64 citations · 91 across the 21 of their papers we have counts for
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Dynamic Imaging using Deep Bi-linear Unsupervised Regularization (DEBLUR)
Abdul Haseeb Ahmed, Prashant Nagpal, Mathews Jacob
Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data. These methods rely on spar…
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI
Aniket Pramanik, Xiaodong Wu, Mathews Jacob
The volume estimation of brain regions from MRI data is a key problem in many clinical applications, where the acquisition of data at high spatial resolution is desirable. While pa…
Dynamic imaging using a deep generative SToRM (Gen-SToRM) model
Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal +2
We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that…
Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR
Aniket Pramanik, Mathews Jacob
The main focus of this work is a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for cali…
Deep Generative SToRM model for dynamic imaging
Qing Zou, Abdul Haseeb Ahmed, Prashant Nagpal +2
We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed gene…