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GPT4MR: Exploring GPT-4 as an MR Sequence and Reconstruction Programming Assistant

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Daniel Cremers

Prof. Dr.

Director

Computer Vision & Artificial Intelligence

Abstract

In this study, we explore the potential of generative pre-trained transformer (GPT), as a coding assistant for MRI sequence programming using the Pulseq framework. The programming of MRI sequences is traditionally a complex and time-consuming task, and the Pulseq standard has recently simplified this process. It allows researchers to define and generate complex pulse sequences used in MRI experiments. Leveraging GPT-4’s capabilities in natural language generation, we adapted it for MRI sequence programming, creating a specialized assistant named GPT4MR. Our tests involved generating various MRI sequences, revealing that GPT-4, guided by a tailored prompt, outperformed GPT-3.5, producing fewer errors and demonstrating improved reasoning. Despite limitations in handling complex sequences, GPT4MR corrected its own errors and successfully generated code with step-by-step instructions. The study showcases GPT4MR’s ability to accelerate MRI sequence development, even for novel ideas absent in its training set. While further research and improvement are needed to address complexity limitations, a well-designed prompt enhances performance. The findings propose GPT4MR as a valuable MRI sequence programming assistant, streamlining prototyping and development. The future prospect involves integrating a PyPulseq plugin into lightweight, open-source LLMs, potentially revolutionizing MRI sequence development and prototyping.

inproceedings

ESMRMB 2023

39th Annual Meeting of the European Society for Magnetic Resonance in Medicine and Biology. Basel, Switzerland, Oct 04-07, 2023.

Authors

M. Zaiss • H. N. Dang • V. Golkov • J. R. Rajput • D. Cremers • F. Knoll • A. Maier

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Research Area

 B1 | Computer Vision

BibTeXKey: ZDG+23

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