SeqGPT: Training a Large Language Model to Generate MRI Pulse Sequences

Motivation: LLMs possess remarkable ability to process and generate sequential data which can be used to generate MRI pulse sequences. Goal(s): Train a custom implementation of Generative Pre-trained Transformer (GPT) to generate MRI pulse sequences. Approach: Pseudo random version of sequences like FLASH were generated to create training set which were then tokenized through a custom tokenizer with reduced vocabulary size. These tokenized sequences were used to train a custom implementation of GPT. Results: GPT was able to learn the sequence structure and generate out-of-data pseudo random sequences that can be simulated to get a reconstruction. Impact: This shows the capability of LLMs to generate MRI sequences. Which then can be fine-tuned with a differentiable simulator to adjust the sequence towards desired imaging objectives.