Currently, medical images require a physician to extract clinically relevant information. This talk will explore current work towards making images part of the quantitative medical history and to enable large-scale image-based studies of disease. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Our goal is to enable application of existing algorithms that were originally developed for high resolution research scans to severely undersampled images. Application of the method is illustrated in the context of neurodegeneration and white matter disease studies in stroke patients.
Polina Golland will discuss her group's research in computational analysis of MRI scans that aims to provide accurate measurements of healthy anatomy and physiology, and biomarkers of pathology. Applications range from fetal development to aging brain.
I will review our work in extracting clinically relevant characterizations of anatomy and pathology from medical images in two domains. First, joint modeling of image, genetic and clinical data is used to gain insight into the patterns of disease in large heterogeneous clinical populations. Examples include studies of white matter disease in stroke patients from brain MRI, of genetically defined patterns of emphysema in COPD patients as observed in chest CT, and others. The second family of applications aims to provide accurate delineations of pathology and make predictions in medical scans of individual patients. Examples include functional imaging of the placenta and cardiac image analysis for surgical planning.
2016 MIT Digital Health Conference