Accelerated cloud and GPU-based simulations for quantification of relaxation times: an example with MOLLI

Georgios Kantasis,Christos G Xanthis,Anthony H Aletras

doi:10.1186/1532-429x-18-s1-p42

Abstract

Accelerated cloud and GPU-based simulations for quantification of relaxation times: an example with MOLLI

Highlights

Quantification of native T1 in the myocardium has the potential to become an important biomarker for assessing specific cardiomyopathies
This method is based on comprehensive, GPUbased MR simulations of the identical pulse sequence on a large population of spins resulting in a database of all possible outcomes
Each node performed the simulation of the entire pulse sequence on a subset of the spin population and one of them was assigned the role of job manager

Summary

Introduction

Quantification of native T1 in the myocardium has the potential to become an important biomarker for assessing specific cardiomyopathies. The accuracy and precision of the clinically available CMR mapping techniques are affected by several parameters [1]. A recently developed method that allows for accuracy improvement of MOLLI quantification was presented [2,3]. This method is based on comprehensive, GPUbased MR simulations of the identical pulse sequence on a large population of spins resulting in a database of all possible outcomes. The aim of this study was to improve performance to more clinically acceptable levels. We hypothesized that this could be accomplished by a cloud and GPU-based implementation approach

Objectives

Methods

Conclusion