Professional Education


  • Doctor of Philosophy, Stanford University, BIOE-PHD (2022)
  • Master of Science, Stanford University, BIOE-MS (2017)

Stanford Advisors


Current Research and Scholarly Interests


My ongoing dissertation work is focused on the integration of multimodal, multiscale imaging modalities to further characterize the mechanisms underlying neurodegeneration in Alzheimer’s disease. My work is specifically aiming to use a range of imaging tools both in vivo and ex vivo, including high field MRI, PET-MRI, X-ray microscopy, electron microscopy, and histology, to better understand the roles of iron and inflammation in Alzheimer’s disease and how these factors interact with the hallmark pathologies (amyloid plaques and neurofibrillary tangles). Currently, I am working on developing a novel pipeline to coregister histology, specimen MRI, and microscopy to enable spatiotemporal analysis of these various pathological features across the time course of disease. A manuscript detailing early results of using advanced electron microscopy techniques to confirm the presence and inflammatory oxidative state of iron in human Alzheimer’s disease brain specimens is currently in preparation. Additionally, I am working on improved hardware for motion correction on high-field MRI and MR-PET data to translate early ex vivo findings into robust neuroimaging biomarkers. Successful completion of this work will facilitate the development of novel neuroimaging biomarkers that can improve diagnosis and assess treatment response in patients and also the identification of novel molecular targets for disease-modifying therapies.

Concurrently, I am PI on a project funded by the Stanford SPARK Translational Research Program aimed at discovering and developing small molecules to a novel molecular target to treat neuropsychiatric and neurodegenerative conditions. This work stems from the observations of myself and my colleagues and involves the development of novel molecule screening assays and concurrent in vivo validation of our target using a novel PET tracer. As the target is relevant in Alzheimer’s disease, this work has the potential to serve as a possible strategy for modulating the pathology and biomarkers that we discover with my imaging studies, directly tying into my thesis work,.

All Publications


  • Rigid Motion Correction for Brain PET/MR Imaging Using Optical Tracking IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES Spangler-Bickell, M. G., Khalighi, M., Hoo, C., DiGiacomo, P., Maclaren, J., Aksoy, M., Rettmann, D., Bammer, R., Zaharchuk, G., Zeineh, M., Jansen, F. 2019; 3 (4): 498–503
  • Rigid Motion Correction for Brain PET/MR Imaging using Optical Tracking. IEEE transactions on radiation and plasma medical sciences Spangler-Bickell, M. G., Khalighi, M. M., Hoo, C., DiGiacomo, P. S., Maclaren, J., Aksoy, M., Rettmann, D., Bammer, R., Zaharchuk, G., Zeineh, M., Jansen, F. 2019; 3 (4): 498-503

    Abstract

    A significant challenge during high-resolution PET brain imaging on PET/MR scanners is patient head motion. This challenge is particularly significant for clinical patient populations who struggle to remain motionless in the scanner for long periods of time. Head motion also affects the MR scan data. An optical motion tracking technique, which has already been demonstrated to perform MR motion correction during acquisition, is used with a list-mode PET reconstruction algorithm to correct the motion for each recorded event and produce a corrected reconstruction. The technique is demonstrated on real Alzheimer's disease patient data for the GE SIGNA PET/MR scanner.

    View details for DOI 10.1109/TRPMS.2018.2878978

    View details for PubMedID 31396580

    View details for PubMedCentralID PMC6686883

  • A Lagrangian cylindrical coordinate system for characterizing dynamic surface geometry of tubular anatomic structures. Medical & biological engineering & computing Lundh, T., Suh, G., DiGiacomo, P., Cheng, C. 2018

    Abstract

    Vascular morphology characterization is useful for disease diagnosis, risk stratification, treatment planning, and prediction of treatment durability. To quantify the dynamic surface geometry of tubular-shaped anatomic structures, we propose a simple, rigorous Lagrangian cylindrical coordinate system to monitor well-defined surface points. Specifically, the proposed system enables quantification of surface curvature and cross-sectional eccentricity. Using idealized software phantom examples, we validate the method's ability to accurately quantify longitudinal and circumferential surface curvature, as well as eccentricity and orientation of eccentricity. We then apply the method to several medical imaging data sets of human vascular structures to exemplify the utility of this coordinate system for analyzing morphology and dynamic geometric changes in blood vessels throughout the body. Graphical abstract Pointwise longitudinal curvature of a thoracic aortic endograft surface for systole and diastole, with their absolute difference.

    View details for PubMedID 29500737