Research

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.

Accordion: 
Hania Al-Hallaq (radiation therapy, computer-aided diagnosis, radiomics)

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. 

Samuel Armato (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. 
 

Bulent Aydogan (image-guided radiation therapy and functional imaging)

I have a broad background in medical physics, with specific training and expertise in key research and clinical areas in radiotherapy. I am currently serving as the Director of Medical Physics in the Department of Radiation and Cellular Oncology at UChicago. My experience and interest are mainly in clinical translational radiation oncology, including image-guided therapy, intensity modulated radiation therapy, and imaging for therapy response. I have developed a targeted linac-based Total Marrow Irradiation (TMI) technique to improve outcome in advanced hematological malignancies and played an important role in its clinical application. My work led to four Phase I and two Phase II studies thus far. I have trained and helped national and international institutions to implement intensity modulated total marrow irradiation (IMTMI) programs. I am also very interested in the development of nanotechnology platforms for theranostic applications. My research on targeted nanogold contrast agent for cancer diagnosis and therapy has been featured in different venues and has a full patent. 

Kenneth Bader (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: baderlab.uchicago.edu 

Timothy J. Carroll (neurologic imaging)

Evaluates and quantifies physiologic changes in the brain resulting from neurovascular disease and stroke. He develops advanced magnetic resonance imaging (MRI) to target changes in arterial vasculature, tissue perfusion, and the arterial wall itself, in order to quantify physiologic changes in patients. The imaging techniques he employs have the potential to allow for rapid, real-time assessment of cerebral blood flow, enabling the triage of patients suffering from acute stroke and staging and tracking the response to therapy of certain types of cancer. As a physicist and biomedical engineer, he works collaboratively with physicians from the disciplines of radiology, neurosurgery, neurology, cardiology, and preventative medicine.

Chin-Tu Chen (PET instrumentation, tomographic image reconstruction, molecular imaging)

My research interests, primarily in multi-modality molecular imaging, cover a broad spectrum of imaging-centered topics including imaging physics and instrumentation, image reconstruction and processing, imaging tracers and probes development, physiological modeling, quantitative and intelligent image analysis, as well as applications of molecular imaging methods in a wide spectrum of biological and medical investigations, especially in cancer, brain and behavioral disorders, cardiopulmonary diseases, diabetes, and tissue/organ injury and repair. We pioneered the concept of multi-modality imaging, including image co-registration and integration, hybrid image instrumentation, image/information fusion in CT, MRI, PET and SPECT reconstruction, processing, and analysis, as well as functionalized targeting imaging probes and tracers research & development.

Patrick La Rivière (microscopy, image reconstruction, XFCT, emerging image modalities)

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: voices.uchicago.edu/larivierelab

Maryellen Giger (computed-aided diagnosis, machine learning, deep learning, quantitative imaging, radiomics)

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. 

Howard Halpern (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. 

Yulei Jiang (computer-aided diagnosis, radiomics artificial intelligence, machine learning, deep learning in medical image interpretation and analysis

My primary research is in the development of CAD methods for the detection and diagnosis of breast cancer and prostate cancer. I developed one of the first CAD methods for the classification of breast calcifications as malignant or benign based fully on computer analysis of mammograms. I have also developed CAD methods for quantitative analysis of multi-parametric prostate MR images and prostate histology images.  Furthermore, I have led my research laboratory in developing quantitative analysis of digital histology images and have developed a method that can quantify the distance between cells as a surrogate for observation of interactions between cells, e.g., between T cells and B cells.  In addition, I have experience in the development of statistical classifiers in CAD applications and in the receiver operating characteristic (ROC) analysis. My research employs both artificial neural networks and linear discriminant analysis classifiers, and I have published on methodological research of statistical classifiers. I have conducted evaluations of CAD methods, developed novel concepts in ROC analysis, and conducted methodological analysis of the evaluation of CAD methods in clinical trials. 

Chien-Min Kao (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. 

Gregory Karczmar (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.

Xiaochuan Pan (tomographic imaging)

My research interest centers on tomographic imaging with an emphasis on its application to advanced CT and PET. I have performed research and published results for more than 25 years on the development of analytic and optimization-based reconstruction for improving current CT and PET performance and/or for enabling new scanning configurations of practical significance with considerably lower cost in CT and PET. My laboratory has also developed hardware design and imaging systems of CT and PET for research and application. I have maintained a sustained, active, close collaboration with clinical and industrial investigators that ensures the clinical and practical relevance of our research on medical imaging. Our work has laid the groundwork for much of the recent progress in reconstruction algorithms and their applications to diagnostic imaging and image-guided surgery and radiation therapy.

Steffen Sammet (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.