Gabriel Ramos-Llordén1, Lipeng Ning1, Congyu Liao2, Rinat Mukhometzianov1,3, Oleg Michailovich3, Kawin Setsompop2, Yogesh Rathi1. 1. Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 2. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 3. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
Abstract
PURPOSE: To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in vivo scan on a clinical scanner. METHODS: We extend the ultra-high resolution diffusion MRI acquisition technique, gSlider, by allowing undersampling in q-space and radiofrequency (RF)-encoding space, thereby dramatically reducing the total acquisition time of conventional gSlider. The novel method, termed gSlider-SR, compensates for the lack of acquired information by exploiting redundancy in the dMRI data using a basis of spherical ridgelets (SR), while simultaneously enhancing the signal-to-noise ratio. Using Monte Carlo simulation with realistic noise levels and several acquisitions of in vivo human brain dMRI data (acquired on a Siemens Prisma 3T scanner), we demonstrate the efficacy of our method using several quantitative metrics. RESULTS: For high-resolution dMRI data with realistic noise levels (synthetically added), we show that gSlider-SR can reconstruct high-quality dMRI data at different acceleration factors preserving both signal and angular information. With in vivo data, we demonstrate that gSlider-SR can accurately reconstruct 860 μm diffusion MRI data (64 diffusion directions at b = 2000 s / mm 2 ), at comparable quality as that obtained with conventional gSlider with four averages, thereby providing an eight-fold reduction in scan time (from 1 hour 20 to 10 minutes). CONCLUSIONS: gSlider-SR enables whole-brain high angular resolution dMRI at a submillimeter spatial resolution with a dramatically reduced acquisition time, making it feasible to use the proposed scheme on existing clinical scanners.
PURPOSE: To develop an accelerated, robust, and accurate diffusion MRI acquisition and reconstruction technique for submillimeter whole human brain in vivo scan on a clinical scanner. METHODS: We extend the ultra-high resolution diffusion MRI acquisition technique, gSlider, by allowing undersampling in q-space and radiofrequency (RF)-encoding space, thereby dramatically reducing the total acquisition time of conventional gSlider. The novel method, termed gSlider-SR, compensates for the lack of acquired information by exploiting redundancy in the dMRI data using a basis of spherical ridgelets (SR), while simultaneously enhancing the signal-to-noise ratio. Using Monte Carlo simulation with realistic noise levels and several acquisitions of in vivo human brain dMRI data (acquired on a Siemens Prisma 3T scanner), we demonstrate the efficacy of our method using several quantitative metrics. RESULTS: For high-resolution dMRI data with realistic noise levels (synthetically added), we show that gSlider-SR can reconstruct high-quality dMRI data at different acceleration factors preserving both signal and angular information. With in vivo data, we demonstrate that gSlider-SR can accurately reconstruct 860 μm diffusion MRI data (64 diffusion directions at b = 2000 s / mm 2 ), at comparable quality as that obtained with conventional gSlider with four averages, thereby providing an eight-fold reduction in scan time (from 1 hour 20 to 10 minutes). CONCLUSIONS: gSlider-SR enables whole-brain high angular resolution dMRI at a submillimeter spatial resolution with a dramatically reduced acquisition time, making it feasible to use the proposed scheme on existing clinical scanners.
Authors: Gwendolyn Van Steenkiste; Ben Jeurissen; Jelle Veraart; Arnold J den Dekker; Paul M Parizel; Dirk H J Poot; Jan Sijbers Journal: Magn Reson Med Date: 2015-01-22 Impact factor: 4.668
Authors: Lipeng Ning; Frederik Laun; Yaniv Gur; Edward V R DiBella; Samuel Deslauriers-Gauthier; Thinhinane Megherbi; Aurobrata Ghosh; Mauro Zucchelli; Gloria Menegaz; Rutger Fick; Samuel St-Jean; Michael Paquette; Ramon Aranda; Maxime Descoteaux; Rachid Deriche; Lauren O'Donnell; Yogesh Rathi Journal: Med Image Anal Date: 2015-11-10 Impact factor: 8.545
Authors: José V Manjón; Pierrick Coupé; Luis Concha; Antonio Buades; D Louis Collins; Montserrat Robles Journal: PLoS One Date: 2013-09-03 Impact factor: 3.240
Authors: Susie Y Huang; Thomas Witzel; Boris Keil; Alina Scholz; Mathias Davids; Peter Dietz; Elmar Rummert; Rebecca Ramb; John E Kirsch; Anastasia Yendiki; Qiuyun Fan; Qiyuan Tian; Gabriel Ramos-Llordén; Hong-Hsi Lee; Aapo Nummenmaa; Berkin Bilgic; Kawin Setsompop; Fuyixue Wang; Alexandru V Avram; Michal Komlosh; Dan Benjamini; Kulam Najmudeen Magdoom; Sudhir Pathak; Walter Schneider; Dmitry S Novikov; Els Fieremans; Slimane Tounekti; Choukri Mekkaoui; Jean Augustinack; Daniel Berger; Alexander Shapson-Coe; Jeff Lichtman; Peter J Basser; Lawrence L Wald; Bruce R Rosen Journal: Neuroimage Date: 2021-08-28 Impact factor: 7.400
Authors: J L Waugh; Aao Hassan; J K Kuster; J M Levenstein; S K Warfield; N Makris; N Brüggemann; N Sharma; H C Breiter; A J Blood Journal: Neuroimage Date: 2021-11-18 Impact factor: 7.400