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Chi Liu received his Ph.D. in 2008 from Johns Hopkins University with emphasis on quantitative SPECT/CT imaging. Following his graduate work, he was a postdoctoral fellow at University of Washington, specializing in oncological PET/CT studies with emphasis on compensation algorithms for respiratory motion. In 2010, he joined Yale University as a faculty member. He is board certified in Nuclear Medicine physics and instrumentation by the American Board of Science in Nuclear Medicine. His current research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging, including reconstruction algorithms, motion correction, dynamic imaging, and translational imaging. The translational and clinical applications of these projects include early detection of chemotherapy-induced cardiotoxicity, multimodality imaging of heart failure, and eliminating respiratory motion variability for assessing response to therapy. Many of the imaging technologies developed in his lab has been or is being implemented in clinical PET and SPECT scanners. In 2012, he was awarded with the Bruce Hasegawa Young Investigator Medical Imaging Science Award from the IEEE Nuclear Medical and Imaging Sciences Council for "contributions to the imaging physics of SPECT/CT and PET/CT, with emphasis in quantitative imaging and motion correction."
Radiation dose reduction methods in PET/SPECT/CT
Machine learning and deep learning in imaging applications
Motion correction methods for PET/CT and SPECT/CT
Early detection of chemotherapy-induced cardiotoxicity through advanced cardiac imaging
Chi Liu received his Ph.D. in 2008 from Johns Hopkins University with emphasis on quantitative SPECT/CT imaging. Following his graduate work, he was a postdoctoral fellow at University of Washington, specializing in oncological PET/CT studies with emphasis on compensation algorithms for respiratory motion. In 2010, he joined Yale University as a faculty member. He is board certified in Nuclear Medicine physics and instrumentation by the American Board of Science in Nuclear Medicine. His current research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging, including reconstruction algorithms, motion correction, dynamic imaging, and translational imaging. The translational and clinical applications of these projects include early detection of chemotherapy-induced cardiotoxicity, multimodality imaging of heart failure, and eliminating respiratory motion variability for assessing response to therapy. Many of the imaging technologies developed in his lab has been or is being implemented in clinical PET and SPECT scanners. In 2012, he was awarded with the Bruce Hasegawa Young Investigator Medical Imaging Science Award from the IEEE Nuclear Medical and Imaging Sciences Council for "contributions to the imaging physics of SPECT/CT and PET/CT, with emphasis in quantitative imaging and motion correction."
Radiation dose reduction methods in PET/SPECT/CT
Machine learning and deep learning in imaging applications
Motion correction methods for PET/CT and SPECT/CT
Early detection of chemotherapy-induced cardiotoxicity through advanced cardiac imaging
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Santiago Callegari,Billy Vermillion,Alaa Alashi,Rachel Burns, Sun-Joo Jang,Nicole Guerrera, Liang Guo, Ana Depino, Fatema Zohora,Kim G.E. Smolderen,Carlos Mena Hurtado,Chi Liu,
Journal of the American College of Cardiologyno. 13 (2024): 2251
Bo Zhou, Jun Hou, Tianqi Chen, Yinchi Zhou,Xiongchao Chen,Huidong Xie,Qiong Liu,Xueqi Guo,Yu-Jung Tsai,Vladimir Y. Panin,Takuya Toyonaga,James S. Duncan,
CoRR (2024)
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Medical Image Analysispp.103180, (2024)
IEEE transactions on medical imagingno. 99 (2024): 1-1
Alexandre Bousse, Venkata Sai Sundar Kandarpa,Kuangyu Shi,Kuang Gong,Jae Sung Lee,Chi Liu,Dimitris Visvikis
IEEE Transactions on Radiation and Plasma Medical Sciencesno. 99 (2024): 1-1
CoRR (2024)
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MACHINE LEARNING IN MEDICAL IMAGING, MLMI 2023, PT I (2024): 12-22
Xueqi Guo,Jing Wu, Ming-Kai Chen,Qiong Liu, John A. Onofrey,Darko Pucar, Yulei Pang, David Pigg,Michael E. Casey, Nicha C. Dvornek,Chi Liu
IEEE Transactions on Radiation and Plasma Medical Sciencesno. 4 (2023): 344-353
Lecture Notes in Computer Science (2023): 119-131
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