Biology subjects

Prior, M.

Publications and source records attributed to Prior, M..

2 recordsLinked to original sources

bioRxiv2021

Avoiding Data Loss: Synthetic MRIs Generated from Diffusion Imaging Can Replace Corrupted Structural Acquisitions For Freesurfer-Seeded Tractography

1Magnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily scrubbed of motion affected volumes, the same is not true for structural images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations which allow simulation of MRI intensities given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88 - 0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00 - 0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons. HighlightsO_LIWe propose a simple means of synthesizing T1w and T2w images from diffusion data C_LIO_LIThe proposed method worked well for a variety of acquisitions C_LIO_LISynthetic images showed tissue contrast akin to acquired images C_LIO_LISynthetic images were high enough quality to be used for Freesurfer seeded diffusion tractography C_LIO_LIThis method enables analysis of datasets where motion has corrupted acquired structural MRIs C_LI

neuroscience↗

bioRxiv2020

Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms.

Unruptured intracranial aneurysms (UIAs) are prevalent neurovascular anomalies which, in rare circumstances, rupture to create a catastrophic subarachnoid haemorrhage. Although surgical management can reduce rupture risk, the majority of IAs exist undiscovered until rupture. Current computer-aided UIA diagnoses sensitively detect and measure UIAs within cranial angiograms, but remain limited to low specificities whose output requires considerable neuroradiologist interpretation not amenable to broad screening efforts. To address these limitations, we propose an analysis which interprets single-voxel morphometry of segmented neurovasculature to identify UIAs. Once neurovascular anatomy of a specified resolution is segmented, interrelationships between voxel-specific morphometries are estimated and spatially-clustered outliers are identified as UIA candidates. Our automated solution detects UIAs within magnetic resonance angiograms (MRA) at unmatched 86% specificity and 81% sensitivity using 3 minutes on a conventional laptop. Our approach does not rely on interpatient comparisons or training datasets which could be difficult to amass and process for rare incidentally discovered UIAs within large MRA files, and in doing so, is versatile to user-defined segmentation quality, to detection sensitivity, and across a range of imaging resolutions and modalities. We propose this method as a unique tool to aid UIA screening, characterisation of abnormal vasculature in at-risk patients, morphometry-based rupture risk prediction, and identification of other vascular abnormalities. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/216812v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@166f3aborg.highwire.dtl.DTLVardef@76ea1org.highwire.dtl.DTLVardef@1f03619org.highwire.dtl.DTLVardef@16fd797_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG Highlights O_LIRapid and automated detection of unruptured intracranial aneurysms (UIAs) in MRAs C_LIO_LIHighly specific, sensitive UIA detection to reduce radiologist input for screening C_LIO_LIDetection is versatile to image resolution, modality and has tuneable mm sensitivity C_LI

bioinformatics↗