Background: Fiber tracking with diffusion-weighted MRI 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. Purpose: To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap. Study Type: A systematic review of algorithms and tract reproducibility studies. Subjects: Single healthy volunteers.View this article online at wileyonlinelibrary.com.
47Purpose: Fiber tracking with diffusion weighted magnetic resonance imaging has become an 48 essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are 49 sensitive to the choice of processing method and tracking criteria. Phantom studies provide 50 concrete quantitative comparisons of methods relative to absolute ground truths, yet do not capture 51 variabilities because of in vivo physiological factors. 52 Methods: To date, a large-scale reproducibility analysis has not been performed for the assessment 53 of the newest generation of tractography algorithms with in vivo data. Reproducibility does not 54 assess the validity of a brain connection however it is still of critical importance because it 55 describes the variability for an algorithm in group studies. The ISMRM 2017 TraCED challenge 56 was created to fulfill the gap. The TraCED dataset consists of a single healthy volunteer scanned 57 on two different scanners of the same manufacturer. The multi-shell acquisition included b-values 58 of 1000, 2000 and 3000 s/mm 2 with 20, 45 and 64 diffusion gradient directions per shell, 59 respectively. 60 Results: Nine international groups submitted 46 tractography algorithm entries. The top five 61 submissions had high ICC > 0.88. Reproducibility is high within these top 5 submissions when 62 assessed across sessions or across scanners. However, it can be directly attributed to containment 63 of smaller volume tracts in larger volume tracts. This holds true for the top five submissions where 64 they are contained in a specific order. While most algorithms are contained in an ordering there 65 are some outliers. 66 Conclusion: The different methods clearly result in fundamentally different tract structures at the 67 more conservative specificity choices (i.e., volumetrically smaller tractograms). The data and 68 challenge infrastructure remain available for continued analysis and provide a platform for 69 comparison. 70 Keywords: Tractography, Reproducibility, in vivo, Challenge, DW-MRI, HARDI 71 72 73 74 75Despite the wide range of validation studies, there have been few reproducibility studies of 105 tractography [19][20][21]. Rather than ask how right (or wrong) tractography is, we ask how stable are 106 the outputs of these techniques? Because tractography is an essential part of track segmentation, 107 network analysis, and microstructural imaging, it is important that reproducibility is high, 108otherwise power is lost in group analyses or in longitudinal comparisons. In this study, given a 109 standard, clinically realistic, diffusion protocol, we aim to assess how reproducible tractography 110 results are between repeats, between scanners, and between algorithms. 111Publicly organized challenges provide unique opportunities for research communities to fairly 112 compare algorithms in an unbiased format, resulting in quantitative measures of the reliability and 113 limitation of competing approaches, as well as potential strategies for improving consistency. In 114 the ...
Image modality recognition is essential for efficient imaging workflows in current clinical environments, where multiple imaging modalities are used to better comprehend complex diseases. Emerging biomarkers from novel, rare modalities are being developed to aid in such understanding, however the availability of these images is often limited. This scenario raises the necessity of recognising new imaging modalities without them being collected and annotated in large amounts. In this work, we present a few-shot learning model for limited training examples based on Deep Triplet Networks. We show that the proposed model is more accurate in distinguishing different modalities than a traditional Convolutional Neural Network classifier when limited samples are available. Furthermore, we evaluate the performance of both classifiers when presented with noisy samples and provide an initial inspection of how the proposed model can incorporate measures of uncertainty to be more robust against out-of-sample examples.
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