Personalized synthetic MR imaging with deep learning enhancements.

ABSTRACT:

Purpose

Personalized synthetic MRI (syn-MRI) uses MR images of an individual subject acquired at a few design parameters (echo time, repetition time, flip angle) to obtain underlying parametric (ρ,T1,T2)$$ \left(\rho, {\mathrm{T}}_1,{\mathrm{T}}_2\right) $$ maps, from where MR images of that individual at other design parameter settings are synthesized. However, classical methods that use least-squares (LS) or maximum likelihood estimators (MLE) are unsatisfactory at higher noise levels because the underlying inverse problem is ill-posed. This article provides a pipeline to enhance the synthesis of such images in three-dimensional (3D) using a deep learning (DL) neural network architecture for spatial regularization in a personalized setting where having more than a few training im

SUBMITTER: Pal S 

PROVIDER: S-EPMC10100029 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature