Welcome to the Center For Advanced Metabolic Imaging in Precision Medicine (CAMIPM), a National Center for Biomedical Imaging and Bioengineering (NCBIB) in the Perelman School of Medicine at the University of Pennsylvania.
The CAMIPM develops and translates cutting edge noninvasive metabolic imaging biomarkers for use in biomedical research. Technology development is focused in four major application areas: Oncology, Cardiovascular disease, Neuropsychiatry, and Musculoskeletal disorders. These technologies will have substantial impact on the fundamental understanding of disease mechanisms, early diagnosis, and development of novel therapies for several diseases such as Alzheimer’s disease, Epilepsy, Arthritis, Cancer, Stroke, and heart disease, and thus contribute to precision medicine and enhanced patient care. The facility’s core sections provide research and computing resources for numerous user, collaborative, and training projects.
The focus of this center is on developing instrumentation, methodologies, and data analysis techniques for the quantitative assessment of functional, structural, and metabolic parameters in humans with the use of chemical exchange weighted molecular magnetic resonance imaging (MRI), MRI of oxygen consumption, down field spectroscopy, and diffuse optical imaging techniques.
We are supported by the NIBIB under Grant No. P41 EB029460.
CAMIPM Seminar Series
"Machine Learning in MR Image Reconstruction and Processing"
Ze Wang, PhD
Department of Diagnostic Radiology and Nuclear Medicine
Center for Advanced Imaging Research (CAIR)
University of Maryland Baltimore
Machine learning has been widely used in MR image reconstruction and processing. In this talk, I will introduce several methods we developed in this line of research. I will first introduce a POCS-enhanced deep learning network for MR image reconstruction and then introduce a few methods for denoising and accelerating arterial spin labeling (ASL) perfusion MRI. Learning points in my talk will include: the rationale for why deep learning can solve highly complex technique problems; capability of deep learning for denoising and data acquisition acceleration.
Date: Thursday, March 21, 2024
Location: Room 1412 BRB2
Time: 3:00 PM
Meeting ID: 934 3573 0386