Position: Ph.D. Student

Current Institution: Carnegie Mellon University

Abstract:
Transport-Based Morphometry in Radiology and Applications

Patient care has come a long way since Paul Lauterbur and Sir Peter Mansfield invented magnetic resonance imaging (MRI) in the 1970s. Today, imaging studies are a vital part of accurate medical diagnosis and treatment for every part of the body, from assessing cardiac function to monitoring cancer metastases, checking for bone fractures and assessing brain damage after stroke. Each of these images contains rich, detailed and complex information about the mysterious human body. The key challenge is to extract meaningful information from the deluge of image data. Human visual inspection is the traditional method to interpret these images intelligently. However, with increasing modalities of imaging, resolution, and numbers of studies ordered, there is a growing need for machine vision techniques. Beyond simple automation, machine vision techniques are needed to decipher hidden processes that elude interpretation by human vision. The goals are to identify disease states that escape human detection, and model the changes enabling sensitive differentiation. Understanding the hidden changes would have far reaching impact on early diagnosis, understanding inscrutable medical diseases, and contributing objective measures to help clinical assessment.

Current morphometry techniques often preclude direct biological interpretation of differences enabling classification. In these approaches, the models transforming images to a representation in feature domain are non-invertible; thus, statistical functions constructed in the feature domain cannot be mapped to images that illustrate discriminating changes. We seek a modeling approach that enables invertible, nonlinear transformation of data to a representation that streamlines information extraction through machine learning. Such an approach would also enable direct visual interpretation of the changes leading to sensitive classification or regression models.

Our technique, Transport-Based Morphometry (TBM), addresses the limitations of traditional morphometry approaches. TBM is based on the mathematics of optimal mass transport (OMT) and enables fully automated, data-driven analysis and statistical results that are easily interpreted biologically. We extend the mathematics of OMT to enable application of TBM to 3D radiology data. We apply TBM to achieve the state of the art in a variety of clinical applications.

Currently, diagnosis of osteoarthritis (OA) cannot be made until symptoms and irreversible damage on x-ray develop. TBM enables detection of OA three years in advance of symptoms with 86% accuracy based on the appearance of cartilage on knee MRIs. Focal damage in the medial condyle is identified as culprit for future progression to OA.

Copy number variants (CNV) in 16p11.2 chromosomal locus are associated with many neurodevelopmental diseases, including autism and epilepsy. TBM allows sensitive prediction of the 16p11.2 genotype based on brain structure alone – 100% accuracy using white matter appearance. TBM identifies changes in white matter distribution (deletion carriers > controls > duplication carriers). Furthermore, in addition to classification, the TBM approach also facilitates regression tasks, showing that aerobic fitness is associated with changes in brain tissue distribution in areas that overlap with those affected in normal aging. In the future, TBM has

Furthermore, in addition to classification, the TBM approach also facilitates regression tasks, showing that aerobic fitness is associated with changes in brain tissue distribution in areas that overlap with those affected in normal aging. In the future, TBM has potential to bridge the knowledge gap between structure and function in a wide range of diseases.

In the future, TBM has potential to bridge the knowledge gap between structure and function in a wide range of diseases.

Bio:

I am an MD-PhD student in the Medical Scientist Training Program (MSTP) at the University of Pittsburgh and Carnegie Mellon University. Currently I am in my third year of PhD at CMU, working under the supervision of Prof. Gustavo Rohde, who holds joint appointments in Electrical Engineering and Biomedical Engineering.

Prior to joining the MSTP program, I earned my B.S. and M.S. degrees in Electrical Engineering from Stanford University at the ages of 19 and 20, respectively.

My PhD research involves computer-aided extraction of anatomical information from high-resolution medical imaging data to aid in medical diagnostics. Interpreting subtle patterns in high resolution images eludes human visual inspection, yet we need to look across images from multiple subjects for data-driven learning – which is the subject of my research. This work has drawn attention from various bodies as I have received the Philip and Marsha Dowd Graduate student fellowship award, Hertz Foundation Finalist award, and a university-wide 3-minute thesis competition award. I am the author of 8 peer-reviewed publications, and of an additional 5 journal papers under various stages of review and preparation.

I am preparing to become a professor, a career path that I am motivated to pursue because it will enable me to combine my passion for image technology and signal processing research with my desire to improve patient care. My objective is to become a leading expert in biomedical imaging technology.

I became interested in pursuing an MD-PhD after I begin taking classes in signal processing. From k-space and Fourier transform in MRI, to projection slice in CT, I began to see the intimate relationship between signal processing theory and medical imaging. I subsequently pursued an internship at GE Healthcare, where I wrote software to remotely focus x-ray machines. There, I was inspired by the wide-reaching impact that engineers had on modern patient care.

However, I also became acutely aware of the gender imbalance in the fields I wanted to pursue. At Stanford, I only had two female EE professors – the rest were all male. Currently, in my spare time, I am a coordinator of the Women in Science and Medicine Association (WSMA). We work to connect young female scientists to mentors and discuss the challenges at the intersection of pursuing a career in academia and being a woman in STEM.