SUMMARY
Kumar Sandrasegaran, M.B., Ch.B., focuses his research on advanced imaging techniques for pancreatic and hepatobiliary diseases. His work centers on improving diagnostic accuracy and treatment planning through innovations in magnetic resonance imaging (MRI), including diffusion- and perfusion‑weighted imaging, secretin‑enhanced magnetic resonance cholangiopancreatography (MRCP) and quantitative imaging biomarkers. He also examines the use of artificial intelligence (AI) for early detection of pancreatic cancer and the application of texture analysis for tumor characterization.
Dr. Sandrasegaran's contributions extend to imaging‑based prediction of treatment response in liver and pancreatic cancers, as well as functional imaging approaches for chronic pancreatitis and liver disease. He aims to refine imaging protocols and integrate quantitative tools into clinical workflows to enhance precision in diagnosis and patient management.
Focus areas
- Advanced MRI for pancreatic disease. Dr. Sandrasegaran develops and validates MRI techniques, such as diffusion‑weighted imaging and secretin‑enhanced MRCP, to help distinguish chronic pancreatitis from pancreatic cancer and to evaluate exocrine function. These methods offer noninvasive options for early diagnosis and staging, reducing the need for invasive procedures.
- AI in cancer detection. Dr. Sandrasegaran leads studies evaluating AI algorithms for pancreatic cancer detection with CT scans, including methods to identify visually occult preinvasive lesions. This work advances the clinical integration of AI‑based tools for early diagnosis, a critical step toward improving survival outcomes in pancreatic cancer.
- Functional imaging for liver disease. Dr. Sandrasegaran's research investigates hepatobiliary MRI parameters and intravoxel incoherent motion techniques to predict outcomes in cirrhosis and assess liver fibrosis. These imaging biomarkers seek to replace invasive biopsies and enable longitudinal monitoring of disease progression and therapy response.
- Quantitative imaging biomarkers. By applying texture analysis and T1 mapping, Dr. Sandrasegaran characterizes renal and pancreatic tumors and assesses the severity of chronic pancreatitis. These quantitative imaging approaches improve reproducibility and objectivity in interpretation, supporting more-personalized and more-precise treatment strategies.
- Imaging response to therapy. Using dynamic contrast-enhanced MRI and magnetic resonance spectroscopy, Dr. Sandrasegaran monitors treatment response in hepatocellular carcinoma and pancreatic cancer. These techniques help predict therapeutic outcomes and guide adjustments in oncologic care.
Significance to patient care
Dr. Sandrasegaran advances how pancreatic and hepatobiliary diseases are diagnosed and monitored. He develops MRI and CT techniques that produce clearer, more detailed images, helping healthcare professionals tell the difference between conditions that can look similar — such as chronic pancreatitis and pancreatic cancer — without surgery or other invasive tests.
Dr. Sandrasegaran also studies AI tools that can detect very early signs of pancreatic cancer, including small changes that may be hard to see on regular scans. Finding cancer earlier gives patients more treatment options and improves outcomes.
Professional highlights
- Radiological Society of North America:
- Magna Cum Laude Award, RSNA Educational Exhibit, 2013, 2025.
- Chair, Gastrointestinal Refresher Course Program, 2012-2020.
- RSNA Honored Educator Award, 2013, 2014, 2015, 2016, 2017, 2018.
- Stanford/Elsevier Top 2% Scientists List, Stanford University, Elsevier, 2025.
- Transformative Science Award, Pancreas Cancer Artificial Intelligence Team, Mayo Clinic, 2025.
- Co-director, Body Imaging Course, American College of Radiology, 2017-2023.
- Board of editors, Abdominal Imaging, 2014-2022.
- Chair, Scientific Program Subcommittee, American Roentgen Ray Society, 2011-2018.
- Wylie J. Dodds Research Award, Society of Gastrointestinal Radiologists, 2009.
- Showalter Research Award, Indiana University School of Medicine, 2008.