Prof. Jerome Zhengrong Liang
Jerome Z. Liang gained a PhD degree in Physics from the City University of New York in 1987, followed by one year Research Fellow in Nuclear Medicine and Radiation Oncology at Albert Einstein College of Medicine. Then he was a Research Associate and Assistant Professor in Radiology at Duke University Medical Center until 1992 when he joined the State University of New York at Stony Brook (SUNY-SB). He holds a Professorship in the Departments of Radiology, Computer Science, and Biomedical Engineering since 2000. He is a co-founder of the Program in Biomedical Engineering at SUNY-SB. His primary research interests in medical imaging include data acquisition geometry, image formation and processing methodology, feature-based visualization, and computer-aided detection and diagnosis. He has authored more than 100 scientific journal publications, in addition to numerous conference articles and book chapters. He serves on the Editorial Board of the IEEE Transactions on Medical Imaging since 1999 and on various research proposal review committees of NIH, DOE, DOD, AAPM, etc. He is a co-founder of a startup company, Viatronix Inc. (www.viatronix.com), in commercializing virtual colonoscopy for colon cancer screening and other software for medical utility. He has been leading, since 1999, a team to develop low-dose computed tomography by modeling the original data properties and optimizing the reconstruction strategies. Currently he is the principal investigator for two NIH-sponsored R01 projects of “Developing Virtual Colonoscopy for Colon Cancer Screening” and “Screening Lung Cancer by Ultra Low-dose Computed Tomography”. He was elevated to IEEE Fellow in 2007 by the citation “for contributions to medical image reconstruction and virtual colonoscopy”.
Abstract:
Building a low-dose computed tomography (LDCT) system has been a concern when the hardware was designed for human use in the 1970s. In the software aspect, majority of the effort has been focused on data calibration to meet the Radon transform, which is the mathematical foundation for CT image formation, so that diagnostic images can be reconstructed by inverting the transform. The inversion is based on the mean of each acquired datum. The output image of the inversion becomes noisy when the data are noisy for the patients of larger size. Therefore, in order to mitigate this difficulty, one choice is to increase the mAs level, i.e., to deliver more X-rays to the patients. The other choice is to filter the image noise. The former will increase the risk of X-ray exposure while the latter will sacrifice the spatial resolution in the image. When the reconstruction is no longer based on the data means, as we have learned from SPECT/PET image reconstruction, then LDCT has the new meanings, i.e., modeling the data physical and statistical properties and reconstructing the corresponding images with the quality of adequately serving the clinical task by as low as achievable X-ray exposure. Modeling the properties into a mathematically-tractable formula for image reconstruction and optimizing the reconstruction has attracted a great research interest since around 2000. After a decade of research effort on modeling and optimizing the image reconstruction for low X-ray exposure patient scanning, (no longer based on the data means), variable claims have been reported for dose reduction. Quantifying the dose reduction shall be task-dependent. It is expected that further optimization of hardware design and its associated image reconstruction will deliver an optimal dose to the patient under the task adequately serving the clinical needs. An example will be given using cone-beam LDCT terminology.
邀请单位：光学与生物医学工程创新团队
时间: 2012年12月12日(星期三) 15:00
地点: 大学城清华校区 CI 108
