Choosing the Right Technique for the Right Restriction – A Domain-Specific Approach for Enforcing Search-Space Restrictions in Evolutionary Algorithms

Plump, Christina; Berger, Bernhard J.; Drechsler, Rolf

doi:10.1007/978-3-031-05359-7_28

Cited by 2 publications

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“…The rest is taken care of via constraints. In Plump et al ( 2022a ), a technique is presented to equip constraints with different levels of strictness. In this work, we adopt this technique for the acquisition metrics.…”

Section: Methodsmentioning

confidence: 99%

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

Hoinkiss

Huber

Plump

et al. 2023

Front. Neuroimaging

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IntroductionThe 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.MethodsWe 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 that are formulated by the DSL and export them using the generic file format introduced by the Pulseq framework, making it possible to simulate phantom data using the open-source MR simulation framework JEMRIS to build a training database that relates input MRI sequences to output sets of metrics. Utilizing ML techniques, we learn this correspondence to allow efficient optimization of MRI sequences meeting the clinical demands formulated as a starting point.ResultsML methods are capable of capturing the relation of input and simulated output parameters. Evolutionary algorithms show promising results in finding optimal MRI sequences with regards to the training data. Simulated and acquired MRI data show high correspondence to the initial set of requirements.DiscussionThis work has the potential to offer optimal solutions for different clinical scenarios, potentially reducing exam times by preventing suboptimal MRI protocol settings. Future work needs to cover additional DSL layers of higher flexibility as well as an optimization of the underlying MRI simulation process together with an extension of the optimization method.

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Section: Methodsmentioning

confidence: 99%