Author

B. A. Landman

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioengineering· 8

Signature Assays, Organisms & Methods

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Assay:imaging· 7 Research Focus:benchmarking· 4 Approach:deep learning· 4 Approach:experimental assay· 3 Organism:human· 3 Approach:transformer / foundation model· 3 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1

Frequent Co-Authors

K. G. Schilling· 6 shared L. Y. Cai· 6 shared F. Rheault· 3 shared H. H. Lee· 3 shared K. Ramadass· 3 shared P. Kanakaraj· 3 shared Q. Yang· 3 shared A. T. Shafer· 2 shared

Activity by Year

2020-2023 · peak 4/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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6 shared papersBioengineering

K. G. Schilling

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Author profile →

8 papers

Recent·Most cited

Characterizing Low-cost Registration for Photographic Images to Computed Tomography

M. E. Kim, H. H. Lee, K. Ramadass, C. Gao, K. Van Schaik, E. Tkaczyk, J. M. Spraggins, D. C. Moyer, B. A. Landman

The paper compares low-cost photographic imaging methods with a 3-D camera for registering photographic images to CT scans, finding iPhone imaging has lower landmark error in some cases.

bioRxiv · Sep 22, 2023 · PreprintBioengineering

Spatiospectral image processing workflow considerations for advanced MR spectroscopy of the brain

L. Y. Cai, S. N. Del Tufo, L. Barquero, M. D'Archangel, L. Sachs, L. Cutting, N. Glaser, S. Ghetti, S. S. Jaser, A. W. Anderson, L. C. Jordan, B. A. Landman

The paper describes a framework for processing advanced MR spectroscopy data, integrating with LCModel to improve spatial and spectral analysis of brain metabolites.

bioRxiv · Sep 7, 2023 · PreprintBioengineering

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