Ronald Boellaard, PhD Amsterdam University Medical Centre, VUMC; Amsterdam, Netherlands
Prof. Boellaard’s major research interest is on (improving) quantitative PET imaging, including development and evaluation of image reconstruction methods, image processing algorithms, partial volume correction methods, semi-quantitative analysis and full pharmacokinetic modeling of (dynamic) PET studies. He is actively involved in the standardization and harmonization of quantitative FDG PET/CT studies in multi-center studies. He is the first author of the European guideline for quantitative FDG PET/CT imaging.
Dr. Buvat’s research focuses on developing and validating new biomarkers in Positron Emission Tomography, including using Artificial Intelligence methods, for precision medicine. She is a strong advocate of reproducible research and her lab, thanks to Christophe Nioche, leads the development of the LIFEx freeware for radiomics (ie the high-throuput extraction of features from medical images), a software that currently has more than 5,000 users worldwide.
- Quantitative MRI and Image Processing
- Development of novel and clinically relevant MR imaging biomarkers in breast cancer
- Implementation of MR imaging biomarkers in dedicated reading algorithms and translation into clinical care for an improved diagnosis, prediction, and prognosis
- Application of radiomics and machine learning to multiparametric MRI
Dr. Jayashree Kalpathy-Cramer is the Director of the QTIM lab and the Center for Machine Learning at the Athinoula A. Martinos Center for Biomedical Imaging and an Associate Professor of Radiology at MGH/Harvard Medical School. Dr. Kalpathy-Cramer is also Scientific Director at MGH & BWH Center for Clinical Data Science. An electrical engineer by training, she worked in the semiconductor industry for a number of years. After returning to academia, she is now focused on the applications of machine learning and modeling in healthcare. Her research interests include medical image analysis, machine learning and artificial intelligence for applications in radiology, oncology, and ophthalmology. The work in her lab spans the spectrum from novel algorithm development to clinical deployment. She is passionate about the potential that these techniques have to improve access to healthcare in the US and worldwide.