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Akshay S. Chaudhari
Researcher at Stanford University
Publications - 79
Citations - 1091
Akshay S. Chaudhari is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 14, co-authored 54 publications receiving 604 citations. Previous affiliations of Akshay S. Chaudhari include University of California, San Diego.
Papers
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Journal ArticleDOI
Super-resolution musculoskeletal MRI using deep learning.
Akshay S. Chaudhari,Zhongnan Fang,Feliks Kogan,Jeffrey P. Wood,Kathryn J. Stevens,Eric K. Gibbons,Jin Hyung Lee,Garry E. Gold,Brian A. Hargreaves +8 more
TL;DR: To develop a super‐resolution technique using convolutional neural networks for generating thin‐slice knee MR images from thicker input slices, and compare this method with alternative through‐plane interpolation methods.
Journal ArticleDOI
Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning.
Eric K. Gibbons,Kyler K. Hodgson,Akshay S. Chaudhari,Lorie Richards,Jennifer J. Majersik,Ganesh Adluru,Edward V. R. DiBella +6 more
TL;DR: To develop a robust multidimensional deep‐learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q‐space datasets for use in stroke imaging.
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Utility of deep learning super-resolution in the context of osteoarthritis MRI biomarkers.
Akshay S. Chaudhari,Kathryn J. Stevens,Jeffrey P. Wood,Amit Chakraborty,Eric K. Gibbons,Zhongnan Fang,Arjun D. Desai,Jin Hyung Lee,Garry E. Gold,Brian A. Hargreaves +9 more
TL;DR: This work has shown that super‐resolution is an emerging method for enhancing MRI resolution and its impact on image quality is still unknown.
Journal ArticleDOI
Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.
Akshay S. Chaudhari,Christopher M. Sandino,Elizabeth K. Cole,David B. Larson,Garry E. Gold,Shreyas S. Vasanawala,Matthew P. Lungren,Brian A. Hargreaves,Curtis P. Langlotz +8 more
TL;DR: A framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices is provided.
Journal ArticleDOI
The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset
Arjun D. Desai,Francesco Caliva,Claudia Iriondo,Aliasghar Mortazi,Sachin Jambawalikar,Ulas Bagci,Mathias Perslev,Christian Igel,Erik B. Dam,Sibaji Gaj,Mingrui Yang,Xiaojuan Li,Cem M. Deniz,Vladimir Juras,Ravinder R. Regatte,Garry E. Gold,Brian A. Hargreaves,Valentina Pedoia,Akshay S. Chaudhari +18 more
TL;DR: Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.