Hendrik Mattern^{1} and Oliver Speck^{1,2,3,4}

^{1}Biomedical Magnetic Resonance, Otto von Guericke University, Magdeburg, Germany, ^{2}German Center for Neurodegenerative Disease, Magdeburg, Germany, ^{3}Center for Behavioral Brain Sciences, Magdeburg, Germany, ^{4}Leibniz Institute for Neurobiology, Magdeburg, Germany

The detection of vessel in MRI depends on the imaging resolution used. Hence, subsequent quantification with vessel densities and vessel distance mapping (VDM) is resolution dependent. In this study, the resolution dependency of both metrics was evaluated for arteries and veins in deep gray matter regions by retrospective downsampling of high-resolution vessel images. Quantitative comparison shows that with decreasing resolution the estimated vessel distances increase, while vessel densities decline. The resolution dependency of vessel densities and distances can be modeled linearly. The here found scaling factors may improve inter-study comparability of vessel densities and VDM.

Five subjects were scanned at 7T (Siemens Healthineers, Erlangen, Germany) with a 32-channel head coil (Nova Medical, Wilmington, USA) after given written consent (study approved by local ethic committee). Time-of-Flight (ToF) angiography at 0.2mm isotropic resolution and susceptibility-weighted gradient echo data at 0.35mm isotropic resolution were acquired. ToF images were bias-field corrected with the N4 algorithm

To isolate the vasculature, arteries and veins were enhanced using the Jerman Filter

To assess the effect of the effective resolution, the data was downsampled after acquisition and prior to vessel segmentation and quantification. To that end, the data was transferred to k-space and the high frequency components were set to zero. Thus, the nominal voxel size remained constant throughout the analysis, but the effective resolution decreased, hence, the effective voxel length and volume increased. Retrospective downsampling was performed to increase the effective voxel volume from 100% to 200%, 400%, 600%, 800%, 1500%, 2500% and 2700%, respectively. Due to the cubic relation of voxel length and volume, this corresponds to a factor 1.0 to 3.0 increase of the effective voxel edge length. Hence, for each subject, arterial and venous densities as well as VDM were computed for 8 different effective resolutions. Box plots were used to compare the mean densities and distances of all subjects bilaterally in the thalamus, putamen, pallidum, and caudate nucleus across the different effective resolutions. To investigate the resolution dependency throughout deep gray matter, the vessel densities and distances across all regions and subjects were averaged and plotted against the effective voxel volume. Linear regression, correlation coefficients, and root-mean-square-percent-error (RMSPE) were used to investigate how accurately the resolution dependency can be modeled by a linear scaling factor.

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Figure 1: Effect of effective voxel length on vessel
assessment: Partial Maximum Intensity Projections (MIP) of ToF angiography and
vessel segmentation along a single vessel distance map (VDM)slice show that
with increasing voxel size the depicted and segmented vasculature declines (red
arrows). Consequently, the arterial densities decrease and distances increase.

Figure 2: Effect of effective voxel length on vessel
assessment: Partial Maximum Intensity Projections (MIP) of QSM and vessel
segmentation along a single vessel distance map (VDM) slice show that with
increasing voxel size the depicted and segmented vasculature declines (red
arrows). Consequently, the arterial densities decrease and distances increase.

Figure 3: Vessel density and vessel distance map (VDM) box plots (5 subjects) for arteries and veins detected in deep gray matter regions. By increasing the effective voxel volume (zero-filling in k-space), ergo reducing the effective resolution, less vessels are detected and segmented subsequently. Hence, average distances increase while densities decline. Note that initial downsampling (i.e. 100% to 200%) can reduce the noise level and, therefore, improve vessel segmentation marginally.

Figure 4: Average arterial and venous density vs.
relative effective voxel volume. The average across all subjects and deep gray
matter regions is plotted with a solid and linear regression result with a
dotted line, respectively. With increasing effective voxel volume, the average
densities decrease. Note that downsampling can reduce the noise level and,
therefore, improve vessel segmentation, resulting in an increase in venous
density (i.e. 100% to 200%).

Figure 5: Average arterial and venous distance vs.
effective voxel volume. The average across all subjects and deep gray matter
regions is plotted with a solid and linear regression result with a dotted
line, respectively. With increasing effective voxel volume, the average
distances increases as well.

DOI: https://doi.org/10.58530/2022/1251