2 citations · 2 across the 5 of their papers we have counts for
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Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)
Aniket Pramanik, Mathews Jacob
Model-based deep learning methods that combine imaging physics with learned regularization priors have been emerging as powerful tools for parallel MRI acceleration. The main focus…
Improved Model based Deep Learning using Monotone Operator Learning (MOL)
Aniket Pramanik, Mathews Jacob
Model-based deep learning (MoDL) algorithms that rely on unrolling are emerging as powerful tools for image recovery. In this work, we introduce a novel monotone operator learning…
Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstr…
Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)
Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction. The proposed scheme is a non-linear generalization of structured low rank (S…
Off-the-grid model based deep learning (O-MODL)
Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors. The main difference of the proposed scheme with current deep learning strategies i…