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

Introduction: The 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.

Methods: We define domain-specific languages (DSL) both for describing MRI sequences and for formulating clinical demands for sequence optimization.

Analyzing SystemC Designs: SystemC Analysis Approaches for Varying Applications.

The complexity of hardware designs is still increasing according to Moore's law. With embedded systems being more and more intertwined and working together not only with each other, but also with their environments as cyber physical systems (CPSs), more streamlined development workflows are employed to handle the increasing complexity during a system's design phase. SystemC is a C++ library for the design of hardware/software systems, enabling the designer to quickly prototype, e.