QT Imaging Holdings, Inc. (NASDAQ:QTI) (“QTI”) a medical device company engaged in research, development, and commercialization of innovative body imaging systems is pleased to announce the release of a machine learning-enabled image interpolation algorithm designed to significantly reduce scan times by approximately 50%. This advancement is expected to enhance patient comfort by decreasing time spent on the scanner and improve operational efficiency by increasing the number of patients scanned within a given timeframe.
The advanced machine learning algorithm enables sophisticated image interpolation, allowing the system to skip every other scan level while accurately preserving high image quality. This technological breakthrough reduces overall scan time by nearly half without sacrificing diagnostic accuracy, showcasing the ability of QTI’s technology to streamline imaging processes, optimize diagnostic workflows, and deliver superior patient outcomes, reinforcing its position at the forefront of medical imaging innovation. The results of this groundbreaking development will be presented for the first time at the SPIE Medical Imaging 2025 conference in San Diego, California.
“Our commitment to advancing QTI’s technology and revolutionizing medical imaging is exemplified by this development,” said Dr. Bilal Malik, Chief Science Officer at QTI. “By integrating machine learning into our imaging systems, we ensure its application is to create meaningful improvements in patient experience by reducing discomfort and scan times, while simultaneously streamlining workflows to enhance efficiency for healthcare providers ”
This innovation aligns with QTI’s strategy to improve health outcomes by making medical imaging safe, affordable, accessible, and centered on patient experience. The company continues to expand its clinical and research and development efforts, including collaborations with leading institutions such as the National Institutes of Health (NIH), Sunnybrook Health Sciences Centre, and the Stephenson Cancer Center at the University of Oklahoma, to advance precision in cancer detection and treatment.
