AI-driven and automated MRI sequence optimization in scanner-independent MRI sequences formulated by a domain-specific language.

ABSTRACT:

Introduction

The complexity of Magnetic Resonance Imaging (MRI) sequences requires expert knowledge about the underlying contrast mechanisms to select from the wide range of available applications and protocols. Automation of this process using machine learning (ML) can support the radiologists and MR technicians by complementing their experience and finding the optimal MRI sequence and protocol for certain applications.

Methods

We define domain-specific languages (DSL) both for describing MRI sequences and for formulating clinical demands for sequence optimization. By using various abstraction levels, we allow different key users exact definitions of MRI sequences and make them more accessible to ML. We use a vendor-independent MRI framework (gammaSTAR) to build sequences t

SUBMITTER: Hoinkiss DC 

PROVIDER: S-EPMC10406289 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature