Real-Time Deep-Learning Image Reconstruction and Instrument Tracking in MR-Guided Biopsies.

Abstract

Background: Transrectal in-bore MR-guided biopsy (MRGB) is accurate but time-consuming, limiting clinical throughput. Faster imaging could improve workflow and enable real-time instrument tracking. Existing acceleration methods often use simulated data and lack validation in clinical settings.

Purpose: To accelerate MRGB by using deep learning for undersampled image reconstruction and instrument tracking, trained on multi-slice MR DICOM images and evaluated on raw k-space acquisitions.

Study type: Prospective feasibility study.

Population: Briefly, 1289 male patients (aged 44-87, median age 68) for model training, 8 male patients (aged 59-78, median age 65) for prospective feasibility testing.

Field strength/sequence: 2D Cartesian balanced steady-state free precession, 3 T.

Assessment: Segmentation and reconstruction models were trained on 8464 MRGB confirmation scans containing a biopsy needle guide instrument and evaluated on 10 prospectively acquired dynamic k-space samples. Needle guide tracking accuracy was assessed using instrument tip prediction (ITP) error, computed per frame as the Euclidean distance from reference positions defined via pre- and post-movement scans. Feasibility was measured by the proportion of frames with < 5 mm error. Additional experiments tested model robustness under increasing undersampling rates.

Statistical tests: In a segmentation validation experiment, a one-sample t-test tested if the mean ITP error was below 5 mm. Statistical significance was defined as p < 0.05. In the tracking experiments, the mean, standard deviation, and Wilson 95% CI of the ITP success rate were computed per sample, across undersampling levels.

Results: ITP was first evaluated independently on 201 fully sampled scans, yielding an ITP error of 1.55 ± 1.01 mm (95% CI: 1.41-1.69). Tracking performance was assessed across increasing undersampling factors, achieving high ITP success rates from 97.5% ± 5.8% (68.8%-99.9%) at 8× up to 92.5% ± 10.3% (62.5%-98.9%) at 16× undersampling. Performance declined at 18×, dropping to 74.6% ± 33.6% (43.8%-91.7%).

Data conclusion: Results confirm stable needle guide tip prediction accuracy and support the robustness of the reconstruction model for tracking at high undersampling.

Evidence level: 2.

Technical efficacy: Stage 2.