Janice embarked on her academic journey at National Taiwan University, where she earned her Bachelor of Science in Engineering with a concentration in biomedical engineering in 2024.
Throughout her time at university, Janice developed a keen interest in biomedical engineering research, particularly focusing on image processing and the practical applications of artificial intelligence in medical imaging. She took on a summer internship at Washington University in St. Louis, where she contributed to a project developing a new method for improving SPECT imaging without the need for CT scans.
Now admitted to the McKelvey School of Engineering and the McDonnell Scholars Academy, Janice hopes to fulfill her research goals that may someday contribute to the healthcare of her country, Indonesia, and other parts of the world that need it.
Outside of her studies and research, Janice enjoys exploring new places and capturing moments through photography. Her experiences studying abroad in Sweden and her love for travel have shaped her perspective and fueled her curiosity about the world.
Scholar Voices
Advancing Cardiac Imaging: Janice Tania Presents Research at SNMMI and NIH Workshops
By Janice Tania | September 2025
Janice Tania recently presented cardiac SPECT research at The Society of Nuclear Medicine and Molecular Imaging (SNMMI) Annual Conference in New Orleans held from June 21-24 and the National Institutes of Health (NIH) Emerging Technologies for Coronary Artery Disease Imaging Workshop in Washington, DC. held from September 17-18.
At the SNMMI conference, Janice gave three oral presentations. In the Generative AI session, she presented an LLM-based framework for extracting population-based statistics of myocardial perfusion defect properties from MPI SPECT clinical reports to strengthen the clinical realism of virtual imaging trials and task-specific evaluation methods. In the Image Generation session, she shared findings from a multi-reader multi-case human observer study evaluating CTLESS, a scatter projection and deep learning–based attenuation compensation method. In the AI Showcase session, she introduced CTLESS to a broader audience, highlighting its motivation, evaluation, and potential for thoughtful integration into clinical practice. At the NIH CAD Workshop, Janice delivered a poster presentation highlighting the lab’s comprehensive work to advance myocardial perfusion SPECT imaging, including the development of personalized acquisition protocols, methods to reduce radiation dose and acquisition time, and the physics-informed and deep learning-based method for accessible attenuation compensation.