#mri reconstruction

Content tagged with "mri reconstruction"

gGRAPPA: A flexible, GPU-accelerated python package for fast and efficient generalized GRAPPA reconstruction
2025-09-16 Maxime Bertrait, Chaithya Giliyar Radhakrishna, Philippe Ciuciu ISMRM & ISMRT 2025-Annual Meeting & Exhibition
#Parallel Imaging #Open-Source Software #MRI #MRI reconstruction #GPU acceleration #CAIPIRINHA

Motivation: Existing open-source MR reconstruction tools often fail to efficiently utilize GPU resources and lack support for generalized GRAPPA implementations. Many tools are limited to 2D or 3D reconstruction, and few incorporate advanced techniques such as 2D-CAIPIRINHA, which enhances imaging capabilities.

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Benchmarking learned non-Cartesian k-space trajectories and reconstruction networks
2022-01-27 Chaithya Giliyar Radhakrishna, Philippe Ciuciu CoRR
#Non-Cartesian Sampling #k-space Sampling #MRI #trajectory learning #MRI reconstruction #projected gradient descent

We benchmark the current existing methods to jointly learn non-Cartesian k-space trajectory and reconstruction: PILOT, BJORK, and compare them with those obtained from the recently developed generalized hybrid learning (HybLearn) framework. We present the advantages of using projected gradient descent to enforce MR…

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NC-PDNet: A density-compensated unrolled network for 2D and 3D non-Cartesian MRI reconstruction
2022-01-18 Zaccharie Ramzi, Chaithya Giliyar Radhakrishna, Jean-Luc Starck, Philippe Ciuciu IEEE Transactions on Medical Imaging
#Non-Cartesian Sampling #Deep Learning #Quantitative MRI #Open-Source Software #MRI #MRI reconstruction

Deep Learning has become a very promising avenue for magnetic resonance image (MRI) reconstruction. In this work, we explore the potential of unrolled networks for non-Cartesian acquisition settings.

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