Kavli Affiliate: Jeremias Sulam
| Authors: Aaron T. Gudmundson, Zahra Shams, Abdelrahman Gad, Shuyuan Wang, Dunja Simicic, Saipavitra Murali-Manohar, Gizeaddis Lamesgin Simegn, Ipek Özdemir, Christopher W. Davies-Jenkins, Yulu Song, Vivek Yedavalli, Georg Oeltzschner, Omer Burak Demirel, Jeremias Sulam, Michael schär, Sandeep Ganji and Richard A. E. Edden
| Summary:
This work presents a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme.
Per Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), allows for deployment of deep learning models and pulse sequence updates. In this study, PEREGRINE operated a time-domain (TD) and frequency-domain (FD) convolutional autoencoder that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the medial prefrontal cortex of healthy volunteers using a MEGA-edited MRS experiment. The degree of OOV contamination (OOV Score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV Scores above a user-defined threshold triggered an update of the crusher gradient scheme, iterating through 48 permutations (6 axis transpositions × 8 polarity flips).
Within each 2-second TR, PEREGRINE successfully provided single-transient OOV Scores and updated gradients accordingly. No difference was observed between the OOV Scores from the full (“Full” condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV Scores than the AI-off scan when selecting the transients where PEREGRINE persisted (“Dwell” condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN) from linear combination modeling improved significantly for the AI-on compared to the AI-off scan.
PEREGRINE enabled an adaptive and AI-integrated sequence allowing for real-time evaluation and response to OOV artifacts, identifying gradient modifications that produced less OOV contamination.