Biology subjects

Blaber, J. A.

Publications and source records attributed to Blaber, J. A..

3 recordsLinked to original sources

bioRxiv2020

Practical gradient non-linearity correction of multi-site diffusion weighted MRI with empirical field maps

BackgroundAchieving inter-site / inter-scanner reproducibility of diffusion weighted magnetic resonance imaging (DW-MRI) metrics has been challenging given differences in acquisition protocols, analysis models, and hardware factors. PurposeMagnetic field gradients impart scanner-dependent spatial variations in the applied diffusion weighting that can be corrected if the gradient nonlinearities are known. However, retrieving manufacturer nonlinearity specifications is not well supported and may introduce errors in interpretation of units or coordinate systems. We propose an empirical approach to mapping the gradient nonlinearities with sequences that are supported across the major scanner vendors. Study TypeProspective observational study SubjectsA spherical isotropic diffusion phantom, and a single human control volunteer Field Strength/Sequence3T (two scanners). Stejskal-Tanner spin echo sequence with b-values of 1000, 2000 s/mm2 with 12, 32, and 384 diffusion gradient directions per shell. AssessmentWe compare the proposed correction with the prior approach using manufacturer specifications against typical diffusion pre-processing pipelines (i.e., ignoring spatial gradient nonlinearities). In phantom data, we evaluate metrics against the ground truth. In human and phantom data, we evaluate reproducibility across scans, sessions, and hardware. Statistical TestsWilcoxon rank-sum test between uncorrected and corrected data. ResultsIn phantom data, our correction method reduces variation in mean diffusivity across sessions over uncorrected data (p<0.05). In human data, we show that this method can also reduce variation in mean diffusivity across scanners (p<0.05). ConclusionOur method is relatively simple, fast, and can be applied retroactively. We advocate incorporating voxel-specific b-value and b-vector maps should be incorporated in DW-MRI harmonization preprocessing pipelines to improve quantitative accuracy of measured diffusion parameters.

neuroscience↗

bioRxiv2019

TractEM: Fast Protocols for Whole Brain Deterministic Tractography-Based White Matter Atlas

Reproducible identification of white matter tracts across subjects is essential for the study of structural connectivity of the human brain. The key challenges are anatomical differences between subjects and human rater subjectivity in labeling. Labeling white matter regions of interest presents many challenges due to the need to integrate both local and global information. Clearly communicating the human/manual processes to capture this information is cumbersome, yet essential to lay a solid foundation for comprehensive atlases. The state-of-the-art for white matter atlas is the single population-averaged Johns Hopkins Eve atlas. A critical bottleneck with the Eve atlas framework is that manual labeling time is extensive and peripheral white matter regions are conservatively labeled. In this work, we developed protocols that will facilitate manual virtual dissection of white matter pathways, with the goals to be anatomically accurate, intuitive, reproducible, and act as an initial stage to build an amenable knowledge base of neuroanatomical regions. We analyzed reproducibility of the fiber bundles and variability of human raters using DICE correlation coefficient, intraclass correlation coefficient, and root mean squared error. The protocols at their initial stage have shown promising results on both typical 3T research acquisition Baltimore Longitudinal Study of Aging and high-acquisition quality Human Connectome Project datasets. The TractEM manual labeling protocols allow for reconstruction of reproducible subject-specific fiber bundles across the brain. The protocols and sample results have been made available in open source to improve generalizability and reliability in collaboration.

neuroscience↗

bioRxiv2018

Tractography Reproducibility Challenge with Empirical Data (TraCED): The 2017 ISMRM Diffusion Study Group Challenge

PurposeFiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do not capture variabilities because of in vivo physiological factors.\n\nMethodsTo date, a large-scale reproducibility analysis has not been performed for the assessment of the newest generation of tractography algorithms with in vivo data. Reproducibility does not assess the validity of a brain connection however it is still of critical importance because it describes the variability for an algorithm in group studies. The ISMRM 2017 TraCED challenge was created to fulfill the gap. The TraCED dataset consists of a single healthy volunteer scanned on two different scanners of the same manufacturer. The multi-shell acquisition included b-values of 1000, 2000 and 3000 s/mm2 with 20, 45 and 64 diffusion gradient directions per shell, respectively.\n\nResultsNine international groups submitted 46 tractography algorithm entries. The top five submissions had high ICC > 0.88. Reproducibility is high within these top 5 submissions when assessed across sessions or across scanners. However, it can be directly attributed to containment of smaller volume tracts in larger volume tracts. This holds true for the top five submissions where they are contained in a specific order. While most algorithms are contained in an ordering there are some outliers.\n\nConclusionThe different methods clearly result in fundamentally different tract structures at the more conservative specificity choices (i.e., volumetrically smaller tractograms). The data and challenge infrastructure remain available for continued analysis and provide a platform for comparison.

neuroscience↗