Radiology

My lab focuses on translating advanced MRI into clinical practice. In Alzheimer's disease, we are investigating the nature of iron deposition to understand how iron interacts with inflammation, amyloid, and tau in the progression of AD. We bring to this disease the full arsenal of imaging: ultra-high resolution MRI of human AD specimens coupled with novel histological methods including x-ray microscopy and electron microscopy. We bring this armamentarium full circle to living human imaging with 7.0T MR and multi-tracer PET-MR.

We are seeking a talented individual for a research associate position in our multidisciplinary team. Our advanced pediatric MRI research program spans across novel developments in hardware, pulse sequences, machine learning algorithms, image reconstruction methods, and image analysis techniques, all with an integrated clinical translational component. Efforts bridge across multiple departments on the Stanford University campus and UC Berkeley, as well as with Silicon Valley companies.

Dr. Spielman’s research is in the field of MRI, spectroscopy (MRS), and PET, with a focus on the development of new methods of imaging in vivo metabolism. Current projects include 13C MRS of hyperpolarized substrates for the assessment of glycolysis and oxidative phosphorylation in cancer, 1H MRS measurements brain oxidative stress and neurotransmission, and combined PET/MRS studies.  He has focused on a novel array of both acquisition and analysis techniques for use in preclinical and clinical studies.

Dr. Jeremy Heit is a neurointerventional surgeon (neurointerventional radiologist) who specializes in treating stroke, brain aneurysms, brain arteriovenous malformations, brain and spinal dural arteriovenous fistulae, carotid artery stenosis, vertebral body compression fractures, and congenital vascular malformations. Dr. Heit treats all of these conditions using minimally-invasive, image-guided procedures and state-of-the-art technology.

Daniel Ennis (Ph.D.) is an Associate Professor in the Department of Radiology. As an MRI scientist for nearly twenty years, he has worked to develop advanced translational cardiovascular MRI methods for quantitatively assessing structure, function, flow, and remodeling in both adult and pediatric populations. He began his research career as a Ph.D.

Cancer Imaging, Nanoparticles, MRI, PET/MR, Cancer Immunotherapy Imaging, Tumor Associated Macrophages, Stem Cell Tracking

My research interests revolve around the following areas: - Novel systems and methods for x-ray and CT imaging - Applications of x-ray/CT to image-guided interventions and therapy and diagnostic imaging - Dual energy / spectral imaging, including photon counting detectors - Applications of artificial intelligence / machine learning / deep learning to medical imaging - Monte Carlo and Deterministic methods for x-ray imaging and radiation dose - Model-based image reconstruction

Stoyanova lab is interested in understanding fundamental molecular mechanisms underlying the development of epithelial cancers and their utility as biomarkers and therapeutic targets. Currently, the major focus of our group is in prostate cancer. We are also interested in breast and neuroendocrine cancers. The ultimate goals of the laboratory are to: 1) improve the stratification of indolent from aggressive prostate cancer and 2) guide the development of novel and effective therapeutic strategies for metastatic cancers.

The Demirci Bio-Acoustic MEMS in Medicine Lab (BAMM) specializes in creating technologies to manipulate cells in nanoliter volumes to enable solutions for real world problems in medicine including applications in infectious disease diagnostics and monitoring for global health, cancer early detection, cell encapsulation in nanoliter droplets for cryobiology, and bottom-up tissue engineering.

The QIAI lab focuses on cutting‐edge research at the intersection of imaging science and biomedical informatics, developing and applying AI methods to large amounts of medical data for biomedical discovery, precision medicine, and precision health (early detection and prediction of future disease). The lab develops novel methods in text and image analysis and AI, including multi-modal and multi-task learning, weak supervision, knowledge representation, natural language processing, and decision theory to tackle the challenges of leveraging medical Big Data.