Search Results for author: Aniket Pramanik

Found 10 papers, 1 papers with code

Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL

no code implementations21 Apr 2023 Aniket Pramanik, Sampada Bhave, Saurav Sajib, Samir D. Sharma, Mathews Jacob

Purpose: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings and field strengths.

Accelerated parallel MRI using memory efficient and robust monotone operator learning (MOL)

no code implementations3 Apr 2023 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.

Compressive Sensing Operator learning

Memory-efficient model-based deep learning with convergence and robustness guarantees

no code implementations6 Jun 2022 Aniket Pramanik, M. Bridget Zimmerman, Mathews Jacob

The proposed iterative algorithm alternates between a gradient descent involving the score function and a conjugate gradient algorithm to encourage data consistency.

Compressive Sensing Operator learning

Improved Model based Deep Learning using Monotone Operator Learning (MOL)

no code implementations22 Nov 2021 Aniket Pramanik, Mathews Jacob

Model-based deep learning (MoDL) algorithms that rely on unrolling are emerging as powerful tools for image recovery.

Operator learning Rolling Shutter Correction

Joint Calibrationless Reconstruction and Segmentation of Parallel MRI

no code implementations19 May 2021 Aniket Pramanik, Xiaodong Wu, Mathews Jacob

We introduce a novel image domain deep-learning framework for calibrationless parallel MRI reconstruction, coupled with a segmentation network to improve image quality and to reduce the vulnerability of current segmentation algorithms to image artifacts resulting from acceleration.

MRI Reconstruction Segmentation

Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR

no code implementations1 Feb 2021 Aniket Pramanik, Mathews Jacob

We introduce an image domain deep network for calibrationless recovery of undersampled PMRI data.

Segmentation

ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms

no code implementations20 Oct 2020 Hemant Kumar Aggarwal, Aniket Pramanik, Maneesh John, Mathews Jacob

We introduce a novel metric termed the ENsemble Stein's Unbiased Risk Estimate (ENSURE) framework, which can be used to train deep image reconstruction algorithms without fully sampled and noise-free images.

Image Denoising Image Reconstruction

Calibrationless Parallel MRI using Model based Deep Learning (C-MODL)

no code implementations27 Nov 2019 Aniket Pramanik, Hemant Aggarwal, Mathews Jacob

We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction.

MRI Reconstruction

Off-the-grid model based deep learning (O-MODL)

no code implementations27 Dec 2018 Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob

We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors.

Image Reconstruction

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