the status of the collateral circulation (Ginsberg, 2018; Raymond and Schaefer, 2017). Yet, existing hemodynamic modelling approaches have to synthesize the pial arterial vasculature either implicitly (Park et al., 2020) or explicitly (Ii et al., 2020) from an incomplete quantitative understanding of its morphometry and variability. In addition, modelling of hemodynamic changes in response to neural activity needs to account for the effect of pial vessels (Markuerkiaga et al., 2016; Polimeni et al., 2010; Uludağ and Blinder, 2018). Although signal changes in fMRI data using a blood-oxygenation-level-dependent (BOLD) contrast arise predominantly on the venous side of the vascular hierarchy (Ogawa et al., 1990), the strongest vascular response to neural activity is located in intracortical arteries and arterioles (Hillman et al., 2007; Vanzetta et al., 2005), and significant diameter changes in upstream arteries have been observed (Bizeau et al., 2018; Cho et al., 2012; Cho et al., 2008). With the recent interest in cerebral blood volume-based fMRI (Huber et al., 2014) and the numerous accounts of vascular contributions found in BOLD fMRI signals (Amemiya et al., 2020; Bright et al., 2020; Chen et al., 2020; Drew et al., 2020), including a detailed, subject-specific depiction of the underlying angio-architecture, would immensely augment forthcoming modelling initiatives (Havlicek and Uludağ, 2020; Tak et al., 2014).

So far, numerous approaches exist to image large, macroscopic proximal intracranial arteries such as the circle of Willis including its branches and the basal arteries with magnetic resonance imaging (MRI) (Carr and Carroll, 2012). Similarly, the density of microscopic, parenchymal vessels can be quantified through techniques such as vessel size imaging and capillary density imaging (Kiselev et al., 2005; Troprès et al., 2001; Xu et al., 2011), and newer methods for estimates of cerebral blood volume of the arteriolar side of the microvascular tree (Hua et al., 2019). However, direct non-invasive imaging of the mesoscopic vessels of the pial arterial network, that is, arteries with a diameter between 50 and 300 µm, is not in current use, either in clinical practice or in basic research.

Recent studies have shown the potential of time-of-flight (TOF)-based magnetic resonance angiography (MRA) at 7 Tesla (T) in subcortical areas (Bouvy et al., 2016; Bouvy et al., 2014; Ladd, 2007; Mattern et al., 2018; Schulz et al., 2016; Von Morze et al., 2007). In brief, TOF-MRA uses the high signal intensity caused by inflowing water protons in the blood to generate contrast, rather than an exogenous contrast agent. By adjusting the imaging parameters of a gradient-recalled echo (GRE) sequence, namely the repetition time (T_R) and flip angle, the signal from static tissue in the background can be suppressed, and high image intensities are only present in blood vessels freshly filled with non-saturated inflowing blood. As the blood flows through the vasculature within the imaging volume, its signal intensity slowly decreases. (For a comprehensive introduction to the principles of MRA, see for example Carr and Carroll, 2012.) At ultra-high field, the increased signal-to-noise ratio (SNR), the longer $T_1$ relaxation times of blood and grey matter, and the potential for higher resolution are key benefits (Von Morze et al., 2007). However, the current description of the magnetic resonance physics underlying TOF-MRA (Brown et al., 2014a; Carr and Carroll, 2012) is not tailored to imaging pial arteries, because their small diameter and complex branching pattern require particular considerations. For example, while the advantage of reduced voxel size for imaging small arteries has empirically been shown numerous times (Haacke et al., 1990; Mattern et al., 2018; Von Morze et al., 2007), the sentiment prevails (Chen et al., 2018; Masaryk et al., 1989; Mut et al., 2014; Park et al., 2020; Parker et al., 1991; Wilms et al., 2001; Wright et al., 2013) that the slower flow in small arteries should significantly diminish the TOF effect (Haacke et al., 1990; Pipe, 2001), and perhaps for that reason imaging the pial arterial vasculature in vivo has received little attention. Here, we revisit the topic of high-resolution TOF-MRA to investigate the feasibility of imaging the pial arterial vasculature in vivo at 7 T. We demonstrate, based on simulations and empirical data, that pial arteries can be detected reliably. Note that while some, particularly larger pial arterials, have undoubtedly been visualized incidentally in high-resolution images, we present here the first targeted, comprehensive account of imaging the pial arteries.

In the following, we first summarize the relevant properties of the pial arterial vasculature, in particular vessel diameter, blood velocity, and blood delivery time. With this in mind, we revisit the theory behind 3D TOF-MRA and derive optimal parameters for imaging these vessels. We then demonstrate the supra-linear relationship between vessel contrast and voxel size and explore the requirements for accurate vessel detection. This work argues that, from a physiological and theoretical perspective, it is indeed possible to image the pial arterial network using a TOF-based contrast. Subsequently, we present results from several experiments supporting our hypothesis and ultimately employ high-resolution TOF-MRA with prospective motion correction to image the pial arterial vasculature at 140 μm isotropic resolution.

Theory

Anatomical architecture of the pial arterial vasculature

The pial arterial vasculature consists of smaller arteries and arterioles on the cortical surface (Figure 1, left) (Duvernoy, 1999). Pial arteries range in diameter from 280 to 50 μm and connect to the smaller (<50 μm) intracortical arterioles. Similar values of 700–30 μm in pial arterial diameter were also reported by Nonaka et al., 2003b, who exclusively characterized the calcarine fissure. The recent work on the topology of the vascular system by Hirsch et al., 2012, might create the impression that mesoscopic pial vessels entirely cover the cortical surface (e.g. Figure 2 in Hirsch et al., 2012). However, the segmentation provided by Duvernoy, 1999, shows that pial cerebral blood vessels are rather sparse, and only approximately 17% of the human cortex is covered by arteries and 11% by veins. (To obtain a rough estimate of their density, we segmented the stylized drawing accompanying Figure 1 (Duvernoy, 1999); see also subsection Data analysis below.) Pial arteries exhibit a distinctive branching pattern on the cortical surface, in that an arterial branch arises at a nearly right angle from its parent vessel (Rowbotham and Little, 1965), usually with a significantly reduced diameter. In the absence of ground-truth data, this conspicuous feature constitutes an important prior that can be used as an image quality marker: simply, the more right-angled branches are detected, the higher the sensitivity to small arteries.

Figure 1

Download asset Open asset

From a cross-sectional point of view cutting through the cortex, pial arteries are embedded in the meningeal layers between the pia and arachnoid mater, that is, the subarachnoid space (Figure 1, right). Thus, their immediately surrounding tissue is not only grey matter, but a mixture of grey matter, cerebrospinal and interstitial fluid, and meningeal and glial tissue (Marin-Padilla, 2012). In the next section, we will propose a simplified partial-volume model to account for the small diameter of pial arteries. Nevertheless, one should keep in mind that the actual surrounding of pial arteries consists of a number of different tissue classes and fluids, with considerable differences in $T_1$ values, especially between grey matter, arterial blood, and cerebrospinal fluid (Rooney et al., 2007; Wright et al., 2008).

Along with their diameter, the blood velocity within pial arteries is an important factor determining the TOF contrast. However, blood velocity values in human pial arteries are difficult to obtain due to their small size. Using optical imaging in cats, Kobari et al., 1984, measured blood velocities in pial arteries ranging from 20 to 200 μm diameter. For example, in pial arteries with a diameter between 100 and 150 μm, the centreline velocity was 42.1 mm/s. Similar values were obtained by Nagaoka and Yoshida, 2006, for arterioles in the human retina using laser Doppler velocimetry, where the average centreline velocity for first-order arterioles with an average diameter of 107.9 μm was 41.1 mm/s. The only study in humans measuring blood velocity in small cerebral arteries was performed by Bouvy et al., 2016, using quantitative flow MRI. They estimated mean blood velocities of 39–51 mm/s in the basal ganglia, where arterial diameters range from 175 to 668 μm (Djulejić et al., 2015). Figure 2 provides a comprehensive overview of all blood velocity values and vessel diameters reported for pial arteries. Note that optical imaging methods (Kobari et al., 1984; Nagaoka and Yoshida, 2006) are expected to considerably overestimate the mean velocity within the vessel, whereas MRI-based measurements can severely underestimate the velocity values due to partial-volume effects (Bouvy et al., 2016; Hofman et al., 1995; Tang et al., 1993). This might explain the large discrepancies that are apparent in Figure 2 between optical and MRI-based methods. Due to the gradual reduction in vessel diameter along the vascular tree, the reported velocity values only apply to the very last branch, and faster velocities are expected along much of the vasculature. For example, blood velocities in the three major cerebral arteries exceed the velocity values depicted here by at least an order of magnitude (Lee et al., 1997; Molinari et al., 2006).

Figure 2

Download asset Open asset

The blood delivery time for a given voxel containing a vessel is the time it takes for the blood water spins to reach that voxel after they first enter the imaging slab, and is a crucial imaging parameter when estimating blood signal enhancement in TOF-MRA. To directly compute blood delivery time from blood velocity, one would need a detailed velocity profile of the blood and its path along the arterial tree together with the exact slab prescription for each scan. While this is not feasible, we can find an upper bound on the blood delivery time expected for pial arteries using estimates of arterial transit time used in arterial spin labelling (Detre et al., 1992). The arterial transit time, which includes the transport of blood water spins from the large carotid arteries in the neck through the pial arterial vasculature and the capillaries into the tissue, is estimated to be between 500 and 1500 ms in healthy grey matter (Alsop et al., 2015). Since the coverage or slab thickness in TOF-MRA is usually kept small to minimize blood delivery time by shortening the path length of the vessel contained within the slab (Parker et al., 1991), and because we are focused here on the pial vasculature, we have limited our considerations to a maximum blood delivery time of 1000 ms, with values of few hundreds of milliseconds being more likely.

In summary, targeting pial arteries places us into an imaging regime of mesoscopic vessels (50–300 μm diameter) in a complex branching pattern on a folded surface with blood velocities of 10–50 mm/s and blood delivery times of 200–700 ms. When revisiting the theory behind TOF-MRA for mesoscopic pial arteries, whose diameters are at the size of the imaging voxels or smaller, we thus need to consider partial-volume effects, and take into account that: (i) there is no preferential vessel orientation; (ii) while the blood delivery times are long relative to macroscopic arteries, they are still sufficiently short that the blood arrives in the pial arteries in a small fraction of the overall acquisition time; and (iii) the blood passes swiftly through each voxel (i.e. in a time which is short relative to commonly used repetition times).

Flow-related enhancement

Before discussing the effects of vessel size, we briefly revisit the fundamental theory of the flow-related enhancement (FRE) effect used in TOF-MRA. Taking into account the specific properties of pial arteries, we will then extend the classical description to this new regime. In general, TOF-MRA creates high signal intensities in arteries using inflowing blood as an endogenous contrast agent. The object magnetization—created through the interaction between the quantum mechanical spins of water protons and the magnetic field—provides the signal source (or magnetization) accessed via excitation with radiofrequency (RF) waves (called RF pulses) and the reception of ‘echo’ signals emitted by the sample around the same frequency. The ${\displaystyle T_1}$ contrast in TOF-MRA is based on the difference in the steady-state magnetization of static tissue, which is continuously saturated by RF pulses during the imaging, and the increased or enhanced longitudinal magnetization of inflowing blood water spins, which have experienced no or few RF pulses. In other words, in TOF-MRA, we see enhancement for blood that flows into the imaging volume. The steady-state longitudinal magnetization ${\displaystyle M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}$ of static tissue imaged with a (spoiled) FLASH sequence can be calculated (Brown et al., 2014a) using the Ernst equation:

(1) $M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}=\frac{M_0\left(1-{\mathrm{e}}^{-T_{\mathrm{R}}/T_1^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}\right)}{1-{\mathrm{e}}^{-T_{\mathrm{R}}/T_1^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}\cdot \cos \theta },$

with $M_0$ being the thermal equilibrium magnetization before any RF pulses, $T_{\mathrm{R}}$ the repetition time, $\theta$ the excitation flip angle, and $T_1^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}$ the longitudinal relaxation time of the tissue.

To generate a TOF contrast, a higher flip angle than the optimal Ernst angle (Ernst and Anderson, 1966) is chosen to decrease or suppress the tissue longitudinal magnetization relative to the thermal equilibrium value. The flowing blood water spins entering into the excited volume are fully relaxed, and as they flow through the imaging volume their longitudinal magnetization $M_z^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}$ just before the nth RF pulse is (Brown et al., 2014a):

(2) $M_z^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}\left(n_{\mathrm{R}\mathrm{F}}\right)=M_{z\mathrm{S}}^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}+{\left(\left(e^{-\frac{T_{\mathrm{R}}}{T_1^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}}}\right)\cdot \cos \theta \right)}^{n_{\mathrm{R}\mathrm{F}}-1}\cdot \left(M_0-M_{z\mathrm{S}}^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}\right).$

The number of RF pulses experienced by the blood, $n_{\mathrm{R}\mathrm{F}}=t_{\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{y}}{/T}_{\mathrm{R}}$, depends on the blood delivery time $t_{\mathrm{d}\mathrm{e}\mathrm{l}\mathrm{i}\mathrm{v}\mathrm{e}\mathrm{r}\mathrm{y}}$, that is, the time it takes the blood spins from entering the imaging volume to reaching the target vessel, and the repetition time $T_{\mathrm{R}}$. Hence, the longitudinal magnetization of the blood water spins is an exponentially decaying function of the number of applied RF pulses, and inversely related to the blood delivery time. We define the FRE, which is the fundamental contrast mechanism in TOF-MRA, as the difference in blood longitudinal magnetization and surrounding tissue signal relative to the tissue signal (Al-Kwifi et al., 2002):

(3) $\mathrm{F}\mathrm{R}\mathrm{E}\left(n_{\mathrm{R}\mathrm{F}}\right)=\frac{M_z^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}\left(n_{\mathrm{R}\mathrm{F}}\right)-M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}{M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}.$

In classical descriptions of the FRE effect (Brown et al., 2014a; Carr and Carroll, 2012), significant emphasis is placed on the effect of multiple ‘velocity segments’ within a slice in the 2D imaging case. Using the simplified plug-flow model, where the cross-sectional profile of blood velocity within the vessel is constant and effects such as drag along the vessel wall are not considered, these segments can be described as ‘disks’ of blood that do not completely traverse through the full slice within one $T_{\mathrm{R}}$, and, thus, only a fraction of the blood in the slice is replaced. Consequently, estimation of the FRE effect would then need to accommodate contribution from multiple ‘disks’ that have experienced 1 to $k$ RF pulses. In the case of 3D imaging as employed here, multiple velocity segments within 1 voxel are generally not considered, as the voxel sizes in 3D are often smaller than the slice thickness in 2D imaging and it is assumed that the blood completely traverses through a voxel each $T_{\mathrm{R}}$. However, the question arises whether this assumption holds for pial arteries, where blood velocity is considerably lower than in intracranial arteries (Figure 2). To comprehensively answer this question, we have computed the blood dwell time (note that the blood dwell time is inversely related to the critical velocity in 2D MRA but does not need to assume a specific $T_{\mathrm{R}}$ value), that is, the average time it takes the blood to traverse a voxel, as a function of blood velocity and voxel size (Figure 2). For reference, the blood velocity estimates from the three studies mentioned above Bouvy et al., 2016; Kobari et al., 1984; Nagaoka and Yoshida, 2006 have been added in this plot as horizontal white lines. For the voxel sizes of interest here, that is, 50–300 μm, blood dwell times are, for all but the slowest flows, well below commonly used repetition times (Brown et al., 2014a; Carr and Carroll, 2012; Ladd, 2007; Von Morze et al., 2007). Thus, in a first approximation using the plug-flow model, it is not necessary to include several velocity segments for the voxel sizes of interest when considering pial arteries, as one might expect from classical treatments, and the FRE effect can be described by Equations 1–3, simplifying our characterization of FRE for these vessels. When considering the effect of more complex flow patterns, it is important to bear in mind that the arteries targeted here are only 1 voxel thick, and signals are integrated across the whole artery.

Based on these equations, optimal ${\displaystyle T_{\mathrm{R}}}$ and excitation flip angle values ($\theta$) can be calculated for the blood delivery times under consideration (Figure 3). To better illustrate the regime of small arteries, we have illustrated the effect of either flip angle or ${\displaystyle T_{\mathrm{R}}}$ while keeping the other parameter values fixed to the value that was ultimately used in the experiments; although both parameters can also be optimized simultaneously (Haacke et al., 1990). Figure 3—figure supplement 1 further delineates the interdependency between flip angle and ${\displaystyle T_{\mathrm{R}}}$ within a parameter range commonly used for TOF imaging at ultra-high field (Kang et al., 2010; Stamm et al., 2013; Von Morze et al., 2007). Note how longer $T_{\mathrm{R}}$ values still provide an FRE effect even at very long blood delivery times, whereas using shorter $T_{\mathrm{R}}$ values can suppress the FRE effect (Figure 3, left). Similarly, at lower flip angles, the FRE effect is still present for long blood delivery times, but it is not available anymore at larger flip angles, which, however, would give maximum FRE for shorter blood delivery times (Figure 3, right). Due to the non-linear relationships of both blood delivery time and flip angle with FRE, the optimal imaging parameters deviate considerably when comparing blood delivery times of 100 and 300 ms, but the differences between 300 and 1000 ms are less pronounced. In the following simulations and measurements, we have thus used a ${\displaystyle T_{\mathrm{R}}}$ value of 20 ms, that is, a value only slightly longer than the readout of the high-resolution TOF acquisitions, which allowed time-efficient data acquisition, and a nominal excitation flip angle of 18°. From a practical standpoint, these values are also favourable as the low flip angle reduces the SAR (Fiedler et al., 2018) and the long $T_{\mathrm{R}}$ value decreases the potential for peripheral nerve stimulation (Mansfield and Harvey, 1993).

Figure 3 with 1 supplement see all

Download asset Open asset

The optimizations presented so far have assumed that the voxel is fully filled with blood. However, in the case of pial arteries, vessel diameters are often comparable to or smaller than the voxel size. In the next section, we will therefore investigate the individual contributions of blood delivery times and voxel size to the overall FRE in pial arteries. We will introduce a model that consider the partial-volume effects of blood and tissue to allow us to decide whether imaging of pial arteries is limited by the physiological properties of the brain vasculature, that is, blood flow rates, or the available image resolution.

Introducing a partial-volume model

To account for the effect of voxel volume on the FRE, the total longitudinal magnetization $M_z$ needs to also consider the number of spins contained within in a voxel (Du et al., 1996; Venkatesan and Haacke, 1997). A simple approximation can be obtained by scaling the longitudinal magnetization with the voxel volume (Venkatesan and Haacke, 1997). (Note that this does not affect the relative FRE definition presented in Equation (3), as the effect of total voxel volume cancels out.) To then include partial-volume effects, the total longitudinal magnetization in a voxel $M_z^{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}$ becomes the sum of the contributions from the stationary tissue $M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}$ and the inflowing blood $M_z^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}$, weighted by their respective volume fractions $V_{\mathrm{r}\mathrm{e}\mathrm{l}}$:

(4) $M_z^{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}\cdot M_z^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}+V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}\cdot M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}.$

For simplicity, we assume a single vessel is located at the centre of the voxel and approximate it to be a cylinder with diameter $d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}}$ and length $l_{\mathrm{v}\mathrm{o}\mathrm{x}\mathrm{e}\mathrm{l}}$ of an assumed isotropic voxel along one side. The relative volume fraction of blood $V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}$ is the ratio of vessel volume within the voxel to total voxel volume (see section Estimation of vessel-volume fraction and Figure 4—figure supplement 1), and the tissue volume fraction $V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}$ is the remainder that is not filled with blood, or

(5) $V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}=1-V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}.$

We can now replace the blood magnetization in Equation (3) with the total longitudinal magnetization of the voxel to compute the FRE as a function of vessel-volume fraction:

(6) $\mathrm{F}\mathrm{R}{\mathrm{E}}_{\mathrm{P}\mathrm{V}}\left(n_{\mathrm{R}\mathrm{F}},l_{\mathrm{v}\mathrm{o}\mathrm{x}\mathrm{e}\mathrm{l}},d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}}\right)=\frac{M_z^{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}\left(n_{\mathrm{R}\mathrm{F}},l_{\mathrm{v}\mathrm{o}\mathrm{x}\mathrm{e}\mathrm{l}},d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}}\right)-M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}{M_{z\mathrm{S}}^{\mathrm{t}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{e}}}.$

Note that this can also be written as $\mathrm{F}\mathrm{R}{\mathrm{E}}_{2\mathrm{C}}\left(n_{\mathrm{R}\mathrm{F}},l_{\mathrm{v}\mathrm{o}\mathrm{x}\mathrm{e}\mathrm{l}},d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}}\right)=V_{\mathrm{r}\mathrm{e}\mathrm{l}}^{\mathrm{b}\mathrm{l}\mathrm{o}\mathrm{o}\mathrm{d}}(l_{\mathrm{v}\mathrm{o}\mathrm{x}\mathrm{e}\mathrm{l}},d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}})\cdot \mathrm{F}\mathrm{R}\mathrm{E}\left(n_{\mathrm{R}\mathrm{F}}\right)$.

Figure 4 illustrates the partial-volume FRE ($\mathrm{F}\mathrm{R}{\mathrm{E}}_{\mathrm{P}\mathrm{V}}$) assuming a pial artery diameter ($d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}}$) of 200 μm, a $T_{\mathrm{R}}$ value of 20 ms, an excitation flip angle of 18°, and longitudinal relaxation times of blood and tissue of 2100 and 1950 ms, respectively (Huber, 2014, Table 2.1). Therein, two prominent regimes appear along the voxel size axis: (i) the blood-delivery-time dominated regime, where as long as the voxel is completely filled with blood, that is, ${\displaystyle l_{\mathrm{v}\mathrm{o}\mathrm{x}\mathrm{e}\mathrm{l}}<\cos \frac{\pi }{4}\cdot d_{\mathrm{v}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{l}}}$, the voxel size has no impact on the FRE, and the FRE is solely an exponential function of the blood delivery time (Equation (2), Figure 3); (ii) the voxel-size dominated regime, where if the vessel is smaller than the voxel, the FRE also depends quadratically on the voxel size (see Estimation of vessel-volume fraction). Note that our partial-volume model encompasses both regimes, and because the $\mathrm{F}\mathrm{R}{\mathrm{E}}_{2\mathrm{C}}$ definition reverts to the classical FRE definition for larger-than-voxel-sized vessels, we will from now on use FRE synonymously with $\mathrm{F}\mathrm{R}{\mathrm{E}}_{2\mathrm{C}}$. In theory, a reduction in blood delivery time increases the FRE in both regimes, and—if the vessel is smaller than the voxel—so would a reduction in voxel size. In practice, a reduction in slab thickness—which is the default strategy in classical TOF-MRA to reduce blood delivery time—might not provide substantial FRE increases for pial arteries. This is due to their convoluted geometry (see section Anatomical architecture of the pial arterial vasculature), where a reduction in slab thickness may not necessarily reduce the vessel segment length if the majority of the artery is still contained within the smaller slab. Thus, given the small arterial diameter, reducing the voxel size is a much more promising avenue when imaging the pial arterial vasculature.

Figure 4 with 2 supplements see all

Download asset Open asset

Inflow artefacts in sinuses and pial veins

Inflow in large pial veins and the sagittal and transverse sinuses can cause FRE in these non-arterial vessels. One common strategy to remove this unwanted signal enhancement is to apply venous suppression pulses during the data acquisition, which saturate bloods spins outside the imaging slab. Disadvantages of this technique are the technical challenges of applying these pulses at ultra-high field due to constraints of the specific absorption rate (SAR) and the necessary increase in acquisition time (Conolly et al., 1988; Heverhagen et al., 2008; Johst et al., 2012; Maderwald et al., 2008; Schmitter et al., 2012; Zhang et al., 2015). In addition, optimal positioning of the saturation slab in the case of pial arteries requires further investigation, and in particular supressing signal from the superior sagittal sinus without interfering in the imaging of the pial arteries vasculature at the top of the cortex might prove challenging. Furthermore, this venous saturation strategy is based on the assumption that arterial blood is traveling head-wards while venous blood is drained foot-wards. For the complex and convoluted trajectory of pial vessels, this directionality-based saturation might be oversimplified, particularly when considering the higher-order branches of the pial arteries and veins on the cortical surface.

Inspired by techniques to simultaneously acquire a TOF image for angiography and a susceptibility-weighted image (SWI) for venography (Bae et al., 2010; Deistung et al., 2009; Du et al., 1994; Du and Jin, 2008), we set out to explore the possibility of removing unwanted venous structures from the segmentation of the pial arterial vasculature during data post-processing. Because arteries filled with oxygenated blood have ${\displaystyle T_2{}^{\ast }}$ values similar to tissue, while veins have much shorter ${\displaystyle T_2{}^{\ast }}$ values due to the presence of deoxygenated blood (Pauling and Coryell, 1936; Peters et al., 2007; Uludağ et al., 2009; Zhao et al., 2007), we used this criterion to remove vessels with short ${\displaystyle T_2{}^{\ast }}$ values from the segmentation (see Data analysis for details). In addition, we also explored whether unwanted venous structures in the high-resolution TOF images—where a two-echo acquisition is not feasible due to the longer readout—can be removed based on detecting them in a lower-resolution image.

Velocity- and T_E-dependent vessel displacement artefacts

While isotropic 3D TOF-MRA provides detailed information on the vessel position, the continuous motion of blood during image acquisition can lead to vessel displacement artefacts in the phase-encoding directions (Brown et al., 2014b; Parker et al., 2003). The magnitude of this misregistration artefact depends on the time elapsed between the time of the phase-encoding blip ($t_{\mathrm{p}\mathrm{e}}$) and the echo time ($T_{\mathrm{E}}$) and on the blood velocity in the primary phase-encoding direction $y$, $v_y$:

(7) $\mathrm{\Delta }{y}_{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{p}\mathrm{l}}=-v_y\cdot \left(T_{\mathrm{E}}-t_{\mathrm{p}\mathrm{e}}\right).$

A similar relationship for $\mathrm{\Delta }{z}_{\mathrm{d}\mathrm{i}\mathrm{s}\mathrm{p}\mathrm{l}}$ exists for the secondary phase-encoding direction $z$. According to this relationship, and based on the reported velocity range in Figure 2, vessel displacements in the phase-encoding directions between 25 and 250 μm for a time delay $\left(T_{\mathrm{E}}-t_{\mathrm{p}\mathrm{e}}\right)$ of 5 ms can be expected. Flow compensation gradients can correct for the effect of constant velocities (Parker et al., 2003), but the required large gradient lobes for high-resolution applications would substantially increase the $T_{\mathrm{E}}$, and, consequently, reduce the SNR. For these reasons, no flow compensation in the phase-encoding-directions was used for the high-resolution images in this study. However, we do assess the extent of vessel displacement using a two-echo low-resolution acquisition.

Considerations for imaging the pial arterial vasculature

We have identified voxel size as the key parameter for imaging pial arteries. While the benefit of smaller voxels was conceptually known and empirically shown before (Haacke et al., 1990; Mattern et al., 2018; Von Morze et al., 2007), clear guidelines on how small is sufficiently small and how strongly the FRE increases with decreasing voxel size were not established. We can now conclude that the voxel size should be at least the size of the pial artery, that is, somewhere between 50 and 280 μm in our case, and ideally around 70% of that, that is, 35–200 μm. While this is challenging in practice, the strong increase in FRE especially when voxel sizes are similar to vessel size makes a reduction of even only 10 or 20 μm worthwhile for imaging pial arteries. Further, we have seen that slow blood velocities are an unlikely explanation for loss of FRE in small vessels, because (i) blood delivery times to pial arteries are sufficiently short due to the perhaps surprisingly fast blood flow in these mesoscopic vessels, (ii) the effect of blood delivery time on FRE decreases with increasing blood delivery times, and (iii) the addition of partial-volume effects to the FRE estimation predicts the often observed drastic loss of contrast in small vessels. Thus, we expect a reduction in voxel size to directly translate into an increased sensitivity for small pial arteries.

The anonymized imaging data presented in this manuscript are stored in OSF (OSF, Center for Open Science, Inc, Charlottesville, Virginia, USA) accessible via https://doi.org/10.17605/OSF.IO/NR6GC.

1. 1. Bollmann S
   2. Mattern H
   3. Bernier M
   4. Robinson SD
   5. Park D
   6. Speck O
   7. Polimeni JR

   (2022) Open Science Framework

   Imaging of the Pial Arterial Vasculature.

   https://doi.org/10.17605/OSF.IO/NR6GC

1. 1. Al-Kwifi O
   2. Emery DJ
   3. Wilman AH

   (2002) Vessel contrast at three Tesla in time-of-flight magnetic resonance angiography of the intracranial and carotid arteries

   Magnetic Resonance Imaging 20:181–187.

   https://doi.org/10.1016/s0730-725x(02)00486-1

   • PubMed
   • Google Scholar
2. 1. Alpers BJ
   2. Berry RG
   3. Paddison RM

   (1959) Anatomical studies of the circle of Willis in normal brain

   A.M.A. Archives of Neurology and Psychiatry 81:409–418.

   https://doi.org/10.1001/archneurpsyc.1959.02340160007002

   • PubMed
   • Google Scholar
3. 1. Alsop DC
   2. Detre JA
   3. Golay X
   4. Günther M
   5. Hendrikse J
   6. Hernandez-Garcia L
   7. Lu H
   8. MacIntosh BJ
   9. Parkes LM
   10. Smits M
   11. van Osch MJP
   12. Wang DJJ
   13. Wong EC
   14. Zaharchuk G

   (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia

   Magnetic Resonance in Medicine 73:102–116.

   https://doi.org/10.1002/mrm.25197

   • PubMed
   • Google Scholar
4. 1. Amemiya S
   2. Takao H
   3. Abe O

   (2020) Origin of the Time Lag Phenomenon and the Global Signal in Resting-State fMRI

   Frontiers in Neuroscience 14:596084.

   https://doi.org/10.3389/fnins.2020.596084

   • PubMed
   • Google Scholar
5. 1. Andersson JLR
   2. Skare S
   3. Ashburner J

   (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

   NeuroImage 20:870–888.

   https://doi.org/10.1016/S1053-8119(03)00336-7

   • PubMed
   • Google Scholar
6. 1. Arts T
   2. Meijs TA
   3. Grotenhuis H
   4. Voskuil M
   5. Siero J
   6. Biessels GJ
   7. Zwanenburg J

   (2021) Velocity and Pulsatility Measures in the Perforating Arteries of the Basal Ganglia at 3T MRI in Reference to 7T MRI

   Frontiers in Neuroscience 15:665480.

   https://doi.org/10.3389/fnins.2021.665480

   • Google Scholar
7. 1. Atkinson D
   2. Brant-Zawadzki M
   3. Gillan G
   4. Purdy D
   5. Laub G

   (1994) Improved MR angiography: magnetization transfer suppression with variable flip angle excitation and increased resolution

   Radiology 190:890–894.

   https://doi.org/10.1148/radiology.190.3.8115646

   • Google Scholar
8. 1. Avants B
   2. Tustison NJ
   3. Song G

   (2009) Advanced Normalization Tools: V1.0

   The Insight Journal 2:1–35.

   https://doi.org/10.54294/uvnhin

   • Google Scholar
9. 1. Bae KT
   2. Park SH
   3. Moon CH
   4. Kim JH
   5. Kaya D
   6. Zhao T

   (2010) Dual-echo arteriovenography imaging with 7T MRI: CODEA with 7T

   Journal of Magnetic Resonance Imaging: JMRI 31:255–261.

   https://doi.org/10.1002/jmri.22019

   • Google Scholar
10. 1. Baran U
    2. Wang RK

    (2016) Review of optical coherence tomography based angiography in neuroscience

    Neurophotonics 3:010902.

    https://doi.org/10.1117/1.NPh.3.1.010902

    • Google Scholar
11. 1. Bartholdi E
    2. Ernst RR

    (1973) Fourier spectroscopy and the causality principle

    Journal of Magnetic Resonance 11:9–19.

    https://doi.org/10.1016/0022-2364(73)90076-0

    • Google Scholar
12. 1. Baumbach GL
    2. Heistad DD

    (1985) Regional, segmental, and temporal heterogeneity of cerebral vascular autoregulation

    Annals of Biomedical Engineering 13:303–310.

    https://doi.org/10.1007/BF02584248

    • PubMed
    • Google Scholar
13. 1. Beevor CE
    2. Ferrier D

    (1997) I. On the distribution of the different arteries supplying the human brain

    Philosophical Transactions of the Royal Society of London. Series B, Containing Papers of a Biological Character 200:1–55.

    https://doi.org/10.1098/rstb.1909.0001

    • Google Scholar
14. 1. Bernier M
    2. Cunnane SC
    3. Whittingstall K

    (2018) The morphology of the human cerebrovascular system

    Human Brain Mapping 39:4962–4975.

    https://doi.org/10.1002/hbm.24337

    • PubMed
    • Google Scholar
15. 1. Bizeau A
    2. Gilbert G
    3. Bernier M
    4. Huynh MT
    5. Bocti C
    6. Descoteaux M
    7. Whittingstall K

    (2018) Stimulus-evoked changes in cerebral vessel diameter: A study in healthy humans

    Journal of Cerebral Blood Flow and Metabolism 38:528–539.

    https://doi.org/10.1177/0271678X17701948

    • PubMed
    • Google Scholar
16. 1. Blinder P
    2. Shih AY
    3. Rafie C
    4. Kleinfeld D

    (2010) Topological basis for the robust distribution of blood to rodent neocortex

    PNAS 107:12670–12675.

    https://doi.org/10.1073/pnas.1007239107

    • Google Scholar
17. Software

    1. Bollmann S

    (2022) PialVesselTOF, version 17599816

    GitLab.

    https://gitlab.com/SaskiaB/pialvesseltof.git
18. 1. Bouvy WH
    2. Biessels GJ
    3. Kuijf HJ
    4. Kappelle LJ
    5. Luijten PR
    6. Zwanenburg JJM

    (2014) Visualization of Perivascular Spaces and Perforating Arteries With 7 T Magnetic Resonance Imaging: Invest

    Radiol 49:307–313.

    https://doi.org/10.1097/RLI.0000000000000027

    • Google Scholar
19. 1. Bouvy WH
    2. Geurts LJ
    3. Kuijf HJ
    4. Luijten PR
    5. Kappelle LJ
    6. Biessels GJ
    7. Zwanenburg JJM

    (2016) Assessment of blood flow velocity and pulsatility in cerebral perforating arteries with 7-T quantitative flow MRI: Blood Flow Velocity And Pulsatility In Cerebral Perforating Arteries

    NMR in Biomedicine 29:1295–1304.

    https://doi.org/10.1002/nbm.3306

    • Google Scholar
20. 1. Bright MG
    2. Whittaker JR
    3. Driver ID
    4. Murphy K

    (2020) Vascular physiology drives functional brain networks

    NeuroImage 217:116907.

    https://doi.org/10.1016/j.neuroimage.2020.116907

    • Google Scholar
21. Book

    1. Brown RW
    2. Cheng YCN
    3. Haacke EM
    4. Thompson MR
    5. Venkatesan R

    (2014a) Chapter 24 - MR Angiography and Flow Quantification

    In: Brown RW, editors. Magnetic Resonance Imaging. John Wiley & Sons, Ltd. pp. 701–737.

    https://doi.org/10.1002/9781118633953.ch24

    • Google Scholar
22. Book

    1. Brown RW
    2. Cheng YCN
    3. Haacke EM
    4. Thompson MR
    5. Venkatesan R

    (2014b) Chapter 23 - Motion Artifacts and Flow Compensation

    In: Brown RW, editors. Magnetic Resonance Imaging. Hoboken, NJ, USA: John Wiley & Sons, Inc. pp. 669–700.

    https://doi.org/10.1002/9781118633953.ch23

    • Google Scholar
23. Book

    1. Brown RW
    2. Cheng YCN
    3. Haacke EM
    4. Thompson MR
    5. Venkatesan R

    (2014c) Magnetic Resonance Imaging

    John Wiley & Sons, Ltd.

    https://doi.org/10.1002/9781118633953.ch15

    • Google Scholar
24. 1. Bui AV
    2. Manasseh R
    3. Liffman K
    4. Sutalo ID

    (2010) Development of optimized vascular fractal tree models using level set distance function

    Medical Engineering & Physics 32:790–794.

    https://doi.org/10.1016/j.medengphy.2010.04.014

    • PubMed
    • Google Scholar
25. 1. Callaghan MF
    2. Josephs O
    3. Herbst M
    4. Zaitsev M
    5. Todd N
    6. Weiskopf N

    (2015) An evaluation of prospective motion correction (PMC) for high resolution quantitative MRI

    Frontiers in Neuroscience 9:97.

    https://doi.org/10.3389/fnins.2015.00097

    • PubMed
    • Google Scholar
26. 1. Candes EJ
    2. Romberg J
    3. Tao T

    (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information

    IEEE Transactions on Information Theory 52:489–509.

    https://doi.org/10.1109/TIT.2005.862083

    • Google Scholar
27. Book

    1. Carr JC
    2. Carroll TJ

    (2012) Magnetic Resonance Angiography: Principles and Applications

    Springer.

    https://doi.org/10.1007/978-1-4419-1686-0

    • Google Scholar
28. 1. Cassot F
    2. Lauwers F
    3. Fouard C
    4. Prohaska S
    5. Lauwers-Cances V

    (2006) A novel three-dimensional computer-assisted method for A quantitative study of microvascular networks of the human cerebral cortex

    Microcirculation (New York, N.Y 13:1–18.

    https://doi.org/10.1080/10739680500383407

    • PubMed
    • Google Scholar
29. 1. Chen L
    2. Mossa-Basha M
    3. Balu N
    4. Canton G
    5. Sun J
    6. Pimentel K
    7. Hatsukami TS
    8. Hwang JN
    9. Yuan C

    (2018) Development of a quantitative intracranial vascular features extraction tool on 3D MRA using semiautomated open-curve active contour vessel tracing

    Magnetic Resonance in Medicine 79:3229–3238.

    https://doi.org/10.1002/mrm.26961

    • PubMed
    • Google Scholar
30. 1. Chen JE
    2. Lewis LD
    3. Chang C
    4. Tian Q
    5. Fultz NE
    6. Ohringer NA
    7. Rosen BR
    8. Polimeni JR

    (2020) Resting-state “physiological networks.”

    NeuroImage 213:116707.

    https://doi.org/10.1016/j.neuroimage.2020.116707

    • PubMed
    • Google Scholar
31. 1. Cho Z-H
    2. Kang C-K
    3. Han J-Y
    4. Kim S-H
    5. Park C-A
    6. Kim K-N
    7. Hong S-M
    8. Park C-W
    9. Kim Y-B

    (2008) Functional MR angiography with 7.0 T

    NeuroImage 42:70–75.

    https://doi.org/10.1016/j.neuroimage.2008.05.003

    • PubMed
    • Google Scholar
32. 1. Cho ZH
    2. Kang CK
    3. Park CA
    4. Hong SM
    5. Kim SH
    6. Oh ST
    7. Kim YB

    (2012) Microvascular functional MR angiography with ultra-high-field 7 t MRI: Comparison with BOLD fMRI

    International Journal of Imaging Systems and Technology 22:18–22.

    https://doi.org/10.1002/ima.22008

    • Google Scholar
33. 1. Choi US
    2. Kawaguchi H
    3. Kida I

    (2020) Cerebral artery segmentation based on magnetization-prepared two rapid acquisition gradient echo multi-contrast images in 7 Tesla magnetic resonance imaging

    NeuroImage 222:117259.

    https://doi.org/10.1016/j.neuroimage.2020.117259

    • Google Scholar
34. 1. Cilliers K
    2. Page BJ

    (2017) Description of the anterior cerebral artery and its cortical branches: Variation in presence, origin, and size

    Clinical Neurology and Neurosurgery 152:78–83.

    https://doi.org/10.1016/j.clineuro.2016.11.024

    • Google Scholar
35. Book

    1. Cipolla M.J.

    (2009)

    Chapter 2: Anatomy and Ultrastructure, in: The Cerebral Circulation

    Morgan & Claypool Life Sciences.

    • Google Scholar
36. 1. Conolly S
    2. Nishimura D
    3. Macovski A
    4. Glover G

    (1988) Variable-rate selective excitation

    Journal of Magnetic Resonance 78:440–458.

    https://doi.org/10.1016/0022-2364(88)90131-X

    • Google Scholar
37. 1. Constantinides CD
    2. Atalar E
    3. McVeigh ER

    (1997) Signal-to-noise measurements in magnitude images from NMR phased arrays

    Magnetic Resonance in Medicine 38:852–857.

    https://doi.org/10.1002/mrm.1910380524

    • PubMed
    • Google Scholar
38. 1. Deistung A
    2. Dittrich E
    3. Sedlacik J
    4. Rauscher A
    5. Reichenbach JR

    (2009) ToF-SWI: simultaneous time of flight and fully flow compensated susceptibility weighted imaging

    Journal of Magnetic Resonance Imaging 29:1478–1484.

    https://doi.org/10.1002/jmri.21673

    • PubMed
    • Google Scholar
39. 1. Detre JA
    2. Leigh JS
    3. Williams DS
    4. Koretsky AP

    (1992) Perfusion imaging

    Magnetic Resonance in Medicine 23:37–45.

    https://doi.org/10.1002/mrm.1910230106

    • PubMed
    • Google Scholar
40. 1. Djulejić V
    2. Marinković S
    3. Milić V
    4. Georgievski B
    5. Rašić M
    6. Aksić M
    7. Puškaš L

    (2015) Common features of the cerebral perforating arteries and their clinical significance

    Acta Neurochirurgica 157:743–754.

    https://doi.org/10.1007/s00701-015-2378-8

    • PubMed
    • Google Scholar
41. 1. Drew PJ
    2. Mateo C
    3. Turner KL
    4. Yu X
    5. Kleinfeld D

    (2020) Ultra-slow Oscillations in fMRI and Resting-State Connectivity: Neuronal and Vascular Contributions and Technical Confounds

    Neuron 107:782–804.

    https://doi.org/10.1016/j.neuron.2020.07.020

    • PubMed
    • Google Scholar
42. 1. Du Y
    2. Parker DL
    3. Davis WL
    4. Blatter DD

    (1993) Contrast-to-noise-ratio measurements in three-dimensional magnetic resonance angiography

    Investigative Radiology 28:1004–1009.

    https://doi.org/10.1097/00004424-199311000-00005

    • PubMed
    • Google Scholar
43. 1. Du YP
    2. Parker DL
    3. Davis WL
    4. Cao G

    (1994) Reduction of partial-volume artifacts with zero-filled interpolation in three-dimensional MR angiography

    Journal of Magnetic Resonance Imaging 4:733–741.

    https://doi.org/10.1002/jmri.1880040517

    • PubMed
    • Google Scholar
44. 1. Du YP
    2. Parker DL
    3. Davis WL
    4. Cao G
    5. Buswell HR
    6. Goodrich KC

    (1996) Experimental and theoretical studies of vessel contrast-to-noise ratio in intracranial time-of-flight MR angiography

    Journal of Magnetic Resonance Imaging 6:99–108.

    https://doi.org/10.1002/jmri.1880060120

    • PubMed
    • Google Scholar
45. 1. Du YP
    2. Jin Z

    (2008) Simultaneous acquisition of MR angiography and venography (MRAV

    Magnetic Resonance in Medicine 59:954–958.

    https://doi.org/10.1002/mrm.21581

    • PubMed
    • Google Scholar
46. 1. Duvernoy HM
    2. Delon S
    3. Vannson JL

    (1981) Cortical blood vessels of the human brain

    Brain Research Bulletin 7:519–579.

    https://doi.org/10.1016/0361-9230(81)90007-1

    • PubMed
    • Google Scholar
47. 1. Duvernoy H
    2. Delon S
    3. Vannson JL

    (1983) The vascularization of the human cerebellar cortex

    Brain Research Bulletin 11:419–480.

    https://doi.org/10.1016/0361-9230(83)90116-8

    • PubMed
    • Google Scholar
48. Book

    1. Duvernoy HM

    (1999) The Human Brain: Surface, Blood Supply, and Three-Dimensional Sectional Anatomy

    Springer.

    https://doi.org/10.1007/978-3-7091-6792-2

    • Google Scholar
49. 1. Eckstein K
    2. Bachrata B
    3. Hangel G
    4. Widhalm G
    5. Enzinger C
    6. Barth M
    7. Trattnig S
    8. Robinson SD

    (2021) Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI

    NeuroImage 237:118175.

    https://doi.org/10.1016/j.neuroimage.2021.118175

    • PubMed
    • Google Scholar
50. 1. Edelstein WA
    2. Glover GH
    3. Hardy CJ
    4. Redington RW

    (1986) The intrinsic signal-to-noise ratio in NMR imaging

    Magnetic Resonance in Medicine 3:604–618.

    https://doi.org/10.1002/mrm.1910030413

    • PubMed
    • Google Scholar
51. 1. Ernst RR
    2. Anderson WA

    (1966) Application of Fourier Transform Spectroscopy to Magnetic Resonance

    Review of Scientific Instruments 37:93–102.

    https://doi.org/10.1063/1.1719961

    • Google Scholar
52. 1. Fan AP
    2. Govindarajan ST
    3. Kinkel RP
    4. Madigan NK
    5. Nielsen AS
    6. Benner T
    7. Tinelli E
    8. Rosen BR
    9. Adalsteinsson E
    10. Mainero C

    (2015) Quantitative oxygen extraction fraction from 7-Tesla MRI phase: reproducibility and application in multiple sclerosis

    Journal of Cerebral Blood Flow and Metabolism 35:131–139.

    https://doi.org/10.1038/jcbfm.2014.187

    • PubMed
    • Google Scholar
53. 1. Fiedler TM
    2. Ladd ME
    3. Bitz AK

    (2018) SAR Simulations & Safety

    NeuroImage 168:33–58.

    https://doi.org/10.1016/j.neuroimage.2017.03.035

    • PubMed
    • Google Scholar
54. Book

    1. Frangi AF
    2. Niessen WJ
    3. Vincken KL
    4. Viergever MA

    (1998) Multiscale vessel enhancement filtering

    In: Wells WM, Colchester A, Delp S, editors. Medical Image Computing and Computer-Assisted Intervention — MICCAI’98, Lecture Notes in Computer Science. Springer. pp. 130–137.

    https://doi.org/10.1007/BFb0056195

    • Google Scholar
55. 1. Frässle S
    2. Aponte EA
    3. Bollmann S
    4. Brodersen KH
    5. Do CT
    6. Harrison OK
    7. Harrison SJ
    8. Heinzle J
    9. Iglesias S
    10. Kasper L
    11. Lomakina EI
    12. Mathys C
    13. Müller-Schrader M
    14. Pereira I
    15. Petzschner FH
    16. Raman S
    17. Schöbi D
    18. Toussaint B
    19. Weber LA
    20. Yao Y
    21. Stephan KE

    (2021) TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

    Frontiers in Psychiatry 12:680811.

    https://doi.org/10.3389/fpsyt.2021.680811

    • PubMed
    • Google Scholar
56. 1. Ginat DT
    2. Smith ER
    3. Robertson RL
    4. Scott RM
    5. Schaefer PW

    (2013) Imaging after direct and indirect extracranial-intracranial bypass surgery

    AJR. American Journal of Roentgenology 201:W124–W132.

    https://doi.org/10.2214/AJR.12.9517

    • PubMed
    • Google Scholar
57. 1. Ginsberg MD

    (2018) The cerebral collateral circulation: Relevance to pathophysiology and treatment of stroke

    Neuropharmacology 134:280–292.

    https://doi.org/10.1016/j.neuropharm.2017.08.003

    • PubMed
    • Google Scholar
58. 1. Gomes FB
    2. Dujovny M
    3. Umansky F
    4. Berman SK
    5. Diaz FG
    6. Ausman JI
    7. Mirchandani HG
    8. Ray WJ

    (1986) Microanatomy of the anterior cerebral artery

    Surgical Neurology 26:129–141.

    https://doi.org/10.1016/0090-3019(86)90365-4

    • PubMed
    • Google Scholar
59. 1. Griswold MA
    2. Jakob PM
    3. Heidemann RM
    4. Nittka M
    5. Jellus V
    6. Wang J
    7. Kiefer B
    8. Haase A

    (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA

    Magnetic Resonance in Medicine 47:1202–1210.

    https://doi.org/10.1002/mrm.10171

    • PubMed
    • Google Scholar
60. Preprint

    1. Gulban OF
    2. Bollmann S
    3. Huber R
    4. Wagstyl K
    5. Goebel R
    6. Poser BA
    7. Kay K
    8. Ivanov D

    (2021) Mesoscopic Quantification of Cortical Architecture in the Living Human Brain

    bioRxiv.

    https://doi.org/10.1101/2021.11.25.470023

    • Google Scholar
61. 1. Haacke EM
    2. Masaryk TJ
    3. Wielopolski PA
    4. Zypman FR
    5. Tkach JA
    6. Amartur S
    7. Mitchell J
    8. Clampitt M
    9. Paschal C

    (1990) Optimizing blood vessel contrast in fast three-dimensional MRI

    Magnetic Resonance in Medicine 14:202–221.

    https://doi.org/10.1002/mrm.1910140207

    • PubMed
    • Google Scholar
62. 1. Havlicek M
    2. Uludağ K

    (2020) A dynamical model of the laminar BOLD response

    NeuroImage 204:116209.

    https://doi.org/10.1016/j.neuroimage.2019.116209

    • PubMed
    • Google Scholar
63. 1. Helthuis JHG
    2. Doormaal TPC
    3. Hillen B
    4. Bleys RL
    5. Harteveld AA
    6. Hendrikse J
    7. Toorn A
    8. Brozici M
    9. Zwanenburg JJM
    10. Zwan A

    (2019) Branching Pattern of the Cerebral Arterial Tree

    The Anatomical Record 302:1434–1446.

    https://doi.org/10.1002/ar.23994

    • Google Scholar
64. 1. Herz DA
    2. Baez S
    3. Shulman K

    (1975) Pial microcirculation in subarachnoid hemorrhage

    Stroke 6:417–424.

    https://doi.org/10.1161/01.str.6.4.417

    • PubMed
    • Google Scholar
65. 1. Hetts SW
    2. Yen A
    3. Cooke DL
    4. Nelson J
    5. Jolivalt P
    6. Banaga J
    7. Amans MR
    8. Dowd CF
    9. Higashida RT
    10. Lawton MT
    11. Kim H
    12. Halbach VV

    (2017) Pial Artery Supply as an Anatomic Risk Factor for Ischemic Stroke in the Treatment of Intracranial Dural Arteriovenous Fistulas

    AJNR. American Journal of Neuroradiology 38:2315–2320.

    https://doi.org/10.3174/ajnr.A5396

    • PubMed
    • Google Scholar
66. 1. Heverhagen JT
    2. Bourekas E
    3. Sammet S
    4. Knopp MV
    5. Schmalbrock P

    (2008) Time-of-flight magnetic resonance angiography at 7 Tesla

    Investigative Radiology 43:568–573.

    https://doi.org/10.1097/RLI.0b013e31817e9b2c

    • PubMed
    • Google Scholar
67. 1. Hilbert A
    2. Madai VI
    3. Akay EM
    4. Aydin OU
    5. Behland J
    6. Sobesky J
    7. Galinovic I
    8. Khalil AA
    9. Taha AA
    10. Wuerfel J
    11. Dusek P
    12. Niendorf T
    13. Fiebach JB
    14. Frey D
    15. Livne M

    (2020) BRAVE-NET: Fully Automated Arterial Brain Vessel Segmentation in Patients With Cerebrovascular Disease

    Front. Artif. Intell 3:552258.

    https://doi.org/10.3389/frai.2020.552258

    • Google Scholar
68. 1. Hillman EMC
    2. Devor A
    3. Bouchard MB
    4. Dunn AK
    5. Krauss GW
    6. Skoch J
    7. Bacskai BJ
    8. Dale AM
    9. Boas DA

    (2007) Depth-resolved optical imaging and microscopy of vascular compartment dynamics during somatosensory stimulation

    NeuroImage 35:89–104.

    https://doi.org/10.1016/j.neuroimage.2006.11.032

    • Google Scholar
69. 1. Hirsch S
    2. Reichold J
    3. Schneider M
    4. Székely G
    5. Weber B

    (2012) Topology and Hemodynamics of the Cortical Cerebrovascular System

    Journal of Cerebral Blood Flow and Metabolism 32:952–967.

    https://doi.org/10.1038/jcbfm.2012.39

    • Google Scholar
70. 1. Hofman MB
    2. Visser FC
    3. van Rossum AC
    4. Vink QM
    5. Sprenger M
    6. Westerhof N

    (1995) In vivo validation of magnetic resonance blood volume flow measurements with limited spatial resolution in small vessels

    Magnetic Resonance in Medicine 33:778–784.

    https://doi.org/10.1002/mrm.1910330606

    • PubMed
    • Google Scholar
71. 1. Hoogeveen RM
    2. Bakker CJG
    3. Viergever MA

    (1998) Limits to the accuracy of vessel diameter measurement in MR angiography

    Journal of Magnetic Resonance Imaging 8:1228–1235.

    https://doi.org/10.1002/jmri.1880080608

    • PubMed
    • Google Scholar
72. 1. Hsu CY
    2. Ghaffari M
    3. Alaraj A
    4. Flannery M
    5. Zhou XJ
    6. Linninger A

    (2017) Gap-free segmentation of vascular networks with automatic image processing pipeline

    Computers in Biology and Medicine 82:29–39.

    https://doi.org/10.1016/j.compbiomed.2017.01.012

    • PubMed
    • Google Scholar
73. 1. Hsu C-Y
    2. Li Y
    3. Han Y
    4. Elijovich L
    5. Sabin ND
    6. Abuelem T
    7. Torabi R
    8. Faught A
    9. Hua C-H
    10. Klimo P
    11. Merchant TE
    12. Lucas JT

    (2019) Automatic image processing pipeline for tracking longitudinal vessel changes in magnetic resonance angiography

    Journal of Magnetic Resonance Imaging 50:1063–1074.

    https://doi.org/10.1002/jmri.26699

    • PubMed
    • Google Scholar
74. 1. Hua J
    2. Liu P
    3. Kim T
    4. Donahue M
    5. Rane S
    6. Chen JJ
    7. Qin Q
    8. Kim SG

    (2019) MRI techniques to measure arterial and venous cerebral blood

    NeuroImage 187:17–31.

    https://doi.org/10.1016/j.neuroimage.2018.02.027

    • Google Scholar
75. Book

    1. Huber L.

    (2014)

    Mapping Human Brain Activity by Functional Magnetic Resonance Imaging of Blood Volume

    Fakultät für Physik und Geowissenschaften der Universität Leipzig.

    • Google Scholar
76. 1. Huber L
    2. Ivanov D
    3. Krieger SN
    4. Streicher MN
    5. Mildner T
    6. Poser BA
    7. Möller HE
    8. Turner R

    (2014) Slab-selective, BOLD-corrected VASO at 7 Tesla provides measures of cerebral blood volume reactivity with high signal-to-noise ratio

    Magnetic Resonance in Medicine 72:137–148.

    https://doi.org/10.1002/mrm.24916

    • PubMed
    • Google Scholar
77. 1. Huck J
    2. Wanner Y
    3. Fan AP
    4. Jäger AT
    5. Grahl S
    6. Schneider U
    7. Villringer A
    8. Steele CJ
    9. Tardif CL
    10. Bazin PL
    11. Gauthier CJ

    (2019) High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps

    Brain Structure & Function 224:2467–2485.

    https://doi.org/10.1007/s00429-019-01919-4

    • PubMed
    • Google Scholar
78. 1. Ii S
    2. Kitade H
    3. Ishida S
    4. Imai Y
    5. Watanabe Y
    6. Wada S

    (2020) Multiscale modeling of human cerebrovasculature: A hybrid approach using image-based geometry and A mathematical algorithm

    PLOS Computational Biology 16:e1007943.

    https://doi.org/10.1371/journal.pcbi.1007943

    • PubMed
    • Google Scholar
79. 1. Iliff JJ
    2. Wang M
    3. Zeppenfeld DM
    4. Venkataraman A
    5. Plog BA
    6. Liao Y
    7. Deane R
    8. Nedergaard M

    (2013) Cerebral arterial pulsation drives paravascular CSF-interstitial fluid exchange in the murine brain

    The Journal of Neuroscience 33:18190–18199.

    https://doi.org/10.1523/JNEUROSCI.1592-13.2013

    • PubMed
    • Google Scholar
80. 1. Jezzard P
    2. Clare S

    (1999) Sources of distortion in functional MRI data

    Human Brain Mapping 8:80–85.

    https://doi.org/10.1002/(sici)1097-0193(1999)8:2/3<80::aid-hbm2>3.0.co;2-c

    • PubMed
    • Google Scholar
81. 1. Johst S
    2. Wrede KH
    3. Ladd ME
    4. Maderwald S

    (2012) Time-of-flight magnetic resonance angiography at 7 T using venous saturation pulses with reduced flip angles

    Investigative Radiology 47:445–450.

    https://doi.org/10.1097/RLI.0b013e31824ef21f

    • PubMed
    • Google Scholar
82. 1. Jones EG

    (1970)

    On the mode of entry of blood vessels into the cerebral cortex

    Journal of Anatomy 106:507–520.

    • PubMed
    • Google Scholar
83. 1. Kang C-K
    2. Park C-A
    3. Kim K-N
    4. Hong S-M
    5. Park C-W
    6. Kim Y-B
    7. Cho Z-H

    (2010) Non-invasive visualization of basilar artery perforators with 7T MR angiography

    Journal of Magnetic Resonance Imaging 32:544–550.

    https://doi.org/10.1002/jmri.22250

    • PubMed
    • Google Scholar
84. 1. Kasper L
    2. Engel M
    3. Barmet C
    4. Haeberlin M
    5. Wilm BJ
    6. Dietrich BE
    7. Schmid T
    8. Gross S
    9. Brunner DO
    10. Stephan KE
    11. Pruessmann KP

    (2018) Rapid anatomical brain imaging using spiral acquisition and an expanded signal model

    NeuroImage 168:88–100.

    https://doi.org/10.1016/j.neuroimage.2017.07.062

    • PubMed
    • Google Scholar
85. 1. Keelan J
    2. Chung EML
    3. Hague JP

    (2019) Development of a globally optimised model of the cerebral arteries

    Physics in Medicine and Biology 64:125021.

    https://doi.org/10.1088/1361-6560/ab2479

    • PubMed
    • Google Scholar
86. 1. Kiselev VG
    2. Strecker R
    3. Ziyeh S
    4. Speck O
    5. Hennig J

    (2005) Vessel size imaging in humans

    Magnetic Resonance in Medicine 53:553–563.

    https://doi.org/10.1002/mrm.20383

    • PubMed
    • Google Scholar
87. 1. Klepaczko A
    2. Szczypiński P
    3. Deistung A
    4. Reichenbach JR
    5. Materka A

    (2016) Simulation of MR angiography imaging for validation of cerebral arteries segmentation algorithms

    Computer Methods and Programs in Biomedicine 137:293–309.

    https://doi.org/10.1016/j.cmpb.2016.09.020

    • PubMed
    • Google Scholar
88. 1. Kobari M
    2. Gotoh F
    3. Fukuuchi Y
    4. Tanaka K
    5. Suzuki N
    6. Uematsu D

    (1984) Blood flow velocity in the pial arteries of cats, with particular reference to the vessel diameter

    Journal of Cerebral Blood Flow and Metabolism 4:110–114.

    https://doi.org/10.1038/jcbfm.1984.15

    • PubMed
    • Google Scholar
89. 1. Korin HW
    2. Riederer SJ
    3. Bampton AEH
    4. Ehman RL

    (1992) Altered phase-encoding order for reduced sensitivity to motion in three-dimensional MR imaging

    Journal of Magnetic Resonance Imaging 2:687–693.

    https://doi.org/10.1002/jmri.1880020613

    • PubMed
    • Google Scholar
90. 1. Ladd ME

    (2007) High-field-strength magnetic resonance: potential and limits

    Topics in Magnetic Resonance Imaging 18:139–152.

    https://doi.org/10.1097/RMR.0b013e3180f612b3

    • PubMed
    • Google Scholar
91. 1. LeCun Y
    2. Bengio Y
    3. Hinton G

    (2015) Deep learning

    Nature 521:436–444.

    https://doi.org/10.1038/nature14539

    • PubMed
    • Google Scholar
92. 1. Lee HW
    2. Caldwell JE
    3. Dodson B
    4. Talke P
    5. Howley J

    (1997) The effect of clonidine on cerebral blood flow velocity, carbon dioxide cerebral vasoreactivity, and response to increased arterial pressure in human volunteers

    Anesthesiology 87:553–558.

    https://doi.org/10.1097/00000542-199709000-00015

    • PubMed
    • Google Scholar
93. 1. Lesage D
    2. Angelini ED
    3. Bloch I
    4. Funka-Lea G

    (2009) A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes

    Medical Image Analysis 13:819–845.

    https://doi.org/10.1016/j.media.2009.07.011

    • PubMed
    • Google Scholar
94. 1. Lüsebrink F
    2. Sciarra A
    3. Mattern H
    4. Yakupov R
    5. Speck O

    (2017) T₁-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 μm

    Scientific Data 4:170032.

    https://doi.org/10.1038/sdata.2017.32

    • PubMed
    • Google Scholar
95. 1. Lüsebrink F
    2. Mattern H
    3. Yakupov R
    4. Acosta-Cabronero J
    5. Ashtarayeh M
    6. Oeltze-Jafra S
    7. Speck O

    (2021) Comprehensive ultrahigh resolution whole brain in vivo MRI dataset as a human phantom

    Scientific Data 8:138.

    https://doi.org/10.1038/s41597-021-00923-w

    • PubMed
    • Google Scholar
96. 1. Maderwald S
    2. Ladd SC
    3. Gizewski ER
    4. Kraff O
    5. Theysohn JM
    6. Wicklow K
    7. Moenninghoff C
    8. Wanke I
    9. Ladd ME
    10. Quick HH

    (2008) To TOF or not to TOF: strategies for non-contrast-enhanced intracranial MRA at 7 T

    Magma (New York, N.Y.) 21:159–167.

    https://doi.org/10.1007/s10334-007-0096-9

    • PubMed
    • Google Scholar
97. 1. Manjón JV

    (2010) Adaptive non-local means denoising of MR images with spatially varying noise levels

    Journal of Magnetic Resonance Imaging: JMRI 31:192–203.

    https://doi.org/10.1002/jmri.22003

    • Google Scholar
98. 1. Mansfield P
    2. Harvey PR

    (1993) Limits to neural stimulation in echo-planar imaging

    Magnetic Resonance in Medicine 29:746–758.

    https://doi.org/10.1002/mrm.1910290606

    • PubMed
    • Google Scholar
99. 1. Marin-Padilla M

    (2012) The human brain intracerebral microvascular system: development and structure

    Frontiers in Neuroanatomy 6:38.

    https://doi.org/10.3389/fnana.2012.00038

    • Google Scholar
100. 1. Markuerkiaga I
     2. Barth M
     3. Norris DG

     (2016) A cortical vascular model for examining the specificity of the laminar BOLD signal

     NeuroImage 132:491–498.

     https://doi.org/10.1016/j.neuroimage.2016.02.073

     • PubMed
     • Google Scholar
101. 1. Masaryk TJ
     2. Modic MT
     3. Ross JS
     4. Ruggieri PM
     5. Laub GA
     6. Lenz GW
     7. Haacke EM
     8. Selman WR
     9. Wiznitzer M
     10. Harik SI

     (1989) Intracranial circulation: preliminary clinical results with three-dimensional (volume) MR angiography

     Radiology 171:793–799.

     https://doi.org/10.1148/radiology.171.3.2717754

     • PubMed
     • Google Scholar
102. 1. Mastorakos P
     2. McGavern D

     (2019) The anatomy and immunology of vasculature in the central nervous system

     Science Immunology 4:eaav0492.

     https://doi.org/10.1126/sciimmunol.aav0492

     • PubMed
     • Google Scholar
103. 1. Mattern H
     2. Sciarra A
     3. Godenschweger F
     4. Stucht D
     5. Lüsebrink F
     6. Rose G
     7. Speck O

     (2018) Prospective motion correction enables highest resolution time-of-flight angiography at 7T: Prospectively Motion-Corrected TOF Angiography at 7T

     Magnetic Resonance in Medicine 80:248–258.

     https://doi.org/10.1002/mrm.27033

     • Google Scholar
104. 1. Mattern H
     2. Sciarra A
     3. Lüsebrink F
     4. Acosta-Cabronero J
     5. Speck O

     (2019) Prospective motion correction improves high-resolution quantitative susceptibility mapping at 7T

     Magnetic Resonance in Medicine 81:1605–1619.

     https://doi.org/10.1002/mrm.27509

     • PubMed
     • Google Scholar
105. 1. McConnell ED
     2. Wei HS
     3. Reitz KM
     4. Kang H
     5. Takano T
     6. Vates GE
     7. Nedergaard M

     (2016) Cerebral microcirculatory failure after subarachnoid hemorrhage is reversed by hyaluronidase

     Journal of Cerebral Blood Flow and Metabolism 36:1537–1552.

     https://doi.org/10.1177/0271678X15608389

     • PubMed
     • Google Scholar
106. 1. Mchedlishvili G
     2. Kuridze N

     (1984) The modular organization of the pial arterial system in phylogeny

     Journal of Cerebral Blood Flow and Metabolism 4:391–396.

     https://doi.org/10.1038/jcbfm.1984.57

     • PubMed
     • Google Scholar
107. 1. Meixner CR
     2. Liebig P
     3. Speier P
     4. Forman C
     5. Hensel B
     6. Schmidt M
     7. Saake M
     8. Uder M
     9. Doerfler A
     10. Heidemann RM
     11. Schmitter S
     12. Nagel AM

     (2019) High resolution time-of-flight MR-angiography at 7 T exploiting VERSE saturation, compressed sensing and segmentation

     Magnetic Resonance Imaging 63:193–204.

     https://doi.org/10.1016/j.mri.2019.08.014

     • PubMed
     • Google Scholar
108. 1. Moccia S
     2. De Momi E
     3. El Hadji S
     4. Mattos LS

     (2018) Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics

     Computer Methods and Programs in Biomedicine 158:71–91.

     https://doi.org/10.1016/j.cmpb.2018.02.001

     • PubMed
     • Google Scholar
109. 1. Molinari F
     2. Liboni W
     3. Grippi G
     4. Negri E

     (2006) Relationship between oxygen supply and cerebral blood flow assessed by transcranial Doppler and near-infrared spectroscopy in healthy subjects during breath-holding

     Journal of Neuroengineering and Rehabilitation 3:16.

     https://doi.org/10.1186/1743-0003-3-16

     • PubMed
     • Google Scholar
110. 1. Mut F
     2. Wright S
     3. Ascoli GA
     4. Cebral JR

     (2014) Morphometric, geographic, and territorial characterization of brain arterial trees

     Int. J. Numer. Methods Biomed. Eng 30:755–766.

     https://doi.org/10.1002/cnm.2627

     • Google Scholar
111. 1. Nagaoka T
     2. Yoshida A

     (2006) Noninvasive Evaluation of Wall Shear Stress on Retinal Microcirculation in Humans

     Investig. Opthalmology Vis. Sci 47:1113.

     https://doi.org/10.1167/iovs.05-0218

     • Google Scholar
112. 1. Nägele T
     2. Klose U
     3. Grodd W
     4. Voigt K
     5. Nüsslin F

     (1995) Nonlinear excitation profiles for three-dimensional inflow MR angiography

     Journal of Magnetic Resonance Imaging: JMRI 5:416–420.

     https://doi.org/10.1002/jmri.1880050408

     • Google Scholar
113. 1. Nishimura DG
     2. Jackson JI
     3. Pauly JM

     (1991) On the nature and reduction of the displacement artifact in flow images

     Magnetic Resonance in Medicine 22:481–492.

     https://doi.org/10.1002/mrm.1910220255

     • PubMed
     • Google Scholar
114. 1. Nishimura DG
     2. Irarrazabal P
     3. Meyer CH

     (1995) A velocity k-space analysis of flow effects in echo-planar and spiral imaging

     Magnetic Resonance in Medicine 33:549–556.

     https://doi.org/10.1002/mrm.1910330414

     • PubMed
     • Google Scholar
115. 1. Nonaka H
     2. Akima M
     3. Hatori T
     4. Nagayama T
     5. Zhang Z
     6. Ihara F

     (2003a) Microvasculature of the human cerebral white matter: arteries of the deep white matter

     Neuropathology 23:111–118.

     https://doi.org/10.1046/j.1440-1789.2003.00486.x

     • PubMed
     • Google Scholar
116. 1. Nonaka H
     2. Akima M
     3. Nagayama T
     4. Hatori T
     5. Zhang Z
     6. Ihara F

     (2003b) Microvasculature of the human cerebral meninges

     Neuropatholog 23:129–135.

     https://doi.org/10.1046/j.1440-1789.2003.00487.x

     • Google Scholar
117. 1. North DO

     (1963) An Analysis of the factors which determine signal/noise discrimination in pulsed-carrier systems

     Proceedings of the IEEE 51:1016–1027.

     https://doi.org/10.1109/PROC.1963.2383

     • Google Scholar
118. 1. Nowinski WL
     2. Chua BC
     3. Marchenko Y
     4. Puspitsari F
     5. Volkau I
     6. Knopp MV

     (2011) Three-dimensional reference and stereotactic atlas of human cerebrovasculature from 7Tesla

     NeuroImage 55:986–998.

     https://doi.org/10.1016/j.neuroimage.2010.12.079

     • PubMed
     • Google Scholar
119. 1. Ogawa S
     2. Lee TM
     3. Kay AR
     4. Tank DW

     (1990) Brain magnetic resonance imaging with contrast dependent on blood oxygenation

     PNAS 87:9868–9872.

     https://doi.org/10.1073/pnas.87.24.9868

     • Google Scholar
120. 1. Papantchev V
     2. Stoinova V
     3. Aleksandrov A
     4. Todorova-Papantcheva D
     5. Hristov S
     6. Petkov D
     7. Nachev G
     8. Ovtscharoff W

     (2013) The role of Willis circle variations during unilateral selective cerebral perfusion: a study of 500 circles

     European Journal of Cardio-Thoracic Surgery 44:743–753.

     https://doi.org/10.1093/ejcts/ezt103

     • Google Scholar
121. 1. Park CS
     2. Hartung G
     3. Alaraj A
     4. Du X
     5. Charbel FT
     6. Linninger AA

     (2020) Quantification of blood flow patterns in the cerebral arterial circulation of individual (human) subjects

     Int. J. Numer. Methods Biomed. Eng 36:3288.

     https://doi.org/10.1002/cnm.3288

     • Google Scholar
122. 1. Parker DL
     2. Yuan C
     3. Blatter DD

     (1991) MR angiography by multiple thin slab 3D acquisition

     Magnetic Resonance in Medicine 17:434–451.

     https://doi.org/10.1002/mrm.1910170215

     • Google Scholar
123. 1. Parker DL
     2. Goodrich KC
     3. Roberts JA
     4. Chapman BE
     5. Jeong EK
     6. Kim SE
     7. Tsuruda JS
     8. Katzman GL

     (2003) The need for phase-encoding flow compensation in high-resolution intracranial magnetic resonance angiography

     Journal of Magnetic Resonance Imaging: JMRI 18:121–127.

     https://doi.org/10.1002/jmri.10322

     • Google Scholar
124. 1. Pauling L
     2. Coryell CD

     (1936) The magnetic properties and structure of hemoglobin, oxyhemoglobin and carbonmonoxyhemoglobin

     PNAS 22:210–216.

     https://doi.org/10.1073/pnas.22.4.210

     • Google Scholar
125. Book

     1. Payne SJ

     (2017) Cerebral Blood Flow and Metabolism

     World Scientific.

     https://doi.org/10.1142/10463

     • Google Scholar
126. 1. Peters AM
     2. Brookes MJ
     3. Hoogenraad FG
     4. Gowland PA
     5. Francis ST
     6. Morris PG
     7. Bowtell R

     (2007) T2* measurements in human brain at 1.5, 3 and 7 T

     Magnetic Resonance Imaging 25:748–753.

     https://doi.org/10.1016/j.mri.2007.02.014

     • PubMed
     • Google Scholar
127. Book

     1. Pfeifer RA

     (1930)

     Grundlegende Untersuchungen Für Die Angioarchitektonik Des Menschlichen Gehirns

     Berlin: Springer.

     • Google Scholar
128. 1. Phellan R
     2. Forkert ND

     (2017) Comparison of vessel enhancement algorithms applied to time-of-flight MRA images for cerebrovascular segmentation

     Medical Physics 44:5901–5915.

     https://doi.org/10.1002/mp.12560

     • PubMed
     • Google Scholar
129. 1. Pipe JG

     (2001) Limits of time-of-flight magnetic resonance angiography

     Topics in Magnetic Resonance Imaging 12:163–174.

     https://doi.org/10.1097/00002142-200106000-00003

     • PubMed
     • Google Scholar
130. 1. Pohmann R
     2. Speck O
     3. Scheffler K

     (2016) Signal-to-noise ratio and MR tissue parameters in human brain imaging at 3, 7, and 9.4 tesla using current receive coil arrays

     Magnetic Resonance in Medicine 75:801–809.

     https://doi.org/10.1002/mrm.25677

     • PubMed
     • Google Scholar
131. 1. Polimeni JR
     2. Fischl B
     3. Greve DN
     4. Wald LL

     (2010) Laminar analysis of 7T BOLD using an imposed spatial activation pattern in human V1

     NeuroImage 52:1334–1346.

     https://doi.org/10.1016/j.neuroimage.2010.05.005

     • PubMed
     • Google Scholar
132. 1. Pruessmann KP
     2. Weiger M
     3. Scheidegger MB
     4. Boesiger P

     (1999) SENSE: sensitivity encoding for fast MRI

     Magnetic Resonance in Medicine 42:952–962.

     https://doi.org/10.1002/(SICI)1522-2594(199911)42:53.0.CO;2-S

     • PubMed
     • Google Scholar
133. Book

     1. Ranson SW
     2. Clark SL

     (1959)

     The Anatomy of the Nervous System: Its Development and Function

     Philadelphia, Saunders.

     • Google Scholar
134. 1. Raymond SB
     2. Schaefer PW

     (2017) Imaging Brain Collaterals: Quantification, Scoring, and Potential Significance

     Topics in Magnetic Resonance Imaging 26:67–75.

     https://doi.org/10.1097/RMR.0000000000000123

     • PubMed
     • Google Scholar
135. 1. Reichenbach JR
     2. Venkatesan R
     3. Schillinger DJ
     4. Kido DK
     5. Haacke EM

     (1997) Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent

     Radiology 204:272–277.

     https://doi.org/10.1148/radiology.204.1.9205259

     • PubMed
     • Google Scholar
136. 1. Rooney WD
     2. Johnson G
     3. Li X
     4. Cohen ER
     5. Kim SG
     6. Ugurbil K
     7. Springer CS

     (2007) Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo

     Magnetic Resonance in Medicine 57:308–318.

     https://doi.org/10.1002/mrm.21122

     • PubMed
     • Google Scholar
137. 1. Rowbotham GF
     2. Little E

     (1965) CIRCULATIONS OF THE CEREBRAL HEMISPHERES

     The British Journal of Surgery 52:8–21.

     https://doi.org/10.1002/bjs.1800520104

     • PubMed
     • Google Scholar
138. 1. Rueckert D
     2. Glocker B
     3. Kainz B

     (2016) Learning clinically useful information from images: Past, present and future

     Medical Image Analysis 33:13–18.

     https://doi.org/10.1016/j.media.2016.06.009

     • Google Scholar
139. 1. Schmid F
     2. Barrett MJP
     3. Jenny P
     4. Weber B

     (2019) Vascular density and distribution in neocortex

     NeuroImage 197:792–805.

     https://doi.org/10.1016/j.neuroimage.2017.06.046

     • Google Scholar
140. 1. Schmitter S
     2. Bock M
     3. Johst S
     4. Auerbach EJ
     5. Uğurbil K
     6. Moortele P

     (2012) Contrast enhancement in TOF cerebral angiography at 7 T using saturation and MT pulses under SAR constraints: Impact of VERSE and sparse pulses

     Magnetic Resonance in Medicine 68:188–197.

     https://doi.org/10.1002/mrm.23226

     • Google Scholar
141. 1. Schulz J
     2. Boyacioglu R
     3. Norris DG

     (2016) Multiband multislab 3D time-of-flight magnetic resonance angiography for reduced acquisition time and improved sensitivity

     Magnetic Resonance in Medicine 75:1662–1668.

     https://doi.org/10.1002/mrm.25774

     • Google Scholar
142. 1. Shattuck DW
     2. Sandor-Leahy SR
     3. Schaper KA
     4. Rottenberg DA
     5. Leahy RM

     (2001) Magnetic Resonance Image Tissue Classification Using a Partial Volume Model

     NeuroImage 13:856–876.

     https://doi.org/10.1006/nimg.2000.0730

     • Google Scholar
143. 1. Shu CY
     2. Sanganahalli BG
     3. Coman D
     4. Herman P
     5. Hyder F

     (2016) New horizons in neurometabolic and neurovascular coupling from calibrated fMRI

     Progress in Brain Research 225:99–122.

     https://doi.org/10.1016/bs.pbr.2016.02.003

     • PubMed
     • Google Scholar
144. 1. Song HS
     2. Kang CK
     3. Kim JS
     4. Park CA
     5. Kim YB
     6. Lee DH
     7. Kang DW
     8. Kwon SU
     9. Cho ZH

     (2010) Assessment of pial branches using 7-tesla MRI in cerebral arterial disease

     Cerebrovascular Diseases (Basel, Switzerland) 29:410.

     https://doi.org/10.1159/000288056

     • PubMed
     • Google Scholar
145. 1. Stamm AC
     2. Wright CL
     3. Knopp MV
     4. Schmalbrock P
     5. Heverhagen JT

     (2013) Phase contrast and time-of-flight magnetic resonance angiography of the intracerebral arteries at 1.5, 3 and 7 T

     Magnetic Resonance Imaging 31:545–549.

     https://doi.org/10.1016/j.mri.2012.10.023

     • PubMed
     • Google Scholar
146. 1. Stefani MA
     2. Schneider FL
     3. Marrone ACH
     4. Severino AG
     5. Jackowski AP
     6. Wallace MC

     (2000) Anatomic variations of anterior cerebral artery cortical branches

     Clinical Anatomy (New York, N.Y.) 13:231–236.

     https://doi.org/10.1002/1098-2353(2000)13:4<231::AID-CA1>3.0.CO;2-T

     • PubMed
     • Google Scholar
147. 1. Stucht D
     2. Danishad KA
     3. Schulze P
     4. Godenschweger F
     5. Zaitsev M
     6. Speck O

     (2015) Highest Resolution In Vivo Human Brain MRI Using Prospective Motion Correction

     PLOS ONE 10:e0133921.

     https://doi.org/10.1371/journal.pone.0133921

     • PubMed
     • Google Scholar
148. 1. Suri JS
     2. Liu K
     3. Reden L
     4. Laxminarayan S

     (2002) A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II

     IEEE Transactions on Information Technology in Biomedicine 6:338–350.

     https://doi.org/10.1109/titb.2002.804136

     • PubMed
     • Google Scholar
149. Book

     1. Szikla G
     2. Bouvier G
     3. Hori T
     4. Petrov V

     (1977)

     Angiography of the Human Brain Cortex: Atlas of Vascular Patterns and Stereotactic Cortical Localization

     Berlin Heidelberg: Springer.

     • Google Scholar
150. 1. Tak S
     2. Wang DJJ
     3. Polimeni JR
     4. Yan L
     5. Chen JJ

     (2014) Dynamic and static contributions of the cerebrovasculature to the resting-state BOLD signal

     NeuroImage 84:672–680.

     https://doi.org/10.1016/j.neuroimage.2013.09.057

     • PubMed
     • Google Scholar
151. 1. Tang C
     2. Blatter DD
     3. Parker DL

     (1993) Accuracy of phase-contrast flow measurements in the presence of partial-volume effects

     Journal of Magnetic Resonance Imaging 3:377–385.

     https://doi.org/10.1002/jmri.1880030213

     • PubMed
     • Google Scholar
152. 1. Tetteh G
     2. Efremov V
     3. Forkert ND
     4. Schneider M
     5. Kirschke J
     6. Weber B
     7. Zimmer C
     8. Piraud M
     9. Menze BH

     (2020) DeepVesselNet: Vessel Segmentation, Centerline Prediction, and Bifurcation Detection in 3-D Angiographic Volumes

     Frontiers in Neuroscience 14:592352.

     https://doi.org/10.3389/fnins.2020.592352

     • PubMed
     • Google Scholar
153. 1. Todorov MI
     2. Paetzold JC
     3. Schoppe O
     4. Tetteh G
     5. Shit S
     6. Efremov V
     7. Todorov-Völgyi K
     8. Düring M
     9. Dichgans M
     10. Piraud M
     11. Menze B
     12. Ertürk A

     (2020) Machine learning analysis of whole mouse brain vasculature

     Nature Methods 17:442–449.

     https://doi.org/10.1038/s41592-020-0792-1

     • PubMed
     • Google Scholar
154. 1. Triantafyllou C
     2. Polimeni JR
     3. Wald LL

     (2011) Physiological noise and signal-to-noise ratio in fMRI with multi-channel array coils

     NeuroImage 55:597–606.

     https://doi.org/10.1016/j.neuroimage.2010.11.084

     • PubMed
     • Google Scholar
155. 1. Troprès I
     2. Grimault S
     3. Vaeth A
     4. Grillon E
     5. Julien C
     6. Payen JF
     7. Lamalle L
     8. Décorps M

     (2001) Vessel size imaging

     Magnetic Resonance in Medicine 45:397–408.

     https://doi.org/10.1002/1522-2594(200103)45:3<397::aid-mrm1052>3.0.co;2-3

     • PubMed
     • Google Scholar
156. 1. Tustison NJ
     2. Avants BB
     3. Cook PA
     4. Zheng Y
     5. Egan A
     6. Yushkevich PA
     7. Gee JC

     (2010) N4ITK: improved N3 bias correction

     IEEE Transactions on Medical Imaging 29:1310–1320.

     https://doi.org/10.1109/TMI.2010.2046908

     • PubMed
     • Google Scholar
157. 1. Uhl E
     2. Lehmberg J
     3. Steiger HJ
     4. Messmer K

     (2003) Intraoperative detection of early microvasospasm in patients with subarachnoid hemorrhage by using orthogonal polarization spectral imaging

     Neurosurgery 52:1307–1315.

     https://doi.org/10.1227/01.neu.0000065154.04824.9e

     • PubMed
     • Google Scholar
158. 1. Uludağ K
     2. Müller-Bierl B
     3. Uğurbil K

     (2009) An integrative model for neuronal activity-induced signal changes for gradient and spin echo functional imaging

     NeuroImage 48:150–165.

     https://doi.org/10.1016/j.neuroimage.2009.05.051

     • PubMed
     • Google Scholar
159. 1. Uludağ K
     2. Blinder P

     (2018) Linking brain vascular physiology to hemodynamic response in ultra-high field MRI

     NeuroImage 168:279–295.

     https://doi.org/10.1016/j.neuroimage.2017.02.063

     • PubMed
     • Google Scholar
160. 1. van der Zwan A
     2. Hillen B
     3. Tulleken CA
     4. Dujovny M

     (1993) A quantitative investigation of the variability of the major cerebral arterial territories

     Stroke 24:1951–1959.

     https://doi.org/10.1161/01.str.24.12.1951

     • PubMed
     • Google Scholar
161. 1. van Laar PJ
     2. Hendrikse J
     3. Golay X
     4. Lu H
     5. van Osch MJP
     6. van der Grond J

     (2006) In vivo flow territory mapping of major brain feeding arteries

     NeuroImage 29:136–144.

     https://doi.org/10.1016/j.neuroimage.2005.07.011

     • PubMed
     • Google Scholar
162. 1. Vanzetta I
     2. Hildesheim R
     3. Grinvald A

     (2005) Compartment-resolved imaging of activity-dependent dynamics of cortical blood volume and oximetry

     The Journal of Neuroscience 25:2233–2244.

     https://doi.org/10.1523/JNEUROSCI.3032-04.2005

     • PubMed
     • Google Scholar
163. 1. Venkatesan R
     2. Haacke EM

     (1997) Role of high resolution in magnetic resonance (MR) imaging: Applications to MR angiography, intracranialT1-weighted imaging, and image interpolation

     International Journal of Imaging Systems and Technology 8:529–543.

     https://doi.org/10.1002/(SICI)1098-1098(1997)8:6<529::AID-IMA5>3.0.CO;2-C

     • Google Scholar
164. 1. Von Morze C
     2. Xu D
     3. Purcell DD
     4. Hess CP
     5. Mukherjee P
     6. Saloner D
     7. Kelley DAC
     8. Vigneron DB

     (2007) Intracranial time-of-flight MR angiography at 7T with comparison to 3T

     Journal of Magnetic Resonance Imaging: JMRI 26:900–904.

     https://doi.org/10.1002/jmri.21097

     • Google Scholar
165. 1. Wang RK
     2. An L

     (2009) Doppler optical micro-angiography for volumetric imaging of vascular perfusion in vivo

     Optics Express 17:8926–8940.

     https://doi.org/10.1364/oe.17.008926

     • PubMed
     • Google Scholar
166. 1. Ward PGD
     2. Ferris NJ
     3. Raniga P
     4. Dowe DL
     5. Ng ACL
     6. Barnes DG
     7. Egan GF

     (2018) Combining images and anatomical knowledge to improve automated vein segmentation in MRI

     NeuroImage 165:294–305.

     https://doi.org/10.1016/j.neuroimage.2017.10.049

     • PubMed
     • Google Scholar
167. 1. Wei L
     2. Otsuka T
     3. Acuff V
     4. Bereczki D
     5. Pettigrew K
     6. Patlak C
     7. Fenstermacher J

     (1993) The velocities of red cell and plasma flows through parenchymal microvessels of rat brain are decreased by pentobarbital

     Journal of Cerebral Blood Flow and Metabolism 13:487–497.

     https://doi.org/10.1038/jcbfm.1993.63

     • PubMed
     • Google Scholar
168. 1. Wilms G
     2. Bosmans H
     3. Demaerel Ph
     4. Marchal G

     (2001) Magnetic resonance angiography of the intracranial vessels

     European Journal of Radiology 38:10–18.

     https://doi.org/10.1016/S0720-048X(01)00285-6

     • Google Scholar
169. 1. Wright PJ
     2. Mougin OE
     3. Totman JJ
     4. Peters AM
     5. Brookes MJ
     6. Coxon R
     7. Morris PE
     8. Clemence M
     9. Francis ST
     10. Bowtell RW
     11. Gowland PA

     (2008) Water proton T1 measurements in brain tissue at 7, 3, and 1.5 T using IR-EPI, IR-TSE, and MPRAGE: results and optimization

     Magma (New York, N.Y.) 21:121–130.

     https://doi.org/10.1007/s10334-008-0104-8

     • PubMed
     • Google Scholar
170. 1. Wright SN
     2. Kochunov P
     3. Mut F
     4. Bergamino M
     5. Brown KM
     6. Mazziotta JC
     7. Toga AW
     8. Cebral JR
     9. Ascoli GA

     (2013) Digital reconstruction and morphometric analysis of human brain arterial vasculature from magnetic resonance angiography

     NeuroImage 82:170–181.

     https://doi.org/10.1016/j.neuroimage.2013.05.089

     • PubMed
     • Google Scholar
171. 1. Xie L
     2. Kang H
     3. Xu Q
     4. Chen MJ
     5. Liao Y
     6. Thiyagarajan M
     7. O’Donnell J
     8. Christensen DJ
     9. Nicholson C
     10. Iliff JJ
     11. Takano T
     12. Deane R
     13. Nedergaard M

     (2013) Sleep drives metabolite clearance from the adult brain

     Science (New York, N.Y.) 342:373–377.

     https://doi.org/10.1126/science.1241224

     • PubMed
     • Google Scholar
172. 1. Xu C
     2. Schmidt WUH
     3. Villringer K
     4. Brunecker P
     5. Kiselev V
     6. Gall P
     7. Fiebach JB

     (2011) Vessel size imaging reveals pathological changes of microvessel density and size in acute ischemia

     Journal of Cerebral Blood Flow and Metabolism 31:1687–1695.

     https://doi.org/10.1038/jcbfm.2011.38

     • PubMed
     • Google Scholar
173. 1. You J
     2. Du C
     3. Volkow ND
     4. Pan Y

     (2014) Optical coherence Doppler tomography for quantitative cerebral blood flow imaging

     Biomedical Optics Express 5:3217–3230.

     https://doi.org/10.1364/BOE.5.003217

     • PubMed
     • Google Scholar
174. 1. Yushkevich PA
     2. Piven J
     3. Hazlett HC
     4. Smith RG
     5. Ho S
     6. Gee JC
     7. Gerig G

     (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability

     NeuroImage 31:1116–1128.

     https://doi.org/10.1016/j.neuroimage.2006.01.015

     • PubMed
     • Google Scholar
175. 1. Zaharchuk G
     2. Gong E
     3. Wintermark M
     4. Rubin D
     5. Langlotz CP

     (2018) Deep Learning in Neuroradiology

     AJNR. American Journal of Neuroradiology 39:1776–1784.

     https://doi.org/10.3174/ajnr.A5543

     • PubMed
     • Google Scholar
176. 1. Zhang Z
     2. Deng X
     3. Weng D
     4. An J
     5. Zuo Z
     6. Wang B
     7. Wei N
     8. Zhao J
     9. Xue R

     (2015) Segmented TOF at 7 T MRI: Technique and clinical applications

     Magnetic Resonance Imaging 33:1043–1050.

     https://doi.org/10.1016/j.mri.2015.07.002

     • PubMed
     • Google Scholar
177. 1. Zhao JM
     2. Clingman CS
     3. Närväinen MJ
     4. Kauppinen RA
     5. van Zijl PCM

     (2007) Oxygenation and hematocrit dependence of transverse relaxation rates of blood at 3T

     Magnetic Resonance in Medicine 58:592–597.

     https://doi.org/10.1002/mrm.21342

     • PubMed
     • Google Scholar
178. 1. Zhu X
     2. Tomanek B
     3. Sharp J

     (2013) A pixel is an artifact: On the necessity of zero-filling in fourier imaging

     Concepts in Magnetic Resonance Part A 42A:32–44.

     https://doi.org/10.1002/cmr.a.21256

     • Google Scholar

Author details

1. Saskia Bollmann

   1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
   2. Department of Radiology, Harvard Medical School, Boston, United States
   3. Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Visualization, Writing - original draft, Writing – review and editing

   For correspondence

   saskia.bollmann@cai.uq.edu.au

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8242-8008

2. Hendrik Mattern

   Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von- Guericke-University, Magdeburg, Germany

   Contribution

   Formal analysis, Investigation, Methodology, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5740-4522

3. Michaël Bernier

   1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
   2. Department of Radiology, Harvard Medical School, Boston, United States

   Contribution

   Conceptualization, Software, Writing – review and editing

   Competing interests

   No competing interests declared

4. Simon D Robinson

   1. Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia
   2. High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
   3. Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Vienna, Austria
   4. Department of Neurology, Medical University of Graz, Graz, Austria

   Contribution

   Resources, Software, Writing – review and editing

   Competing interests

   No competing interests declared

5. Daniel Park

   Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States

   Contribution

   Resources, Software, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7263-9327

6. Oliver Speck

   1. Department of Biomedical Magnetic Resonance, Institute of Experimental Physics, Otto-von- Guericke-University, Magdeburg, Germany
   2. German Center for Neurodegenerative Diseases, Magdeburg, Germany
   3. Center for Behavioral Brain Sciences, Magdeburg, Germany
   4. Leibniz Institute for Neurobiology, Magdeburg, Germany

   Contribution

   Funding acquisition, Methodology, Project administration, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

7. Jonathan R Polimeni

   1. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, United States
   2. Department of Radiology, Harvard Medical School, Boston, United States
   3. Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, United States

   Contribution

   Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1348-1179

• 4,345

  views

• 594

  downloads

• 38

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.