MR-double-zero – Proof-of-concept for a framework to autonomously discover MRI contrasts

Abstract

PurposeA framework for supervised design of MR sequences for any given target contrast is proposed, based on fully automatic acquisition and reconstruction of MR data on a real MR scanner. The proposed method does not require any modeling of MR physics and thus allows even unknown contrast mechanisms to be addressed. MethodsA derivative-free optimization algorithm is set up to repeatedly update and execute a parametrized sequence on the MR scanner to acquire data. In each iteration, the acquired data are mapped to a given target contrast by linear regression. ResultsIt is shown that with the proposed framework it is possible to find an MR sequence that yields a predefined target contrast. In the present case, as a proof-of principle, a sequence mapping absolute creatine concentration, which cannot be extracted from T1 or T2-weighted scans directly, is discovered. The sequence was designed in a comparatively short time and with no human interaction. ConclusionsNew MR contrasts for mapping a given target can be discovered by derivative-free optimization of parametrized sequences that are directly executed on a real MRI scanner. This is demonstrated by ‘re-discovery’ of a chemical exchange weighted sequence. The proposed method is considered to be a paradigm shift towards autonomous, model-free and target-driven sequence design.