Personal academic portfolio and blog of Chaithya G R, researcher in accelerated MRI acquisition and reconstruction.https://chaithyagr.github.io/https://chaithyagr.github.io/publications/2026-mri-non-uniform-fast-fourier-transform-mri-nufft/https://chaithyagr.github.io/publications/2026-mri-non-uniform-fast-fourier-transform-mri-nufft/MRI-NUFFT is an open-source Python library that provides state-of-the-art non-Cartesian MRI tools: trajectories, data loading, fast and memory-efficient operators to be used on laptops, clusters, and MRI consoles.Thu, 18 Jun 2026 00:00:00 GMThttps://chaithyagr.github.io/publications/2026-dd-inr-dynamics-driven-implicit-neural-representation-for-accelerated/https://chaithyagr.github.io/publications/2026-dd-inr-dynamics-driven-implicit-neural-representation-for-accelerated/Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging with high k-space undersampling: Task-evoked BOLD signals are small in magnitude, which traditional anatomical MRI reconstruction methods fail to recover, as they favor…Tue, 09 Jun 2026 00:00:00 GMThttps://chaithyagr.github.io/publications/2026-combining-cartesian-and-non-cartesian-acceleration-techniques-with-spa/https://chaithyagr.github.io/publications/2026-combining-cartesian-and-non-cartesian-acceleration-techniques-with-spa/Purpose: T1-weighted MPRAGE remains a cornerstone of clinical anatomical imaging, yet its long acquisition times constrain routine use. Established acceleration techniques, namely Parallel Imaging (PI) and Compressed Sensing (CS), tend to introduce substantial noise and blurring when pushed to high acceleration…Fri, 29 May 2026 00:00:00 GMThttps://chaithyagr.github.io/publications/2026-towards-elucidating-ripple-artifacts-encountered-in-accelerated-3d-gre/https://chaithyagr.github.io/publications/2026-towards-elucidating-ripple-artifacts-encountered-in-accelerated-3d-gre/International audienceSat, 09 May 2026 00:00:00 GMThttps://chaithyagr.github.io/publications/2026-weakly-convex-ridge-regularization-for-3d-non-cartesian-mri-reconstruc/https://chaithyagr.github.io/publications/2026-weakly-convex-ridge-regularization-for-3d-non-cartesian-mri-reconstruc/While highly accelerated non-Cartesian acquisition protocols significantly reduce scan time, they often entail long reconstruction delays. Deep learning based reconstruction methods can alleviate this, but often lack stability and robustness to distribution shifts.Sat, 28 Mar 2026 00:00:00 GMThttps://chaithyagr.github.io/publications/2025-on-the-impact-of-artifacts-induced-by-mismatches-between-auto-calibrat/https://chaithyagr.github.io/publications/2025-on-the-impact-of-artifacts-induced-by-mismatches-between-auto-calibrat/PURPOSE: field inhomogeneity artifacts arising from remote locations in the FOV and encountered in accelerated 3D gradient-recalled echo (GRE) sequences at ultra-high field, and at providing mitigation strategies. METHODS: Measurements were conducted at 11.7T using a head-shaped phantom and an accelerated 3D GRE…Sat, 18 Oct 2025 00:00:00 GMThttps://chaithyagr.github.io/publications/2025-benchmarking-3d-multi-coil-nc-pdnet-mri-reconstruction/https://chaithyagr.github.io/publications/2025-benchmarking-3d-multi-coil-nc-pdnet-mri-reconstruction/Motivation: Deep Neural Networks (DNNs) show promise in reconstructing undersampled MRI data but face challenges with non-Cartesian 3D multi-coil acquisitions due to high memory demands and training complexity. Goal(s): To address the research gap in validating DNNs performances for non-Cartesian data in 3D multi-coil…Tue, 16 Sep 2025 00:00:00 GMThttps://chaithyagr.github.io/publications/2025-bringing-grappa-to-non-cartesian-mri-through-sparkling-an-application/https://chaithyagr.github.io/publications/2025-bringing-grappa-to-non-cartesian-mri-through-sparkling-an-application/Motivation: Well established clinically used parallel imaging techniques like GRAPPA and CAIPIRINHA are mostly limited to Cartesian sampling, and their extension to non-Cartesian sampling is not direct. Goal(s): We propose to extend the SPARKLING framework to ensure GRAPPA accelerated Cartesian sampling in k-space…Tue, 16 Sep 2025 00:00:00 GMThttps://chaithyagr.github.io/publications/2025-ggrappa-a-flexible-gpu-accelerated-python-package-for-fast-and-efficie/https://chaithyagr.github.io/publications/2025-ggrappa-a-flexible-gpu-accelerated-python-package-for-fast-and-efficie/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.Tue, 16 Sep 2025 00:00:00 GMThttps://chaithyagr.github.io/publications/2025-mri-nufft-doing-non-cartesian-mri-has-never-been-easier/https://chaithyagr.github.io/publications/2025-mri-nufft-doing-non-cartesian-mri-has-never-been-easier/MRI-NUFFT is a python package that provides a universal interface to various Non-Uniform Fast Fourier Transform libraries running on CPU or GPU (gpuNUFFT, finufft, cufinufft, pyNFFT), adding compatibility with standard array library (NumPy, CuPy, PyTorch, TensorFlow, etc.) On top of these libraries it extends the…Tue, 15 Apr 2025 00:00:00 GMThttps://chaithyagr.github.io/publications/2025-mri-nufft-an-open-source-python-package-to-make-non-cartesian-mr-imagi/https://chaithyagr.github.io/publications/2025-mri-nufft-an-open-source-python-package-to-make-non-cartesian-mr-imagi/Motivation: Non-Cartesian imaging remains complicated to use for MRI due to the high computational cost of the Non-Uniform Fourier Transform for image reconstruction. Goal(s): To provide a uniform interface for reconstructing magnetic resonance images from non-Cartesian k-space data and a collection of non-Cartesian…Tue, 26 Nov 2024 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-mc-pdnet-deep-unrolled-neural-network-for-multi-contrast-mr-image-reco/https://chaithyagr.github.io/publications/2022-mc-pdnet-deep-unrolled-neural-network-for-multi-contrast-mr-image-reco/International audienceWed, 02 Oct 2024 00:00:00 GMThttps://chaithyagr.github.io/publications/2024-benchmarking-common-and-advanced-non-cartesian-trajectories-with-high/https://chaithyagr.github.io/publications/2024-benchmarking-common-and-advanced-non-cartesian-trajectories-with-high/Non-Cartesian sampling patterns allow for highly accelerated MRI exams at the cost of exacerbated and more complex artifacts, each impacting image quality in unique ways. Patient-induced B0 field inhomogeneities can notably cause distortions and blurring, but some trajectories are by design more robust to them.Wed, 14 Aug 2024 00:00:00 GMThttps://chaithyagr.github.io/publications/2024-merging-cartesian-and-non-cartesian-sampling-through-golf-sparkling/https://chaithyagr.github.io/publications/2024-merging-cartesian-and-non-cartesian-sampling-through-golf-sparkling/Cartesian sampling can acquire a given k-space region with minimum redundancy, while non-Cartesian sampling can help achieve a larger k-space coverage. Through generalized affine constraints in SPARKLING and an adapted target sampling density, for the first time non-Cartesian and Cartesian sampling are merged within…Wed, 14 Aug 2024 00:00:00 GMThttps://chaithyagr.github.io/publications/2024-non-cartesian-3d-sparkling-vs-cartesian-3d-epi-encoding-schemes-for-fu/https://chaithyagr.github.io/publications/2024-non-cartesian-3d-sparkling-vs-cartesian-3d-epi-encoding-schemes-for-fu/The quest for higher spatial and/or temporal resolution in functional MRI (fMRI) while preserving a sufficient temporal signal-to-noise ratio (tSNR) has generated a tremendous amount of methodological contributions in the last decade ranging from Cartesian vs. non-Cartesian readouts, 2D vs.Mon, 13 May 2024 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-method-and-apparatus-for-performing-accelarated-magnetic-resonance-ima/https://chaithyagr.github.io/publications/2023-method-and-apparatus-for-performing-accelarated-magnetic-resonance-ima/Patent application · 2023Wed, 01 Nov 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-b0-field-distortions-monitoring-and-correction-for-3d-non-cartesian-fm/https://chaithyagr.github.io/publications/2022-b0-field-distortions-monitoring-and-correction-for-3d-non-cartesian-fm/The benefit of retrospective correction of static and dynamic B0 field imperfections was studied on non-Cartesian compressed sensing-based acquisition patterns by taking as an example 3D-SPARKLING encoding scheme. Long-TR dynamic acquisitions of T2*-weighted MRI (fMRI-like) volumes were acquired at 7T, retrospectively…Thu, 03 Aug 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-more-sparkling-non-cartesian-trajectories-with-minimized-off-resonance/https://chaithyagr.github.io/publications/2022-more-sparkling-non-cartesian-trajectories-with-minimized-off-resonance/We augment the recently introduced SPARKLING algorithm and propose an improved mathematical formulation that takes the temporal dependence of the MR signal into account. This prevents the trajectories from sampling similar portions of k-space at different times, thereby reducing distortions and blurring induced by (B0…Thu, 03 Aug 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-prospects-of-non-cartesian-3d-sparkling-encoding-for-functional-mri-a/https://chaithyagr.github.io/publications/2022-prospects-of-non-cartesian-3d-sparkling-encoding-for-functional-mri-a/For the first time, non-Cartesian 3D-SPARKLING (Spreading Projection Algorithm for Rapid K-space sampLING) encoding scheme was used for task-based fMRI (retinotopic mapping) at 7T. Additionally, this new acquisition technique was compared with 3D-EPI, considered as one of the reference acquisition schemes in…Thu, 03 Aug 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-evaluation-of-3d-sparkling-readout-for-sodium-ute-mri-at-ultra-high-ma/https://chaithyagr.github.io/publications/2023-evaluation-of-3d-sparkling-readout-for-sodium-ute-mri-at-ultra-high-ma/Quantitative 23Na MRI provides useful information about brain tissue homeostasis. Most sequences use deterministic non-Cartesian 3D trajectories such as TPI.Thu, 03 Aug 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2024-impact-of-b0-field-imperfections-correction-on-bold-sensitivity-in-3d/https://chaithyagr.github.io/publications/2024-impact-of-b0-field-imperfections-correction-on-bold-sensitivity-in-3d/Purpose Static and dynamic field imperfections are detrimental to functional MRI (fMRI) applications, especially at ultra‐high magnetic fields (UHF). In this work, a field camera is used to assess the benefits of retrospectively correcting field perturbations on Blood Oxygen Level Dependent (BOLD) sensitivity in…Sat, 03 Jun 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-improving-spreading-projection-algorithm-for-rapid-k-space-sampling-tr/https://chaithyagr.github.io/publications/2023-improving-spreading-projection-algorithm-for-rapid-k-space-sampling-tr/Purpose Non‐Cartesian MRI with long arbitrary readout directions are susceptible to off‐resonance artifacts due to patient induced inhomogeneities. This results in degraded image quality with strong signal losses and blurring.Mon, 22 May 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-designing-and-learning-non-cartesian-k-space-sampling-trajectories-for/https://chaithyagr.github.io/publications/2023-designing-and-learning-non-cartesian-k-space-sampling-trajectories-for/Conception et apprentissage de trajectoires d'échantillonnage non cartésiennes dans l'espace k pour l'IRM 3D accélérée L'imagerie par résonance magnétique (IRM) est une technique d'imagerie médicale non invasive très répandue qui permet de comprendre les structures des tissus du corps humain. Cependant, sa portée et…Tue, 25 Apr 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-irm-cerebrale-du-sodium-rapide-avec-sparkling-3d-sous-echantillonnee-a/https://chaithyagr.github.io/publications/2023-irm-cerebrale-du-sodium-rapide-avec-sparkling-3d-sous-echantillonnee-a/Journal article · Journal of Neuroradiology · 2023Wed, 01 Mar 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-jointly-learning-non-cartesian-k-space-trajectories-and-reconstruction/https://chaithyagr.github.io/publications/2023-jointly-learning-non-cartesian-k-space-trajectories-and-reconstruction/Compressed sensing in magnetic resonance imaging essentially involves the optimization of (1) the sampling pattern in k-space under MR hardware constraints and (2) image reconstruction from undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems simultaneously,…Tue, 24 Jan 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2023-accelerated-sodium-mri-using-undersampled-3d-sparkling-at-7t/https://chaithyagr.github.io/publications/2023-accelerated-sodium-mri-using-undersampled-3d-sparkling-at-7t/Submitted to Magnetic Resonance in MedicineMon, 09 Jan 2023 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-impact-de-la-correction-des-imperfections-b0-sur-les-donnees-irmf-coll/https://chaithyagr.github.io/publications/2022-impact-de-la-correction-des-imperfections-b0-sur-les-donnees-irmf-coll/Preprint (arXiv) · HAL (Le Centre pour la Communication Scientifique Directe) · 2022Mon, 19 Dec 2022 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-3d-sparkling-for-functional-mri-a-pilot-study-for-retinotopic-mapping/https://chaithyagr.github.io/publications/2022-3d-sparkling-for-functional-mri-a-pilot-study-for-retinotopic-mapping/International audienceTue, 07 Jun 2022 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-benchmarking-learned-non-cartesian-k-space-trajectories-and-reconstruc/https://chaithyagr.github.io/publications/2022-benchmarking-learned-non-cartesian-k-space-trajectories-and-reconstruc/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…Thu, 27 Jan 2022 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-nc-pdnet-a-density-compensated-unrolled-network-for-2d-and-3d-non-cart/https://chaithyagr.github.io/publications/2022-nc-pdnet-a-density-compensated-unrolled-network-for-2d-and-3d-non-cart/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.Tue, 18 Jan 2022 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-3d-sparkling-en-irmf-application-a-la-retinotopie/https://chaithyagr.github.io/publications/2022-3d-sparkling-en-irmf-application-a-la-retinotopie/Journal article · 2022Sat, 01 Jan 2022 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-pysap-from-galaxies-to-brains-and-beyond/https://chaithyagr.github.io/publications/2022-pysap-from-galaxies-to-brains-and-beyond/Conference paper · Astronomical Data Analysis Software and Systems XXX · 2022Sat, 01 Jan 2022 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-hybrid-learning-of-non-cartesian-k-space-trajectory-and-mr-image-recon/https://chaithyagr.github.io/publications/2022-hybrid-learning-of-non-cartesian-k-space-trajectory-and-mr-image-recon/Compressed sensing (CS) in Magnetic resonance Imaging (MRI) essentially involves the optimization of 1) the sampling pattern in k-space under MR hardware constraints and 2) image reconstruction from the undersampled k-space data. Recently, deep learning methods have allowed the community to address both problems…Mon, 25 Oct 2021 00:00:00 GMThttps://chaithyagr.github.io/publications/2021-learning-the-sampling-density-in-2d-sparkling-mri-acquisition-for-opti/https://chaithyagr.github.io/publications/2021-learning-the-sampling-density-in-2d-sparkling-mri-acquisition-for-opti/The SPARKLING algorithm was originally developed for accelerated 2D magnetic resonance imaging (MRI) in the compressed sensing (CS) context. It yields non-Cartesian sampling trajectories that jointly fulfill a target sampling density while each individual trajectory complies with MR hardware constraints.Mon, 23 Aug 2021 00:00:00 GMThttps://chaithyagr.github.io/publications/2022-optimizing-full-3d-sparkling-trajectories-for-high-resolution-t2-weigh/https://chaithyagr.github.io/publications/2022-optimizing-full-3d-sparkling-trajectories-for-high-resolution-t2-weigh/The Spreading Projection Algorithm for Rapid K-space samplING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D T2*-w MRI using compressed sensing. It has then been extended to address 3D imaging using either stacks of 2D sampling patterns or a local 3D strategy that…Fri, 06 Aug 2021 00:00:00 GMThttps://chaithyagr.github.io/publications/2021-calibration-less-multi-coil-compressed-sensing-magnetic-resonance-imag/https://chaithyagr.github.io/publications/2021-calibration-less-multi-coil-compressed-sensing-magnetic-resonance-imag/Over the last decade, the combination of compressed sensing (CS) with acquisition over multiple receiver coils in magnetic resonance imaging (MRI) has allowed the emergence of faster scans while maintaining a good signal-to-noise ratio (SNR). Self-calibrating techniques, such as ESPiRIT, have become the standard…Fri, 19 Mar 2021 00:00:00 GMThttps://chaithyagr.github.io/publications/2020-pysap-python-sparse-data-analysis-package/https://chaithyagr.github.io/publications/2020-pysap-python-sparse-data-analysis-package/Journal article · Astrophysics Source Code Library · 2020Sat, 01 Aug 2020 00:00:00 GMThttps://chaithyagr.github.io/publications/2020-pysap-python-sparse-data-analysis-package-for-multidisciplinary-image/https://chaithyagr.github.io/publications/2020-pysap-python-sparse-data-analysis-package-for-multidisciplinary-image/We present the open-source image processing software package PySAP (Python Sparse data Analysis Package) developed for the COmpressed Sensing for Magnetic resonance Imaging and Cosmology (COSMIC) project. This package provides a set of flexible tools that can be applied to a variety of compressed sensing and image…Thu, 25 Jun 2020 00:00:00 GMThttps://chaithyagr.github.io/publications/2020-pysap-mri-a-python-package-for-mr-image-reconstruction/https://chaithyagr.github.io/publications/2020-pysap-mri-a-python-package-for-mr-image-reconstruction/International audienceWed, 01 Jan 2020 00:00:00 GMThttps://chaithyagr.github.io/publications/2018-a-multi-center-study-on-human-brain-glutathione-conformation-using-mag/https://chaithyagr.github.io/publications/2018-a-multi-center-study-on-human-brain-glutathione-conformation-using-mag/Molecular dynamics simulation and in vitro nuclear magnetic resonance (NMR) studies on glutathione (GSH) indicated existence of closed and extended conformations. The present work in a multi-center research setting reports in-depth analysis of GSH conformers in vivo using a common magnetic resonance spectroscopy (MRS)…Tue, 04 Sep 2018 00:00:00 GMThttps://chaithyagr.github.io/publications/2017-ieee-signal-processing-cup-2017-real-time-beat-tracking-challenge/https://chaithyagr.github.io/publications/2017-ieee-signal-processing-cup-2017-real-time-beat-tracking-challenge/Journal article · 2017Sun, 01 Jan 2017 00:00:00 GMThttps://chaithyagr.github.io/publications/2016-data-transfer-using-mcm-code/https://chaithyagr.github.io/publications/2016-data-transfer-using-mcm-code/Multicolored Matrix (MCM) Code is a two-dimensional color coded matrix. It consists a sequence of images rendered at an optimum frame rate to transfer simple data between portable devices such as a mobile phone.Tue, 01 Nov 2016 00:00:00 GMThttps://chaithyagr.github.io/publications/compressed-sensing-accelerated-susceptibility-weighted-imaging-at-3t-w/https://chaithyagr.github.io/publications/compressed-sensing-accelerated-susceptibility-weighted-imaging-at-3t-w/Journal article