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15 results on '"Chaithya, G. R."'

5. Optimizing Full 3D SPARKLING Trajectories for High-Resolution Magnetic Resonance Imaging

Author

Chaithya, G. R., Weiss, Pierre, Daval-Frerot, Guillaume, Massire, Aurelien, Vignaud, Alexandre, and Ciuciu, Philippe

Subjects
Imaging, Three-Dimensional, Radiological and Ultrasound Technology, Phantoms, Imaging, Image Processing, Computer-Assisted, Prospective Studies, Electrical and Electronic Engineering, Magnetic Resonance Imaging, Algorithms, Software, Retrospective Studies, Computer Science Applications
Abstract

The Spreading Projection Algorithm for Rapid K-space sampLING, or SPARKLING, is an optimization-driven method that has been recently introduced for accelerated 2D 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 optimizes a single sampling trajectory at a time. 2D SPARKLING actually performs variable density sampling (VDS) along a prescribed target density while maximizing sampling efficiency and meeting the gradient-based hardware constraints. However, 3D SPARKLING has remained limited in terms of acceleration factors along the third dimension if one wants to preserve a peaky point spread function (PSF) and thus good image quality. In this paper, in order to achieve higher acceleration factors in 3D imaging while preserving image quality, we propose a new efficient algorithm that performs optimization on full 3D SPARKLING. The proposed implementation based on fast multipole methods (FMM) allows us to design sampling patterns with up to 10

Published
2022

6. Irm cerebrale du sodium rapide avec sparkling 3d sous-echantillonnee à 7 tesla

Author

Renata Porciuncula Baptista, Mathieu Naudin, Chaithya G R, Guillaume Daval-Frerot, Franck Mauconduit, Alexa Haeger, Sandro Romanzetti, Marc Lapert, Philippe Ciuciu, Cecile Rabrait-Lerman, Remy Guillevin, Alexandre Vignaud, and Fawzi Boumezbeur

Subjects
Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging, Neurology (clinical)
Published
2023

7. Hybrid learning of Non-Cartesian k-space trajectory and MR image reconstruction networks

Author

Chaithya G R, Zaccharie Ramzi, Philippe Ciuciu, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modèles et inférence pour les données de Neuroimagerie (MIND), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), IEEE, European Project: 800945,NUMERICS, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)

Subjects
Signal Processing (eess.SP), acquisition, joint optimization, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Optimization and Control (math.OC), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Electrical Engineering and Systems Science - Signal Processing, Mathematics - Optimization and Control, non-Cartesian trajectories, MRI
Abstract

International audience; 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 simultaneously, especially in the non-Cartesian acquisition setting. This paper aims to contribute to this field by tackling some major concerns in existing approaches.Regarding the learning of the sampling pattern, we perform ablation studies using parameter-free reconstructions like the density compensated (DCp) adjoint operator of the nonuniform fast Fourier transform (NUFFT) to ensure that the learned k-space trajectories actually sample the center of k-space densely. Additionally we optimize these trajectories by embedding a projected gradient descent algorithm over the hardware MR constraints. Later, we introduce a novel hybrid learning approach that operates across multiple resolutions to jointly optimize the reconstruction network and the k-space trajectory and present improved image reconstruction quality at 20-fold acceleration factor on T1 and T2-weighted images on the fastMRI dataset with SSIM scores of nearly 0.92-0.95 in our retrospective studies.

Published
2022

8. Jointly Learning Non-Cartesian k-Space Trajectories and Reconstruction Networks for 2D and 3D MR Imaging through Projection

Author

Chaithya G R and Philippe Ciuciu

Subjects
non-Cartesian, k-space trajectories, Bioengineering, reconstruction networks, MRI
Abstract

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, especially in the non-Cartesian acquisition setting. This work aims to contribute to this field by tackling some major concerns in existing approaches. Particularly, current state-of-the-art learning methods seek hardware compliant k-space sampling trajectories by enforcing the hardware constraints through additional penalty terms in the training loss. Through ablation studies, we rather show the benefit of using a projection step to enforce these constraints and demonstrate that the resulting k-space trajectories are more flexible under a projection-based scheme, which results in superior performance in reconstructed image quality. In 2D studies, our novel method trajectories present an improved image reconstruction quality at a 20-fold acceleration factor on the fastMRI data set with SSIM scores of nearly 0.92–0.95 in our retrospective studies as compared to the corresponding Cartesian reference and also see a 3–4 dB gain in PSNR as compared to earlier state-of-the-art methods. Finally, we extend the algorithm to 3D and by comparing optimization as learning-based projection schemes, we show that data-driven joint learning-based method trajectories outperform model-based methods such as SPARKLING through a 2 dB gain in PSNR and 0.02 gain in SSIM.

Published
2023

9. NC-PDNet: a Density-Compensated Unrolled Network for 2D and 3D non-Cartesian MRI Reconstruction

Author

Jean-Luc Starck, Chaithya G R, Philippe Ciuciu, Zaccharie Ramzi, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Service NEUROSPIN (NEUROSPIN), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), We acknowledge the financial support of the CrossDisciplinary Program on Numerical Simulation of CEA for the project entitled SILICOSMIC. We also acknowledge the French Institute of development and resources in scientific computing (IDRIS) for their AI program allowing us to usethe Jean Zay supercomputer’s GPU partitions., Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Modèles et inférence pour les données de Neuroimagerie (MIND), and IFR49 - Neurospin - CEA

Subjects
Radiological and Ultrasound Technology, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Brain, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], Signal-To-Noise Ratio, Magnetic Resonance Imaging, Computer Science Applications, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Neural Networks, Computer, Electrical and Electronic Engineering, Software
Abstract

This work is an extended version of the work presented at the 2021 ISBI conference.; International audience; 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 the non-Cartesian acquisition setting. We design the NC-PDNet, the first density-compensated unrolled network and validate the need for its key components via an ablation study. Moreover, we conduct some generalizability experiments to test our network in out-of-distribution settings, for example training on knee data and validating on brain data. The results show that the NC-PDNet outperforms the baseline models visually and quantitatively in the 2D settings. Additionally, in the 3D settings, it outperforms them visually. In particular, in the 2D multi-coil acquisition scenario, the NC-PDNet provides up to a 1.2 dB improvement in peak signal-to-noise ratio (PSNR) over baseline networks, while also allowing a gain of at least 1 dB in PSNR in generalization settings. We provide the opensource implementation of our network, and in particular the Non-uniform Fourier Transform in TensorFlow, tested on 2D multi-coil and 3D data.

Published
2021

10. Learning the sampling density in 2D SPARKLING MRI acquisition for optimized image reconstruction

Author

Chaithya G R, Zaccharie Ramzi, Philippe Ciuciu, Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), IFR49 - Neurospin - CEA, Commissariat à l'énergie atomique et aux énergies alternatives (CEA), European Project: 800945,NUMERICS, Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and IEEE

Subjects
Signal Processing (eess.SP), 010102 general mathematics, 01 natural sciences, reconstruction networks, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Compressed Sensing, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Optimization and Control (math.OC), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], Electrical Engineering and Systems Science - Signal Processing, 0101 mathematics, Mathematics - Optimization and Control, non-Cartesian trajectories, MRI
Abstract

International audience; 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. However, the two main limitations of SPARKLING are first that the optimal target sampling density is unknown and thus a user-defined parameter and second that this sampling pattern generation remains disconnected from MR image reconstruction thus from the optimization of image quality. Recently, datadriven learning schemes such as LOUPE have been proposed to learn a discrete sampling pattern, by jointly optimizing the whole pipeline from data acquisition to image reconstruction. In this work, we merge these methods with a state-of-the-art deep neural network for image reconstruction, called XPDNET, to learn the optimal target sampling density. Next, this density is used as input parameter to SPARKLING to obtain 20x accelerated non-Cartesian trajectories. These trajectories are tested on retrospective compressed sensing (CS) studies and show superior performance in terms of image quality with both deep learning (DL) and conventional CS reconstruction schemes.

Published
2021

13. PySAP-MRI: a Python Package for MR Image Reconstruction

Author

Loubna Gueddari, Chaithya G R, Zaccharie Ramzi, Samuel Farrens, Sophie Starck, Antoine Grigis, Jean-Luc Starck, Philippe Ciuciu, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Astrophysique Interprétation Modélisation (AIM (UMR7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7), Laboratoire de Cosmologie et Statistiques (LCS - COSMOSTAT), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, and Ciuciu, Philippe

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-SE] Computer Science [cs]/Software Engineering [cs.SE], [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE]
Abstract

International audience; Target audience: It is expected that the audience has preliminary knowledge of classical MRI acquisition and reconstruction techniques. Pysap-mri is aimed at researchers who need fast MR image reconstruction algorithms for under-sampled k-space data. It has been fully tested on Linux Ubuntu 16.04/18.04 LTS and Mac OS operating systems.Purpose: We present the open-source MRI plugin, called pysap-mri, of the software package PySAP (Python Sparse data Analysis Package). PySAP offers a large set of fast wavelet transforms and a range of integrated optimization algorithms in Python. The plugin pysap-mri provides methods, tools and examples for MR image reconstruction in various acquisition setups (2D and 3D imaging, Cartesian and non-Cartesian readout, parallel imaging, etc.) in the context of accelerated acquisitions using compressed sensing. This plugin is available on Pypi as pysap-mri 0.1.1. Test data are available in pysap-data.

Published
2020

14. Data transfer using MCM code

Author

Manishkumar, Shah Palash, primary, Agarawal, Deepesh J., additional, Tom, Ajin Jiji, additional, Chaithya, G. R., additional, and Varambally, Samhita, additional

Published
2016
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15. A Multi-Center Study on Human Brain Glutathione Conformation using Magnetic Resonance Spectroscopy.

Author

Shukla D, Mandal PK, Ersland L, Grüner ER, Tripathi M, Raghunathan P, Sharma A, Chaithya GR, Punjabi K, and Splaine C

Subjects
Aspartic Acid analogs & derivatives, Aspartic Acid metabolism, Brain diagnostic imaging, Female, Healthy Volunteers, Humans, Image Processing, Computer-Assisted, In Vitro Techniques, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Male, Protein Conformation, Tritium metabolism, Brain metabolism, Glutathione chemistry, Glutathione metabolism
Abstract

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) protocol and signal processing scheme. MEGA-PRESS pulse sequence was applied on healthy subjects using 3T Philips MRI scanner (India) and 3T GE MRI scanner (Norway) using the same experimental parameters (echo time, repetition time, and selective 180° refocusing ON-pulse at 4.40 ppm and 4.56 ppm). All MRS data were processed at one site National Brain Research Center (NBRC) using in-house MRS processing toolbox (KALPANA) for consistency. We have found that both the closed and extended GSH conformations are present in human brain and the relative proportion of individual conformer peak depends on the specific selection of refocusing ON-pulse position in MEGA-PRESS pulse sequence. It is important to emphasize that in vivo experiments with different refocusing and inversion pulse positions, echo time, and voxel size, clearly evidence the presence of both the GSH conformations. The GSH conformer peak positions for the closed GSH (Cys-Hβ) peak at ∼2.80 ppm and extended GSH (Cys-Hβ) peak at ∼2.95 ppm remain consistent irrespective of the selective refocusing OFF-pulse positions. This is the first in vivo study where both extended and closed GSH conformers are detected using the MEGA-PRESS sequence employing the parameters derived from the high resolution in vitro NMR studies on GSH.

Published
2018
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