Intro: Diffusion MRI allows for in vivo probing of brain tissue architecture via micron-level diffusion aggregated at the millimeter scale, but crossing white matter fibers decrease the sensitivity of downstream tasks such as microstructural modeling and tractography. Current fixel-based methods for identifying the num...
Adam M. Saunders, G. Rudravaram, Michael E. Kim et al.· 0 citations
Low-dose chest computed tomography scans acquired in multi-centre studies exhibit heterogeneity in the form of varying spatial resolution, reconstruction kernel and semantic field-of-view that hinder the reproducibility of quantitative measures and generalizability of downstream analyses. The National Lung Screening Tr...
A. Krishnan, Michael E. Kim, Chen-Yu Gao et al.· PLoS ONE· 0 citations
Medical computer vision presents unique challenges compared to natural images, largely due to the high dimensionality of the data (e.g., 3D and 4D imaging) and limited training samples. Certain medical imaging modalities, such as Diffusion-weighted Magnetic Resonance Imaging (dMRI) - a critical tool for examining brain...
Tian-Yuan Yao, Zhi-Yuan Li, Michael E. Kim et al.· Medical Image Analysis· 0 citations
Abstract. Purpose Normative modeling of quantitative brain measurements is being widely adopted as a promising method for charting population-level developmental trajectories and identifying abnormalities in groups and individuals. Generalized additive models for location, scale, and shape (GAMLSS) are a statistical fr...
Michael E. Kim, G. Rudravaram, Adam M. Saunders et al.· Journal of Medical Imaging· 0 citations
Diffusion tractography is the prominent in-vivo technique to study and investigate white-matter pathways in the human brain. While tractography is a powerful method, recent work suggests that different tractography methods can produce strikingly different representations of the same white-matter pathway. This multitude...
Matthew Amandola, B. Herlin, Michael E. Kim et al.· Brain Structure and Function· 0 citations
A fully automated, three-stage cascade pipeline that combines subject-adaptive unsupervised candidate generation with two successive lightweight 3D ResNet classifiers, trained with only human-in-the-loop quality-assurance (QA) labels rather than dense voxel-wise segmentation masks is presented.
Sam Bogdanov, G. Rudravaram, Adam M. Saunders et al.· bioRxiv· 0 citations
A simple power analysis indicates that these subtle effects of gradient nonlinearity correction can become statistically detectable in larger multi-site studies exceeding ~1000 subjects, suggesting that GNL should be considered and, where possible, corrected or at least quantified in such settings.
Praitayini Kanakaraj, Tianyuan Yao, Zhiyuan Li et al.· PLoS ONE· 0 citations
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