Author

K. G. Schilling

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Bioengineering· 6

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Assay:imaging· 5 Research Focus:benchmarking· 4 Approach:deep learning· 4 Approach:transformer / foundation model· 3 Organism:human· 2 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

B. A. Landman· 6 shared L. Y. Cai· 5 shared F. Rheault· 3 shared P. Kanakaraj· 3 shared Q. Yang· 3 shared A. T. Shafer· 2 shared B. N. Conrad· 2 shared C. B. Hansen· 2 shared

Activity by Year

2020-2023 · peak 2/yr

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10 top collaborators · 40 mutual co-author links

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6 shared papersBioengineering

B. A. Landman

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6 papers

Recent·Most cited

Implementation considerations for deep learning with diffusion MRI streamline tractography

L. Y. Cai, H. H. Lee, N. R. Newlin, M. E. Kim, D. Moyer, F. Rheault, K. G. Schilling, B. A. Landman

This paper discusses implementation considerations for deep learning with diffusion MRI streamline tractography and provides an open-source implementation with comparable performance to existing algorithms.

bioRxiv · Apr 3, 2023 · PreprintBioengineering

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

L. Y. Cai, H. H. Lee, N. R. Newlin, C. I. Kerley, P. Kanakaraj, Q. Yang, G. W. Johnson, D. Moyer, K. G. Schilling, F. Rheault, B. A. Landman

This paper develops a convolutional-recurrent neural network to approximate diffusion tractography from T1-weighted MRI, showing results comparable to traditional dMRI tractography.

bioRxiv · Feb 25, 2023 · PreprintBioengineering

SynBOLD-DisCo: Synthetic BOLD images for distortion correction of fMRI without additional calibration scans

T. Yu, L. Y. Cai, V. L. Morgan, S. E. Goodale, D. J. Englot, C. E. Chang, B. A. Landman, K. G. Schilling

This paper introduces SynBOLD-DisCo, a method to correct fMRI distortion using synthetic BOLD images generated from structural images, without needing additional calibration scans.

bioRxiv · Sep 13, 2022 · PreprintBioengineering

MASiVar: Multisite, Multiscanner, and Multisubject Acquisitions for Studying Variability in Diffusion Weighted Magnetic Resonance Imaging

L. Y. Cai, Q. Yang, P. Kanakaraj, V. Nath, A. T. Newton, H. A. Edmonson, J. Luci, B. N. Conrad, G. R. Price, C. B. Hansen, C. I. Kerley, K. Ramadass, F.-C. Yeh, H. Kang, E. Garyfallidis, , B. A. Landman

MASiVar evaluates DWI variability across multiple levels and processing approaches using a multisite, multisubject dataset.

Magnetic Resonance in Medicine · Jul 16, 2021Peer-reviewedBioengineering

PreQual: An automated pipeline for integrated preprocessing and quality assurance of diffusion weighted MRI images

L. Y. Cai, Q. Yang, C. B. Hansen, V. Nath, K. Ramadass, G. W. Johnson, B. N. Conrad, B. D. Boyd, J. P. Begnoche, L. L. Beason-Held, A. T. Shafer, S. M. Resnick, W. D. Taylor, G. R. Price, V. L. Morgan, , B. A. Landman

The paper introduces PreQual, an automated pipeline that preprocesses and quality assures diffusion weighted MRI images using multiple tools and generates QA reports.

bioRxiv · Sep 14, 2020Bioengineering

Distortion correction of diffusion weighted MRI without reverse phase-encoding scans or field-maps

K. G. Schilling, J. Blaber, C. Hansen, L. Cai, B. Rogers, A. W. Anderson, S. Smith, P. Kanakaraj, T. Rex, S. M. Resnick, A. T. Shafer, L. Cutting, N. Woodward, D. Zald, B. A. Landman

This study develops a deep learning method to correct diffusion MRI distortions without reverse phase-encoding scans or field maps, showing effective correction on diverse datasets.

bioRxiv · Jan 19, 2020 · PreprintBioengineering