Preoperative MRI-Guided Freehand Ultrasound Volume Reconstruction.
Authors
Affiliations (5)
Affiliations (5)
- Department of Biomedical Engineering & Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York 12180, United States.
- Molecular Imaging Program, Center for Cancer Research, National Institutes of Health, Bethesda, Maryland 20892, United States.
- Department of Urology, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157, United States.
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892, United States.
- Radiology & Imaging Sciences, Center for Interventional Oncology, National Institutes of Health, Bethesda, Maryland 20892, United States.
Abstract
Transrectal ultrasound (TRUS) is widely used for guiding prostate biopsy due to its real-time imaging capabilities. However, ultrasound (US) lacks sensitivity for detecting prostate cancer, necessitating the integration of preoperative magnetic resonance imaging (MRI) to offer superior soft tissue contrast. To enable MRI-ultrasound fusion during interventions, an accurate 3D reconstruction of freehand TRUS is essential. Existing reconstruction methods typically rely on sequentially estimating interframe transformations, resulting in no explainability and accumulated errors and drift over time. In this paper, we present a framework that leverages preoperative MRI and supervised contrastive learning to reconstruct 3D ultrasound volumes directly from 2D frames. By aligning ultrasound images with corresponding MRI slices based on anatomical similarity, our method bypasses sequential estimation, avoids drift, and improves tracking accuracy. The approach was trained and validated on a large clinical data set of over 500 prostate biopsy cases and demonstrated over 50% improvement in drifting errors. By enhancing both precision and interpretability, our algorithm supports more reliable MRI-ultrasound fusion and holds the potential for improving the diagnostic accuracy of prostate cancer interventions.