no code implementations • 21 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.
no code implementations • 3 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.
no code implementations • 6 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.
no code implementations • 22 Nov 2021 • Aniket Pramanik, Mathews Jacob
Model-based deep learning (MoDL) algorithms that rely on unrolling are emerging as powerful tools for image recovery.
no code implementations • 19 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.
no code implementations • 1 Feb 2021 • Aniket Pramanik, Mathews Jacob
We introduce an image domain deep network for calibrationless recovery of undersampled PMRI data.
no code implementations • 20 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.
1 code implementation • 7 Dec 2019 • Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
The main challenge with this strategy is the high computational complexity of matrix completion.
no code implementations • 27 Nov 2019 • Aniket Pramanik, Hemant Aggarwal, Mathews Jacob
We introduce a fast model based deep learning approach for calibrationless parallel MRI reconstruction.
no code implementations • 27 Dec 2018 • Aniket Pramanik, Hemant Kumar Aggarwal, Mathews Jacob
We introduce a model based off-the-grid image reconstruction algorithm using deep learned priors.