Dr. Kurugol is an Assistant Professor of Radiology at Harvard Medical School and a principal investigator and the director of the Quantitative Intelligent Imaging (QUIN) research group in Computational Radiology Lab (CRL) at Boston Children's Hospital. She received her BS from Middle East Technical University (METU),  MS from Bilkent University, Ankara, Turkey, and PhD from Northeastern University, Boston, MA, all in Electrical and Computer Engineering. Previously she was a research fellow in Laboratory of Mathematics at Brigham and Women's Hospital and Harvard Medical School.

Her research interests include machine intelligence, artifical intelligence, and computational models and their applications in medical imaging and clinical applications.

Dr. Kurugol is a member of MICCAI, ISMRM and SPR. She has served in organizing comitte of international conferences, and is a reviewer for many conferences and journals including ISMRM, MICCAI and ISBI.

RECENT PROJECTS:

Diffusion Weighted MRI  of the kidneys and bowel

Dr. Kurugol recent research focused on development of new algorithms for motion-robust DW-MRI, advanced spatially-regularized probabilistic models for abdominal DW-MRI which aim to improve the IVIM model with applications in bowel imaging for pediatric Crohn's disease and renal imaging for renal diseases. Her team recently developed a new imaging sequence for simulatenous distortion and motion compensation for renal DW-MRI, which corrects for motion and distortion without dramatically increasing the scan time such as in respiratory triggering.

Automated image analysis for extracting imaging biomarkers of disease

Dr. Kurugol and her team developed machine learning techniques for image segmentation with the goal of generating quantitative imaging markers of pediatric diseases, including Crohn's Disease and kidney diseases, cardiopulmonary disease, and for functional imaging of kidneys.

Cost-efficient semi-supervised learning and active learning approaches for medical image analysis

She has also developed cost-efficient deep learning techiques using semi-supervised learning and novel active learning methods for segmentation for imperfect data and with low annotation cost. 

Functional imaging of kidneys in pediatric patients

Another important area of her recent research is developing motion compensated, non-sedated DCE-MR imaging methods for evalutation of kidney function and glomerular filtration rate. Her team developed new image reconstruction and post-processing techniques for motion-robust DCE-MRI imaging of babies and chidren with hydronephrosis.

Funding

Dr. Kurugol's research has been supported by several grants from Harvard and Boston Children's Hospital Translational Research Program, Society of Pediatric Radiology, Crohn's and Colitis Foundation (CCFA) and Americal Gastroenterelogical Association (AGA). She was recently awarded a Summa Cum Laude merit award in ISMRM 2019 and a John Caffey Award for best science paper  from Society of Pediatric Radiology in 2018.

Research goals

Dr. Kurugol asims is to translate her research findings into viable, cost effective and life-saving clinical applications by providing the clinicians with necessary tools to better identify early onset of disease and to help improve patient care.