223 citations · 235 across the 11 of their papers we have counts for
13 papers
Vendor-Agnostic Joint Relaxometry and Myelin Water Fraction Mapping with B1 and Motion Correction
Unay Dorken Gallastegi, Shohei Fujita, Yohan Jun +19
Obtaining consistent quantitative maps of myelin content and relaxation times across different sites and vendors is essential for advancing our understanding of brain development.…
Is the medical image segmentation problem solved? A survey of current developments and future directions
Guoping Xu, Jayaram K. Udupa, Jax Luo +8
Medical image segmentation has advanced rapidly over the past two decades, largely driven by deep learning, which has enabled accurate and efficient delineation of cells, tissues,…
Multishot Dual Polarity GRAPPA: Robust Nyquist Ghost Correction for multishot EPI
Yuancheng Jiang, Yohan Jun, Qiang Liu +4
Purpose: This work aims to develop a robust Nyquist ghost correction method for multishot echo-planar imaging (EPI). The method helps correct challenging Nyquist ghosts, particular…
Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation
Jun Lyu, Lipeng Ning, William Consagra +4
High-resolution whole-brain in vivo MR imaging at mesoscale resolutions remains challenging due to long scan durations, motion artifacts, and limited signal-to-noise ratio (SNR). T…
PRIME: Phase Reversed Interleaved Multi-Echo acquisition enables highly accelerated distortion-free diffusion MRI
Yohan Jun, Qiang Liu, Ting Gong +13
Purpose: To develop and evaluate a new pulse sequence for highly accelerated distortion-free diffusion MRI (dMRI) by inserting additional echoes without prolonging TR, when general…
A Deep Learning Approach to Multi-Fiber Parameter Estimation and Uncertainty Quantification in Diffusion MRI
William Consagra, Lipeng Ning, Yogesh Rathi
Diffusion MRI (dMRI) is the primary imaging modality used to study brain microstructure in vivo. Reliable and computationally efficient parameter inference for common dMRI biophysi…
Neural Orientation Distribution Fields for Estimation and Uncertainty Quantification in Diffusion MRI
William Consagra, Lipeng Ning, Yogesh Rathi
Inferring brain connectivity and structure \textit{in-vivo} requires accurate estimation of the orientation distribution function (ODF), which encodes key local tissue properties.…
SlicerTMS: Real-Time Visualization of Transcranial Magnetic Stimulation for Mental Health Treatment
Loraine Franke, Tae Young Park, Jie Luo +4
We present a real-time visualization system for Transcranial Magnetic Stimulation (TMS), a non-invasive neuromodulation technique for treating various brain disorders and mental he…
SNR-enhanced diffusion MRI with structure-preserving low-rank denoising in reproducing kernel Hilbert spaces
Gabriel Ramos-Llordén, Gonzalo Vegas-Sánchez-Ferrero, Congyu Liao +3
Purpose: To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages non-linear redundancy in the data to boost the SNR while preserving signal…
High-fidelity, accelerated whole-brain submillimeter in-vivo diffusion MRI using gSlider-Spherical Ridgelets (gSlider-SR)
Gabriel Ramos-Llordén, Lipeng Ning, Congyu Liao +4
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 scann…
The White Matter Query Language: A Novel Approach for Describing Human White Matter Anatomy
Demian Wassermann, Makris Nikos, Yogesh Rathi +4
We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tr…
Spatially regularized reconstruction of fibre orientation distributions in the presence of isotropic diffusion
Q. Zhou, O. Michailovich, Y. Rathi
The connectivity and structural integrity of the white matter of the brain is nowadays known to be implicated into a wide range of brain-related disorders. However, it was not befo…
Spatially regularized compressed sensing of diffusion MRI data
Oleg Michailovich, Yogesh Rathi, Sudipto Dolui
The present paper introduces a method for substantial reduction of the number of diffusion encoding gradients required for reliable reconstruction of HARDI signals. The method expl…