Medical Imaging Systems Laboratory

at Marquette University

About

Lab Photo

Our laboratory focuses on the design and optimization of medical imaging systems and reconstruction algorithms, with the goal of improving image quality and reducing radiation dose.

We apply theoretical, computational, and experimental methods to Computed Tomography (CT), tomosynthesis, and X-ray imaging.

Our collaborators include Medical College of Wisconsin, University of Chicago, and industry partners.

Recent News

NIH U01 Grant Award

July 2017: Taly Gilat Schmidt (MU) and Josh Star-Lack (Varian Medical Systems) have been awarded a $2.5 million U01 grant from the NIH entitled, "Software tool for routine, rapid, patient-specific CT organ dose estimation." This project will develop and validate a software tool to estimate the radiation dose delivered to a patient's specific anatomy when a patient undergoes a computed tomography (CT) examination. We are excited to start this four year collaboration between Marquette, Varian, Medical College of Wisconsin, and Children's Hospital of Wisconsin.

New grant from GE Healthcare

June 2017: Taly Gilat Schmidt has been awarded a $187,000 grant from GE Healthcare for a three year project on "Improved Bolus Tracking for CT Angiography." We look forward to continuing our productive collaborations with GE.

Spectral CT work presented at CERN

May 2017: The newest results of our research on photon-counting spectral CT imaging, in collaboration with Emil Sidky and Rina Foygel Barber at the University of Chicago, were presented at the 4th Workshop on Medical Applications of Spectroscopic X-ray Detectors at CERN Lab. The talk, presented by Taly Gilat Schmidt, was titled "Experimental feasibility of quantitative Kedge material decomposition using an optimization-based reconstruction method with empirical spectral modeling."

Presentations at SPIE Medical Imaging 2017

February 2017: PhD student Parag Khobragade presented two posters at the SPIE Medical Imaging Conference related to our collaboration with GE Healthcare on improved image quality metrics. The presentations were titled, "Fractal dimension metric for quantifying noise texture of computed tomography images," and "Effects of Window Width and Window Level Adjustment on the d' Detectability Index in Computed Tomography Images." Taly Gilat Schmidt presented a talk titled, "Spectral CT metal artifact reduction with an optimization-based reconstruction algorithm," in collaboration with Emil Sidky and Rina Foygel Barber from the University of Chicago.