Keywords: Analysis/Processing, Breast, MR-Guided Breast Cancer Biopsy, Deep Learning, Real-time tracking
Motivation: MR-guided biopsies are critical for breast cancer diagnosis, but accurate representation of lesions is challenging, often requiring follow-up imaging that increases patient burden and delays diagnosis.
Goal(s): This study aims to develop a real-time tracking system by identifying lesions, position-marker, needle-tips during breast MR-guided biopsies.
Approach: We employed a Template-Matching-Network, trained on 235 patients’ breast-MRI scans to predict in real-time lesion location, position-marker, and needle-tip during MR-guided breast biopsy.
Results: Our model demonstrated high localization accuracy within images, but requires further adjustment cross-images. Results indicate potential to streamline biopsies and minimize follow-up imaging. Future research will focus on assessing adequacy for breast-MRI biopsy.
Impact: This study introduces a real-time MR-guided breast biopsies tracking system, enhancing localization accuracy of lesions, position-markers, and needle-tips. This approach aims to improve patient outcomes, optimize breast cancer diagnostic confidence, and reduce the need for follow-up examinations.
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