Dr. Stephen J. Glick
Research Biomedical Engineer
SPIE Involvement:
Conference Program Committee | Editorial Board Member: Journal of Medical Imaging | Editorial Board Member: Journal of Medical Imaging | Author | Instructor
Publications (54)

Proceedings Article | 22 May 2020 Paper
Proc. SPIE. 11513, 15th International Workshop on Breast Imaging (IWBI2020)
KEYWORDS: Breast, Roads, Medicine, Tissues, Imaging systems, Printing, Mammography, Inkjet technology, Digital breast tomosynthesis, Breast imaging

Proceedings Article | 16 March 2020 Paper
Proc. SPIE. 11312, Medical Imaging 2020: Physics of Medical Imaging
KEYWORDS: Photon counting, LabVIEW, X-ray computed tomography, 3D image reconstruction, Calibration, X-rays, Control systems, Data acquisition, X-ray imaging, X-ray detectors

Proceedings Article | 16 March 2020 Presentation + Paper
Proc. SPIE. 11312, Medical Imaging 2020: Physics of Medical Imaging
KEYWORDS: Breast, Convolutional neural networks, Tissues, X-rays, Calcium, Biopsy, Monte Carlo methods, Neural networks, Mammography, X-ray imaging

SPIE Journal Paper | 26 February 2020
JMI Vol. 7 Issue 04
KEYWORDS: Digital breast tomosynthesis, Breast, Performance modeling, Signal detection, Data modeling, 3D modeling, Imaging systems, Statistical analysis, Statistical modeling, Clinical trials

SPIE Journal Paper | 17 October 2019
JMI Vol. 6 Issue 04
KEYWORDS: Sensors, Breast, Signal detection, Modulation transfer functions, X-rays, Digital mammography, Tissues, Mammography, X-ray detectors, Sensor performance

Showing 5 of 54 publications
Conference Committee Involvement (14)
Physics of Medical Imaging
14 February 2021 | San Diego, California, United States
Physics of Medical Imaging
16 February 2020 | Houston, Texas, United States
Physics of Medical Imaging
17 February 2019 | San Diego, California, United States
Physics of Medical Imaging
12 February 2018 | Houston, Texas, United States
Physics of Medical Imaging Posters
13 February 2017 | Orlando, FL, United States
Showing 5 of 14 Conference Committees
Course Instructor
SC1292: Technological Assessment of X-Ray Based Breast Imaging Systems Using Anthropomorphic Phantoms
Development of new breast X-ray imaging technologies or improvements to hardware or software of current systems usually require the accurate assessment of image quality. Image quality assessment methods are also required for quality control (QC) of clinical systems, for example as required by the U.S. Mammography Quality Standards Act (MQSA) program. The gold standard for assessment of image quality is human reader studies assessing diagnostic performance over a cohort of representative clinical images. These clinical trials are often difficult and expensive to perform, and therefore researchers have been studying alternative approaches that can assess diagnostic task performance without imaging patients. This short course will discuss methods for objectively assessing task performance of breast imaging systems without conducting a clinical trial. One approach that will be discussed is the in silico modeling of a clinical trial. This approach involves complete computer modeling of each step in the imaging chain including: 1) modeling of breast and relevant breast lesions, 2) modeling of the imaging system, and 3) modeling of the observer. Another more experimental approach that will also be discussed involves: 1) development of anthropomorphic physical phantoms with diagnostic features, 2) imaging of these phantoms on breast imaging commercial or prototype systems, and 3) assessment of task performance with either model or human observers. For maximum efficiency, the proposed in silico and experimental approaches require the development of computer or model observers that can emulate either ideal or human observer task performance. This short course will discuss the use of new machine learning algorithms that can be used to model observer performance in the assessment of breast imaging technology. This course will describe and make attendees aware of useful open-source software tools that can be downloaded.
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