Research projects available to graduate students cover a broad range of Medical Physics topics. The following is a list of faculty research interests encompassing both theoretical and experimental approaches.

    (radiation therapy, computer-aided diagnosis, radiomics) MRI
    Dr. Al-Hallaq's research investigates the use of medical images to: 1) inform treatment selection, 2) guide treatment positioning, and 3) assess treatment response following radiotherapy. To inform treatment selection, we have investigated whether MR imaging could prove useful in selecting appropriate candidates for limited-field radiotherapy, known as partial breast irradiation. To guide treatment positioning, we have investigated both x-ray and 3D surface imaging modalities for breast cancer treatments. To assess treatment response, Dr. Al-Hallaq originated the idea of utilizing texture analysis in combination with deformable registration for quantifying changes in healthy lung tissue induced by radiation treatment. Dr. Al-Hallaq frequently collaborates with Samuel Armato, Ph.D., whose laboratory has developed computerized techniques (i.e., radiomics) to study lung texture in CT scans, to test whether clinical symptoms correlate with changes in CT image features for individual patients. We were the first to publish on the use of radiomics analysis of normal tissue toxicity. Recently, Dr. Al-Hallaq authored the radiation physics sections of two national NRG protocols which aim to determine whether stereotactic body radiotherapy (SBRT) can control metastatic disease without significant toxicity. Dr. Al-Hallaq's research background in texture analysis and clinical background as a clinical radiotherapy physicist has allowed her to contribute significantly to translational cancer research.

    (computer-aided diagnosis, deep learning, machine learning, quantitative imaging, radiomics)
    Dr. Armato's research broadly involves the development and evaluation of computerized techniques for the quantitative analysis of medical images and the assessment of tumor response to therapy through a variety of interdisciplinary image-based projects. More specifically, our research has involved the computerized detection and evaluation of lung nodules in thoracic computed tomography (CT) scans, the assessment of image quality and pathologic change in temporally subtracted chest radiographic images, the computerized evaluation of mesothelioma tumor and response to therapy in CT scans, critical analyses of image-based tumor response assessment for mesothelioma, the development of objective CT-based metrics for the quantification of mucosal inflammation due to sinusitis, the application of radiomics to the pre- and post-treatment CT scans of radiation therapy and immunotherapy patients to predict normal lung tissue complications, and the evaluation of reference standards for computer-aided diagnosis (CAD) research. The assessment of mesothelioma tumor volume from CT scans recently has been augmented by Dr. Armato's group through the application of deep-learning-based methods to this complicated image segmentation task.

    (ultrasound, MRI, histotripsy, novel treatment techniques)
    The focus of the Biomedical Acoustics Development and Engineering Research Laboratory (BADER Lab) is the translation of therapeutic ultrasound for non- or minimally invasive treatment of cardiovascular and cancerous disease. Specifically, the BADER Lab utilizes acoustic cavitation for combinatorial ablation and enhanced drug delivery treatment strategies of pathologies resistant to standard interventional techniques. To assess bubble activity and the resultant changes in tissue structure, Dr. Bader's group is developing multi-modal imaging approaches using both diagnostic ultrasound and magnetic resonance imaging. Analytic and numerical bubble dynamics models are also utilized to gain insight into the mechanism of action of our therapeutic approaches. Current research topics include: - Chronic thrombus ablation with histotripsy and thrombolytic drugs - Passive cavitation and MR imaging to assess histotripsy-induced liquefaction - In vitro assessment of histotripsy-enhanced drug delivery - Histotripsy-induced sonochemical reactions for the treatment of cancer - Numeric and analytic models of bubble dynamics - Magnetic Resonance-guided transurethral prostate ablation For more information, visit the laboratory website:

    (MRI, quantitative imaging, radiomics, disease modeling)
    Dr. Carroll's research uses advanced imaging techniques within MRI to quantify physiologic changes that reflect pathology. His group's special emphasis is on the absolute quantification of these changes to allow for both longitudinal and cross-sectional study of patient groups. Furthermore, precise quantification of physiologic changes over time serve as powerful biomarkers of disease progression/regression resulting from drug therapy, and can therefore dramatically reduce the time cost of drug trials. The Carroll lab has pioneered methods by which internal reference values are used to ?self-calibrate? images and developed protocols for evaluating repeatability and predictability of these quantitative biomarkers. His group has applied this approach to create 3D images of tissue perfusion, oxygen utilization, vascular wall inflammation, and blood volume. In addition, Dr. Carroll's research group collaborates on a wide variety of research topics including, stroke/neurovascular disease, Multiple Sclerosis, cancer, and cardiovascular disease with local, national, and international researchers.

    (computed-aided diagnosis, machine learning, deep learning, quantitative imaging, radiomics) MRI
    Dr. Giger's research has focused on computer-aided diagnosis, including computer vision and machine learning, in the areas of breast cancer, lung cancer, prostate cancer, lupus, and bone diseases. Our computer-aided diagnosis/machine learning research in computational image-based analyses of cancer for risk assessment, diagnosis, prognosis, response to therapy has yielded various translated components, and we are now using these image-based phenotypes in imaging and multi-omics (e.g., genomics) association studies for cancer discovery and predictive modeling.

    (electronic paramagnetic resonance imaging)
    We are actively engaged in development of EPR oxygen imaging with application to tumor physiology and response to therapy. We are also investigating EPR based techniques to image molecular biologic cell signaling. Active areas of investigation in instrument design include rapid scanning continuous wave techniques; magnet design, construction, and evaluation; novel techniques for pulsed EPR projection acquisitions; resonator design, construction, and performance evaluation. In collaboration with chemistry colleagues, we are pursuing development of novel injectable spin probes with sensitivity to various aspects of body fluids with distribution in various (controllable) fluid compartments. We are also researching novel tomographic and non-tomographic image acquisition strategies, and the scaling of EPR imaging technology to larger biologic objects. Visit our website for more information.

    (novel PET detectors, PET reconstruction, PET system design, small-animal imaging)
    Dr. Kao's research centers on developing novel detector technologies and employing them to build practical, high-performance PET systems by synergistically integrating them with advanced data processing and image reconstruction.

    Dr. Kao's lab pioneered a voltage-based sampling method for PET data acquisition called MVT (multi-voltage threshold) that provides a practical solution to digitizing fast signals generated by modern time-of-flight (TOF) PET detectors. It is also used in conjunction with a novel solid-state photodetector called a silicon photomultiplier to produce highly compact and functionally modularized detectors. Dr. Kao is extending his work to developing organ-specific PET imagers for humans, including both dedicated systems and inserts for simultaneous PET/MRI. He is also developing flat-panel TOF PET detectors, and the supporting software platform, to allow rapid configuration and development of human PET systems.

    Dr. Kao is the faculty co-director of the PET/CT/SPECT facility of the BSD Integrated Small Animal Imaging Research Resource, and actively collaborates with Dr. Chin-Tu Chen and other investigators in using PET for conducting basic and translational biomedical research, including the development of new drugs/treatments for cancer and neurological and cardiological diseases. Therefore, in addition to contributing his PET systems, he is also interested in developing artificial-intelligence based methods for registering multi-modality and longitudinal studies, for analyzing static and dynamic data with compartmental modeling, and for making discoveries from population studies.

    Karczmar and Roman
    (MRI, MR spectroscopy, dynamic contrast-enhanced MRI)
    We work on extending the current clinical application of MR imaging by developing and deploying novel sequences that can provide functional information on both healthy and diseased tissue. We are currently evaluating the clinical utility of an advanced spectroscopic imaging method, primarily as applied to imaging of breast, but also testing it in other sites, such as the prostate, liver, and brain. This sequence is developed for both morphological and functional imaging. Another method currently in development is a hybrid diffusion-weighted/T2-mapping sequence which can be used to obtain information on tissue structure within an imaging voxel. This is of particular interest in prostate, where there is a strong need to stratify cancerous lesions by grade in order to make optimal treatment decisions, but this sequence could have broad application in other sites. We work very closely with clinical faculty to identify and address the most pressing clinical challenges. In breast and prostate, these primarily relate to cancer detection and diagnostics, as well as to development of personalized treatment and risk mitigation plans. We are also leading multiple projects that have the goal of improving or optimizing the acquisition and processing of dynamic contrast enhanced MRI, for higher clinical utility. We are also interested in the reduction of risk through development of protocols that minimize the use of gadolinium-based contrast agents.

    La Rivière
    (microscopy, image reconstruction, XFCT, emerging image modalities) MRI
    Thanks to the affiliation of the University of Chicago with the Marine Biological Laboratory in Woods Hole, MA, we have developed a number of collaborations that apply our expertise in inverse problems to the development of new computational microscopy approaches. One strand, in collaboration with Hari Shroff of NIH, involves developing novel approaches to modeling and fusing multi-view data in light-sheet microscopy, including a three-lens, three-view system and a mirror-based system to create orthogonal light sheets and capture four views of the sample. A second strand involves developing novel approaches to estimate the orientation of molecules that have been tagged rigidly with anisotropic fluorophores like GFP. At present, we are seeking to merge the two strands by developing novel multiview, light-sheet approaches to imaging of molecular orientation.

    We have also worked for several years to develop new image reconstruction algorithms and new image acquisition strategies for X-ray fluorescence computed tomography (XFCT). X-ray fluorescence computed tomography (XFCT) is an emerging imaging modality that allows for the reconstruction of the distribution of nonradioactive elements (mostly metals) within a sample from measurements of fluorescence X-rays produced by irradiation of the sample. Many endogenous metals and metal ions, such as Fe, Cu, and Zn, play critical roles in signal transduction and reaction catalysis, while others (Hg, Cd, Pb) are quite toxic even in trace quantities. In recent years, in collaboration with Ling-Jian Meng at UIUC, we have begun to explore radically different ways of measuring XFCT data. Our insight was to exploit the fact that X-ray fluorescence is a stimulated emission modality to perform selective illumination coupled with detection by pixelated cameras through collimating apertures to perform direct imaging without need for tomographic image reconstruction. For more information, visit the laboratory website:

    (digital tomosynthesis, computed tomography, cone-beam CT, tomographic image reconstruction, emerging image modalities) MRI
    The research of the Pan lab centers on the investigation and development of advanced tomographic imaging techniques, with an eye toward their translation to medical and other applications. In collaboration with leading academic and industrial investigators around the world, we have performed research on a variety of tomographic imaging techniques, including: X-ray tomographic imaging in the forms of diagnostic computed tomography (Dx CT), cone-beam CT (CBCT), and digital tomosynthesis (DT), molecular imaging technologies in the forms of single-photon-emission CT (SPECT) and positron emission tomography (PET), and magnetic resonance imaging (MRI) and electron paramagnetic resonance imaging (ERPI). Encompassing both algorithm- (software) and system/workflow- (hardware) development, our research seeks to fully exploit the potential of new algorithms to elevate existing imaging systems' performance as well as to enable new imaging systems with lower-cost, user-friendly hardware design, high application significance and possible market value.

    We currently have a number of ongoing research programs: (1) development of algorithm-enabled CBCT technology tailored specifically for enabling the planning, monitoring, and evaluation of therapeutic cancer treatments; (2) development and translation of spectral (i.e., photon-counting) CT imaging capability, enabled by advanced algorithms, on current Dx and CBCT systems without an increase in hardware cost and scanning-protocol complexity; (3) development of advanced DT for breast-cancer screening, and possibly diagnosis, with boosted sensitivity and specificity; (4) investigation of algorithm-enabled, time-of-flight PET with a longer field of view, but lowered-cost, for clinical and research cancer imaging and for drug/tracer development; and (5) development and translation of technologies specifically optimized for oncological specimen imaging in surgery and pathology.

    (image-guided radiation therapy)
    Dr. Redler's research focuses on the role of imaging in radiation therapy and includes preclinical, translational, and clinical applications of image guided radiation therapy (IGRT).

    Dr. Redler is working to develop a preclinical platform for small animal precision intensity modulated radiation therapy (IMRT) modelled after clinical IMRT approaches, based on custom 3D printed beam shaping/modulating devices. Such a platform will enable preclinical studies investigating how to incorporate a myriad of functional imaging information into clinical treatment planning. For example, electron paramagnetic resonance imaging (EPRI) as developed in the Halpern lab at UofC can be used to image in vivo tissue oxygenation in 3D and guide radiation dose painting in small animal tumors to help determine how this can be done effectively in human patients to improve outcomes and overcome radiation resistance due to hypoxic tumor environments.

    Dr. Redler is also working to incorporate advanced imaging approaches for improved precision and accuracy in clinical radiation therapy. This work includes using the TrueBeam on board kV imaging for intrafraction motion mitigation via automatic tracking of implanted fiducials in prostate patients and real-time visualization of spine SBRT patient bony anatomy/implanted orthopedic hardware. Dr. Redler is also investigating the feasibility of using MRI for guiding radiosurgical (Brain SRS and Spine SBRT) procedures on the ViewRay MRLinac. Additionally, Dr. Redler has interests in developing novel modalities and approaches to clinical IGRT, including a patented scatter imaging approach which utilizes inherent scattered radiation resulting during treatment delivery to form images of the treated patient's anatomy to track targeting in real time.

    (MRI, image-guided interventional procedures, MRI-guided therapeutic ultrasound)
    The improvements of hardware and software in medical imaging have resulted in an expanding and evolving role for image guidance during interventional or surgical procedures. We develop novel methodologies in high- and ultra-high field magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), ultrasound imaging and computed tomography (CT) imaging. We advance innovative sequences and protocols in MRI and MRS for diagnosis and treatment monitoring of anatomical and molecular changes. We have pioneered several ultrasound research projects, including MRI-guided treatment monitoring of the therapeutic effects of high-intensity focused ultrasound. In addition, we have optimized image guidance for LASER tissue ablation and needle-guided interventions for various interventional procedures. In CT imaging our emphasis is on improving neurosurgical navigation and in ultrasound imaging we focus on automatic segmentation techniques of tumors and lymph nodes.

    (motion-guided radiation therapy, image-guided radiation therapy, treatment planning) MRI
    The Wiersma Lab is primarily focused on automated systems in the field of radiation therapy, with an emphasis on robotics, optimization, and quality assurance. Our robotics project involves the design, construction, and clinical deployment of a robot to replace current stereotactic radiosurgery methods that have therapeutic drawbacks and are highly invasive to patients. This next-generation frameless, maskless approach will use 3D patient surface monitoring to track patient head motion in real-time and input it into a 6DoF parallel kinematic robot that is located below the patient's head to continuously cancel out patient head motion during treatment. Our optimization research explores the use of optimization algorithms to solve large scale radiation therapy treatment planning problems that involve thousands of variables. Of particular interest is the use of L-BFGS or POGS solvers to perform ultra-fast optimization for the purposes of real-time adaptive radiation therapy. This project involves a wide range of methods ranging from volumetric image acquisition techniques, deformable image registration, and fast radiation dose calculation, and, if successful, would solve the long standing intra-fractional motion management problem in radiation therapy. Finally, our quality assurance research involves the use of on-line collaborative systems to improve current QA methods of ensuring that medical devices such as CT scanners, MRI units, or linear accelerators are operating within proper safety standards. For more details please visit