A former Ph. D. graduate of the Biomedical Imaging Laboratory (BIG) from the Faculty of Science and Technology at the University of Macau, Jingzhang Sun, received the best technical paper award of the Journal of Nuclear Cardiology (JNC) at the 29^th Annual Meeting of the American Society of Nuclear Cardiology (ASNC), held in Austin, Texas, US. ASNC, the world’s largest and most specialized academic association in nuclear cardiology, selects one exceptional technical paper published in JNC from hundreds of submissions worldwide each year.

The awarded study, entitled “Deep Learning-Based Denoising in Projection-Domain and Reconstruction-Domain for Low-Dose Myocardial Perfusion SPECT”, was led by Prof. Greta Mok from FST, collaborated with Taipei Veterans General Hospital and Yale University. This work demonstrates that using deep learning techniques in denoising in the projection domain (raw data) is superior to the denoising in the reconstruction domain (processed data) for myocardial perfusion single photon emission computed tomography (SPECT), a well-established method for diagnosing coronary artery disease yet with radiation concern. This study demonstrates the state-of-the-art deep learning-based denoising performance for SPECT, which can be translated to reduced ionizing radiation to patients and medical staff, as well as shortened scan time to increase patient throughput and enhanced patients’ comfort.

BIG is one of the world-leading teams in applying deep learning techniques in SPECT, with strong emphasis on clinical translation. This study was supported by the first Excellent Young Scientists Fund (Hong Kong and Macau) from the National Natural Science Foundation of China (81922080).