arXiv subjects

Mahesh Bharath Keerthivasan

Publications and source records attributed to Mahesh Bharath Keerthivasan.

5 recordsLinked to original sources

arXiv2026

Distortion-Free High-Resolution PROPELLER-DWI: Technical Advances and Initial Demonstration for the Prostate

Purpose: To develop and evaluate an integrated acquisition and reconstruction framework for distortion-free, high-resolution prostate diffusion-weighted imaging (DWI). Methods: PROPELLER-DWI combines pulsed-gradient spin-echo diffusion preparation, an interleaved gradient-and-spin-echo PROPELLER acquisition with golden-angle blade rotation, and self-supervised joint reconstruction and denoising. It was compared with conventional NUFFT reconstruction and MPPCA denoising in a NIST/QIBA diffusion phantom, 25 volunteers, and 4 clinical patients undergoing 3T prostate MRI across three spatial-resolution settings and two b-values (50 and 1000 s/mm2). Reader studies compared geometric fidelity, image quality, and overall preference with clinical single-shot EPI-DWI. Results: PROPELLER-DWI preserved prostate geometry in cases where EPI-DWI showed susceptibility-induced distortion, signal loss, and metal-related artifacts. Self-supervised reconstruction improved noise suppression, with the greatest benefit at higher spatial resolution. Across all resolutions, PROPELLER-DWI had significantly higher geometric fidelity than EPI-DWI, and radiologist preference increased with the severity of EPI distortion. ADC measurements remained consistent across resolutions and comparable to clinical EPI-DWI, whereas EPI-DWI ADC varied with increasing resolution. Retrospective reduced-shot reconstruction also supported shorter scan times. Conclusion: PROPELLER-DWI enables distortion-free, high-resolution prostate DWI while preserving quantitative diffusion measurements, providing a practical alternative for technically challenging prostate MRI and applications requiring improved geometric fidelity.

eess.IV↗

arXiv2023

Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI

Random matrix theory (RMT) combined with principal component analysis has resulted in a widely used MPPCA noise mapping and denoising algorithm, that utilizes the redundancy in multiple acquisitions and in local image patches. RMT-based denoising relies on the uncorrelated identically distributed noise. This assumption breaks down after regridding of non-Cartesian sampling. Here we propose a Universal Sampling Denoising (USD) pipeline to homogenize the noise level and decorrelate the noise in non-Cartesian sampled k-space data after resampling to a Cartesian grid. In this way, the RMT approaches become applicable to MRI of any non-Cartesian k-space sampling. We demonstrate the denoising pipeline on MRI data acquired using radial trajectories, including diffusion MRI of a numerical phantom and ex vivo mouse brains, as well as in vivo $T_2$ MRI of a healthy subject. The proposed pipeline robustly estimates noise level, performs noise removal, and corrects bias in parametric maps, such as diffusivity and kurtosis metrics, and $T_2$ relaxation time. USD stabilizes the variance, decorrelates the noise, and thereby enables the application of RMT-based denoising approaches to MR images reconstructed from any non-Cartesian data. In addition to MRI, USD may also apply to other medical imaging techniques involving non-Cartesian acquisition, such as PET, CT, and SPECT.

physics.med-ph↗

arXiv2020

A Contrast Synthesized Thalamic Nuclei Segmentation Scheme using Convolutional Neural Networks

Thalamic nuclei have been implicated in several neurological diseases. WMn-MPRAGE images have been shown to provide better intra-thalamic nuclear contrast compared to conventional MPRAGE images but the additional acquisition results in increased examination times. In this work, we investigated 3D Convolutional Neural Network (CNN) based techniques for thalamic nuclei parcellation from conventional MPRAGE images. Two 3D CNNs were developed and compared for thalamic nuclei parcellation using MPRAGE images: a) a native contrast segmentation (NCS) and b) a synthesized contrast segmentation (SCS) using WMn-MPRAGE images synthesized from MPRAGE images. We trained the two segmentation frameworks using MPRAGE images (n=35) and thalamic nuclei labels generated on WMn-MPRAGE images using a multi-atlas based parcellation technique. The segmentation accuracy and clinical utility were evaluated on a cohort comprising of healthy subjects and patients with alcohol use disorder (AUD) (n=45). The SCS network yielded higher Dice scores in the Medial geniculate nucleus (P=.003) and Centromedian nucleus (P=.01) with lower volume differences for Ventral anterior (P=.001) and Ventral posterior lateral (P=.01) nuclei when compared to the NCS network. A Bland-Altman analysis revealed tighter limits of agreement with lower coefficient of variation between true volumes and those predicted by the SCS network. The SCS network demonstrated a significant atrophy in Ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P=0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS network showed spurious atrophy of the Ventral posterior lateral nucleus. CNN-based contrast synthesis prior to segmentation can provide fast and accurate thalamic nuclei segmentation from conventional MPRAGE images.

cs.LG↗

arXiv2020

A Comparison of Deep Learning Convolution Neural Networks for Liver Segmentation in Radial Turbo Spin Echo Images

Motion-robust 2D Radial Turbo Spin Echo (RADTSE) pulse sequence can provide a high-resolution composite image, T2-weighted images at multiple echo times (TEs), and a quantitative T2 map, all from a single k-space acquisition. In this work, we use a deep-learning convolutional neural network (CNN) for the segmentation of liver in abdominal RADTSE images. A modified UNET architecture with generalized dice loss objective function was implemented. Three 2D CNNs were trained, one for each image type obtained from the RADTSE sequence. On evaluating the performance of the CNNs on the validation set, we found that CNNs trained on TE images or the T2 maps had higher average dice scores than the composite images. This, in turn, implies that the information regarding T2 variation in tissues aids in improving the segmentation performance.

eess.IV↗

arXiv2019

T2-weighted Spine Imaging using a Single-Shot Turbo Spin Echo Pulse Sequence

T2 weighted imaging of the spine is commonly performed using fast spin echo (FSE/TSE) based sequences, resulting in long scan times and vulnerability to motion artifacts. While single shot fast spin echo sequences have been attempted, their adoption has been limited by poor spatial resolution and specific absorption rate (SAR) limitations. We investigate the use of a half-Fourier acquisition single-shot turbo spin-echo variable flip angle (HASTE-VFA) sequence for T2 weighted spine imaging. A variable refocusing flip angle echo train was first optimized for the spine to improve the point spread function (PSF) and minimize SAR, yielding images with improved spatial resolution and signal-to-noise ratio compared to the constant flip angle sequence. Data was acquired from 29 patients (20 lumbar and thoracolumbar, 9 whole-spine) using conventional fast spin echo and the proposed variable flip angle single-shot sequences. All images were graded by two experienced neuroradiologists in a blinded fashion and scores were assigned based on blurring, motion, artifacts, and noise as well as appearance of disks, facet joints, end plates, nerve roots and the spinal cord.

physics.med-ph↗