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Background Quantification of native T1 in the myocardium has the potential to become an important biomarker for assessing specific cardiomyopathies. However, 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. MR simulations are computationally intensive with long execution times not well suited for clinical applications. 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.

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