Per ACGME guidelines, all residents are required to participate in at least one research project that culminates in a publication or presentation at a national meeting. We hold an annual research fair, where residents can learn about opportunities for research throughout the department (see photo below). In addition, residents interested in pursuing more advanced research opportunities may apply for participation in one of the following research tracks.
Residents specifically interested in a career that includes basic science or translational research can apply into the Clinician-Scientist Research Pathway (CSRP) at the end of the R2 year. Residents in this pathway are given a total of approximately 8 months or more of dedicated research time during the R3 and R4 years during which they learn the fundamentals of performing NIH-funded basic science or translational research in an established laboratory. CSRP represents will present their research at the annual department research symposium. Publication of research is expected and grant writing is encouraged.
Sharath Bhagavatula, MD (Class of 2018)
My research focuses on helping to develop novel technologies for interventional oncology. One of my projects involves developing tools to percutaneously deliver and retrieve implantable microdevices, which can potentially be used to assess efficacy of locally delivered chemotherapeutic agents in vivo prior to systemic delivery (Brigham and Women's Hospital, PI Dr. Oliver Jonas PhD and Dr. Stuart Silverman MD). Another project I’m working on is focused on developing and assessing the feasibility of percutaneous optical image guidance systems for procedures such as molecular targeted biopsies and tumor ablations (MGH/Wellman Center for Photomedicine, PI Dr. Guillermo Tearney MD/PhD).
Jeffrey Guenette, MD (Class of 2018)
My primary research focuses on new MR imaging applications in the head, neck, and spine, both for diagnostic and interventional purposes. Through a grant from the American Society of Head and Neck radiology, I am investigating new methods of imaging and 3D rendering of the facial nerve. I am also investigating applications of percutaneous image-guided head, neck, and spine interventions with a focus on developing methods that allow ablation of tumors adjacent to major neurovascular structures (PI: Thomas C. Lee M.D.). I have also spent time investigating quantitative patterns of brain injury in a group of symptomatic former NFL players and in a group of active duty military personnel who have sustained concussion and/or have PTSD (Psychiatry Neuroimaging Laboratory, PI: Martha Shenton Ph.D.). Finally, I have been performing radiology resident education research, primarily related to resident burnout (PI: Stacy E. Smith M.D.).
Elizabeth George, MBBS (Class of 2019)
My research emphasis is on multimodality imaging in response assessment and treatment planning in neuro-oncology. My primary research project focuses on glioblastoma patients treated with PD-L1 inhibition immunotherapy towards identifying response patterns and characterization of pseudo-response using advanced MR analysis and machine learning. In addition, I am also evaluating the role of novel amino-acid PET in patients with GBM on anti-angiogenic therapy (PI: Raymond Huang, MD, PhD). Other collaborative research involves combination of intraoperative US and MRI in assessing adequacy of surgical resection (PI: Alexandra Golby, MD).
Thomas Ng, MD, PhD (Class of 2019)
I am interested in translational functional and molecular imaging, especially with regards to oncology. With my current research project, I seek to understand the pharmacokinetics/dynamics of nano- and immuno- therapies in aggressive primary and metastatic cancers using multiscale molecular imaging. Findings from these studies will be used to develop efficacious combination treatment strategies and complementary, clinically-relevant, molecular imaging tools to evaluate these strategies in patients. (MGH/Center for Systems Biology, PIs: Ralph Weissleder MD PhD, Miles Miller PhD). I am also investigating the use of quantitative clinical DCE-MRI to characterize the tumor microenvironment to guide mesothelioma treatment and diagnosis (PI: Ritu Gill MD, BIDMC).
Nityanand Miskin, MD (Class of 2020)
I am interested in the exploding field of machine learning (ML) as it applies to radiological diagnosis. My main research project involves computer-assisted characterization of the ubiquitous renal cystic lesion using texture analysis and ML techniques, with the objective of quick and accurate categorization of lesions, along with separating benign cysts from those associated with malignancy (PI: Atul Shinagre, MD). I am also working on a ML project focused on early cervical spine fracture detection in the emergency department (Center for Clinical Data Science, PI: Bharti Khurana, MD). Another collaboration also related to spine imaging investigates variability in radiologist interpretation of degenerative disease on MRI (PI: Jacob C. Mandell, MD).
Borna Dabiri, MD, PhD (Class of 2021)
My research broadly focuses on the interfaces between biophysics, mechanical tissue characteristics, and imaging. My work currently focuses on how these factors affect needle tip accuracy in the context of MRI-guided prostate biopsies (PI Junichi Tokuda, PhD), where I am investigating how various patient characteristics, including tumor location and grade, alter local tissue mechanical properties that in turn alter the trajectory of biopsy needles. These data will be utilized to develop a model of predicted needle trajectories as a function of tumor imaging and patient characteristics, increasing throughput and accuracy of MRI-guided prostate biopsies.
Liwei Jiang, MD (Class of 2021)
As a general "early adopter" of technology, I am interested in new tools that will to change how we work. My primary focus is on PET/CT-guided interventions, for which we are using a near-instantaneous image fusion platform to make procedures faster and easier (PI: Paul B. Shyn, MD). I am also part of an Emergency Radiology / Center for Clinical Data Science collaboration to develop an artificial intelligence algorithm for the detection, reporting, and triage of rib fractures on trauma CT scans (PI: Aaron Sodickson, MD, PhD). Furthermore, I am exploring practical applications of 3D printing and am contributing my 3D printing expertise toward the conservation of medical supplies during the COVID-19 crisis (panfab.org).
Clare Poynton, MD, PhD (Class of 2021)
Newest CSRP residents being honored at the End-of-Year celebration 2018
Residents specifically interested in evidence-based imaging and quality improvement can apply at the end of the R2 year into a combined Residency and Fellowship Training Program at the Center for Evidence-Based Imaging (CEBI). This program is run in conjunction with the Harvard T.H. Chan School of Public Health’s Summer Program in Clinical Effectiveness. This program combines 12 months of translational and performance improvement research with 12 months of a radiology clinical fellowship in any radiology subspecialty. Residents are expected to remain at BWH for the clinical fellowship component of this program to maximize learning and continuity.
Tony Trinh, MD (CEBI)
As a part of the BWH radiology residency "3+2” training pathway, I was able to participate in the Center for Evidence Based Imaging (CEBI) Informatics fellowship during my fourth year of radiology residency training. Preparation for this fellowship required enrollment in the Harvard School of Public Health’s Program in Clinical Effectiveness (PCE), which provides a foundation of epidemiological and biostatistics training in preparation for future research endeavors (the HSPH program tuition is subsidized by CEBI). After completion of the PCE, I have spent 80% of my time on imaging informatics research with translation of results to clinical operations initiatives. These projects are focused on quality, safety, and efficiency of care delivery related to medical imaging.
The MGH & BWH Center for Clinical Data Science (CCDS) is a collaboration between Mass General and Brigham and Women's Hospitals to develop, evaluate, clinically translate, and potentially commercialize artificial intelligence for healthcare. By using AI techniques such as machine learning and convolutional neural networks, the center is building systems to improve the detection, diagnosis, treatment, and management of diseases as well as improve clinical operations. Residents may choose to spend elective time in the R4 year at the CCDS.
Jisoo Kim, MD
Clare Poynton, MD, PhD
Matthew Haber, MD (Class of 2020)
I am interested in applications of machine learning to improve upon screening and diagnosis of neurologic disease that might be difficult for the human eye to detect, particularly in a fast-paced clinical setting. My specific research emphasis is on automated detection of idiopathic normal pressure hydrocephalus (iNPH) on head CT using a deep convolutional neural network (PI: Katherine Andriole, PhD). Our goal is to develop a model which can screen for iNPH, a surgically treatable yet underdiagnosed form of dementia, using a robust clinical ground truth.
Olga Laur, MD (Class of 2020)
Sacroiliitis represents an inflammation of the sacroiliac joint, which has a range of radiographic findings including erosions, reactive sclerosis and ankylosis. Diagnosis of sacroiliitis and its consistent staging is of utter importance in rheumatologic care for ankylosing spondylitis as it represents one of the main diagnostic criteria necessary for administration of appropriate pharmaceutical treatments. However, identification of sacroiliitis on a radiograph can be challenging as it can stem from a subtle finding, with the joint often obscured by overlying bowel gas. In addition, there is a low inter-reader agreement in staging sacroiliitis, which affects clinical care as under staging or missing subtle findings of the disease may prevent patient access to appropriate medications and result in high morbidity and mortality with the progression of the disease.
The goal of this project was to provide automated identification and staging of sacroiliitis and its differentiation from normal pelvic radiographs and its mimics such as osteoarthritis. Automatic detection of sacroiliitis may serve as an “expert opinion” and be a helpful tool for a radiologist to arrive at the correct diagnosis without a delay in patient’s care.
Cory Robinson, MD (Class of 2020)
My data science pathway research project involves using deep learning to identify adrenal glands and adrenal masses on full CT abdomen/pelvis series, with the goal of aiding the radiologist in identifying and characterizing adrenal masses, to help standardize interpretation of these incidental findings, reduce variation in recommendations, and improve patient care. Future endeavors include automatically comparing masses to prior imaging to assess stability and applying the ACR guidelines for management of incidental adrenal masses. (Center for Clinical Data Science, PI: Katherine Andriole, and Brigham and Women’s Hospital, PI: William Mayo-Smith).
Travis Caton, MD (Class of 2019)
Walter Wiggins, MD (Class of 2019)
In partnership with the National Center for Image Guided Therapy (NCIGT), our department has an NIH R25 fellowship, which could be incorporated into a 3+2 program for the appropriate resident. The Advanced Multimodality Image Guided Operating (AMIGO) suite is the clinical test-bed for research in the NCIGT and is the main area with R25 fellows complete cancer therapy-focused research projects.
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