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Deep learning-based temporal MR image reconstruction for accelerated interventional imaging during in-bore biopsies.

Constant R Noordman¹, Steffan J W Borgers¹, Martijn F Boomsma²

¹Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.

Journal of Medical Imaging (Bellingham, Wash.)

|June 5, 2025

Summary

Deep learning accelerates MR-guided prostate biopsies by enabling 16x faster imaging. This improves instrument tracking accuracy and image quality, making the procedure more efficient and effective for cancer diagnosis.

Keywords:

artificial intelligence deep learning image processing (computer-assisted)interventional radiology magnetic resonance imaging neural networks (computer)

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Interventional Magnetic Resonance (MR) imaging faces challenges in speed and efficiency.
Accurate instrument localization is crucial for MR-guided biopsies.

Purpose of the Study:

To accelerate transrectal in-bore MR-guided biopsies for prostate cancer.
To improve image reconstruction and instrument localization using deep learning.

Main Methods:

A deep learning-based spatiotemporal MR image reconstruction model and a nnU-Net segmentation model were trained and tested.
Data from 1289 patients undergoing prostate biopsy were used, with synthetic undersampling up to R=32.
A reader study compared the model's performance against a nontemporal model and radiologists.

Main Results:

The temporal model achieved a maximum noninferior undersampling rate of 16x with minimal instrument tip position (ITP) error (2.28 mm).
The temporal model demonstrated a 95% instrument prediction success rate, significantly outperforming the nontemporal model (46%) and readers (60%).
Nontemporal models failed to produce noninferior image reconstructions compared to the reference standard.

Conclusions:

Deep learning-based spatiotemporal MR image reconstruction significantly enhances time-critical interventions like instrument tracking.
A 16x undersampling rate represents the optimal balance for preserving image quality, minimizing ITP error, and maximizing instrument prediction success.