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QT Imaging Announces Presentation at SPIE Medical Imaging 2026
NOVATO, Calif.--(BUSINESS WIRE)-- QT Imaging Holdings, Inc. (NASDAQ: QTI) (“QT Imaging” or the “Company”), a medical device company dedicated to transforming
About this update from Qt Imaging Holdings, Inc.
[{"type":"text","content":" NOVATO, Calif.--(BUSINESS WIRE)--\nQT Imaging Holdings, Inc. (NASDAQ: QTI) (“QT Imaging” or the “Company”), a medical device company dedicated to transforming breast health management through innovative, radiation-free imaging technology, is pleased to announce that its Chief Science Officer, Bilal Malik, Ph.D., will present at the prestigious International Society for Optics & Photonics (“SPIE”) Medical Imaging meeting taking place in Vancouver, BC, Canada, from February 15–19, 2026.\n\n\n \nSPIE Medical Imaging 2026 is a premier international symposium focusing on the latest research in medical image processing, physics, and computer-aided diagnosis. The event, which is in its 50th year, covers advancements in AI, ultrasound, digital pathology, and image-guided procedures.\n\n\n \nIn his oral presentation, titled “Automated breast boundary extraction from reflection data in breast acoustic CT,” Dr. Malik will highlight an automated method to accurately define the breast boundary in QT Imaging Breast Acoustic CT™ scans, a critical step in image formation and analysis. The work reflects QT Imaging’s broader strategy to combine advanced physics-based imaging with modern artificial intelligence and machine learning (AI/ML) tools to enhance clinical utility.\n\n\n \n“Accurate breast boundary detection guides the nonlinear optimization for image reconstructions, determines the gain factor for reflection imaging, enables water-region denoising, supports calculation of breast density, and enhances the robustness of QT Imaging’s reconstruction pipeline,” said Dr. Malik.\n\n\n \n“We developed a multi-step algorithm that extracts the breast boundary from reflection data by detecting primary reflection events, refining them through filtering and interpolation, and compensating for missing data near the chest wall via extrapolation. This algorithm is fast, validated on a large dataset, and fully integrated into QT Imaging’s production software, underscoring the maturity of the technology.”\n\n\n \nThis work also serves as a foundation for QT Imaging’s planned AI/ML initiatives, including automated lesion segmentation and clinical decision support tools. By pairing precise boundary detection with advanced machine learning, the Company aims to improve lesion characterization, streamline clinical workflows, and provide clinicians with mo...