17 citations · 25 across the 9 of their papers we have counts for
9 papers
Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning
Jeffrey Dominic, Nandita Bhaskhar, Arjun D. Desai +8
Although supervised learning has enabled high performance for image segmentation, it requires a large amount of labeled training data, which can be difficult to obtain in the medic…
SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation
Arjun D Desai, Andrew M Schmidt, Elka B Rubin +9
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and d…
VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction
Arjun D Desai, Beliz Gunel, Batu M Ozturkler +6
Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstructio…
Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning
Arjun D Desai, Batu M Ozturkler, Christopher M Sandino +7
Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled)…
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 +26
Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthriti…
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks
Arjun D. Desai, Garry E. Gold, Brian A. Hargreaves +1
High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications…
Circumventing the Curse of Dimensionality in Magnetic Resonance Fingerprinting through a Deep Learning Approach
Marco Barbieri, Leonardo Brizi, Enrico Giampieri +4
MR fingerprinting (MRF) is a rapid growing approach for fast quantitave MRI. A typical drawback of dictionary-based MRF is its explosion in size as a function of the number of reco…
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee +2
Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and resea…
On-the-fly Adaptive -Space Sampling for Linear MRI Reconstruction Using Moment-Based Spectral Analysis
Evan Levine, Brian Hargreaves
In high-dimensional magnetic resonance imaging applications, time-consuming, sequential acquisition of data samples in the spatial frequency domain (-space) can often be acceler…