Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Abstract

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

Keywords: MRI acquisition; compressed sensing; diffusion-weighted imaging; white matter.

Figure 1:

Description of the examined diffusion schemes with the histogram of b-values and distribution of b-vectors in q-space. B-vectors are scaled, and color coded by b-value. HA-SC: Homogenous Angular Sampling Scheme; RAND: Random Sampling Scheme.

Figure 2:

Preprocessing schematic and analytic overview.

Figure 3:

Graphical representation of range of Dice scores. The exemplar bundle shown here is the left Arcuate Fasciculus. The first two columns (A and B) show the left Arcuate Fasciculus derived from different DSI images (Top row: same participant, different full DSI sessions; Bottom row: same participant and session, RAND57 and full DSI) The third column shows voxels that overlap between the bundles (green), and differences (red).

Figure 4:

Schematic of DSI scalars examined.

Figure 5:

All CS images can segment both long-range and short-range bundles, comparable with those derived from a full DSI image. These figures are exemplar segmentations of different bundles in a single participant from the retrospective dataset.

Figure 6

CS-DSI bundle segmentation accuracy is comparable and correlated with full DSI inter-scan reliability both within and across scan sessions. a) Same-scan accuracy of CS-DSI schemes is comparable with inter-scan reliability of the full DSI scheme. Violins represent distributions of Dice scores across all bundles. b) Median distribution of the same-scan accuracy for each CS-DSI scheme is highly correlated with the median distribution of the inter-scan reliability of the full DSI scheme. Here, each point on the scatter plot represents the median of Dice scores for a single bundle across participants and sessions. The gray dashed line denotes x=y. c) Inter-scan accuracy of CS-DSI schemes is comparable with inter-scan reliability of the full DSI scheme. Violins represent distributions of Dice scores across all bundles. d) Median distribution of the inter-scan accuracy for each CS-DSI scheme is highly correlated with the median distribution of the inter-scan reliability of the full DSI scheme. Here, each point on the scatter plot represents the median of Dice scores for a single bundle across participants and session pairs. The gray dashed line denotes x=y.

Figure 7:

CS-DSI inter-scan reliability of bundle segmentation is comparable to and correlated with full DSI inter-scan reliability. a) CS-DSI inter-scan reliability is comparable with full DSI inter-scan reliability. Violins represent distributions of Dice score across bundles and participants. b) Median distribution of the inter-scan reliability for each CS-DSI scheme is highly correlated with the median distribution of the inter-scan reliability of the full DSI scheme. Here, each scatter dot represents the across-participant median of Dice scores for a single bundle. The gray dashed line denotes x=y.

Figure 8:

Comparing full DSI reliability with CS-DSI same-scan accuracy (a-c), inter-scan accuracy (d-f) and inter-scan reliability (g-i) when deriving whole-brain voxel-wise scalar maps.

Figure 9:

CS-DSI accuracy of bundle segmentation can be replicated in a dataset where CS-DSI images are prospectively acquired. a) Dice scores between most CS-segmented bundles and combined DSI-segmented bundles are similar to the inter-scan reliability of full DSI bundles from the retrospective data. Violins represent distributions of Dice scores across all participants and bundles for a given scheme. b) Median distribution of the accuracy for each CS-DSI scheme is highly correlated with the median distribution of the full DSI inter-scan reliability from the retrospective data. Here, each point on the scatter plot represents the median of Dice scores for a single bundle across participants. The gray dashed lined denotes x=y.

Figure 10:

CS-DSI accuracy of scalar map generation can be replicated in a dataset where CS-DSI data is prospectively acquired. Pearson correlations of the voxel-wise scalars between the CS schemes and the combined scheme in the prospective dataset are similar to the full DSI inter-scan reliability from the retrospective data.