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Abstract #4837

Real-time AI image reconstruction and instrument tracking in MR-guided biopsies

Stan Noordman1, Harm H.A. Weekenstroo2, Marnix C. Maas2, Frank F.J. Simonis3, Jurgen J. Fütterer2, and Henkjan Huisman2
1Radboudumc, Arnhem, Netherlands, 2Radboudumc, Nijmegen, Netherlands, 3University of Twente, Enschede, Netherlands

Synopsis

Keywords: MR-Guided Interventions, Prostate

Motivation: Interventional MRI struggles with speed and efficiency.

Goal(s): To accelerate transrectal in-bore MR-guided biopsy procedures through undersampled image reconstruction and instrument tracking using a novel deep learning AI approach.

Approach: Image reconstruction and instrument segmentation models were trained using 8457 synthetically undersampled clinical MR-guided biopsy scans from 1289 patients. The models were tested on 5 prospectively undersampled MR-guided biopsy dynamic k-space datasets, evaluating the needle guide tip prediction error and the failure rate of needle guide prediction at increasing levels of undersampling.

Results: We found stable performance with up to 16x undersampling.

Impact: A deep-learning temporal model utilizing spatiotemporal information achieved up to 16x undersampling rates for MR-guided prostate biopsy scans while maintaining accurate instrument tip positioning. This could enable real-time instrument tracking in interventional tasks, improving efficiency and usability.

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Keywords