MRzero ‐ Automated discovery of MRI sequences using supervised learning

Abstract

Magnetic resonance (MR) images can be created noninvasively using only static and dynamic magnetic fields, and radio frequency pulses. MR imaging provides fast image acquisitions which have been clinically feasible only since the discovery of efficient MR sequences, 1-3 ie, time-efficient application of two building blocks: radio frequency pulses and spatial magnetic

An efficient approach to optimal experimental design for magnetic resonance fingerprinting with B‐splines

“…However, the benefits of the CRB‐based experimental design are not tied to any specific reconstruction algorithm, since, the CRB, as an estimation‐theoretic bound, is independent of the reconstruction process. This is generally different from the emerging supervised learning based OED approaches, 22,49,50 which often incorporate a specific reconstruction algorithm into an objective function that performs empirical risk minimization. In practice, we observed that a variety of reconstruction algorithms, including both model‐based 51‐54 and data‐driven methods, 55,56 can benefit from the CRB‐based experimental design, given the SNR benefit such design provides.…”

Pulseq‐CEST: Towards multi‐site multi‐vendor compatibility and reproducibility of CEST experiments using an open‐source sequence standard

“…24 This pulseq-file is then read and played out on the scanner using a vendor-specific interpreter sequence. Although Pulseq is a great tool that enables a flexible implementation of complex sequence patterns, 25 it is complicated to incorporate vendorprovided image reconstruction functions, which are generally proprietary. However, having the source code of a full interpreter sequence at hand, a capsulated interpreter can be included into other existing sequences for imaging readout.…”

An end‐to‐end AI‐based framework for automated discovery of rapid CEST/MT MRI acquisition protocols and molecular parameter quantification (AutoCEST)

“…In the future, the number of raw images acquired (N) could be defined as a dynamically optimized parameter. In addition, while the saturation power was limited to not exceed a fixed value for each of the scenarios (Supporting Information Table S1), it could be replaced in the future by a specific absorption rate (SAR) penalty term, incorporated in the cost‐function 58 . Similarly, a penalty term for exceedingly long scan times could be used to further improve SNR/scan‐time balance.…”