Radiology
Showing 61-80 of 87 Results
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Liyan Sun
Postdoctoral Scholar, Radiological Sciences Laboratory
Current Research and Scholarly InterestsPhysics-driven deep learning algorithms for MRI/CT reconstruction and analysis:
(1) MRI acceleration with partial measurements.
(2) Medical image segmentation under limited data resources.
(3) Unsupervised/supervised medical image synthesis for MRI or CT.
(4) Longitudinal medical data analysis with deep learning models.
(5) PET image reconstruction and analysis. -
Simon Thalén
Postdoctoral Scholar, Radiological Sciences Laboratory
BioI am a clinical physiology resident at Karolinska University Hospital and completed my thesis on cardiovascular magnetic resonance imaging (MRI). With a background in mathematics, I am trying to live at the intersection of mathematics, technology, and medicine. My thesis focused on MRI evaluation of constrictive heart diseases, such as pericardial effusion and constrictive pericarditis. I used phase contrast MRI to measure respiratory variation in mitral and tricuspid peak early blood flow velocities and T1 mapping to characterize pericardial effusion fluid.
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Matheus Tonholo Ikedo
Postdoctoral Scholar, Radiology
BioMatheus Tonholo Ikedo is a Postdoctoral Research Fellow at Stanford University’s Department of Radiology, where he conducts research under the guidance of Dr. Bruno P. Soares. His academic interests lie at the intersection of pediatric neuroradiology and artificial intelligence, specifically focusing on how AI-driven tools can optimize magnetic resonance imaging (MRI) diagnostics and improve healthcare delivery for neuropediatric patients.
A Brazilian-trained physician, Matheus earned his medical degree from the Federal University of São Paulo (UNIFESP) and completed his Radiology residency at the University of São Paulo (USP), where he was recognized with the Guerbet-InRad Best Resident Award in his final year. -
Henk van Voorst
Postdoctoral Scholar, Radiology
BioDr. van Voorst is a postdoctoral scholar in Radiology studying the interfaces of artificial intelligence and neuroradiological imaging in stroke. Originally educated as an MD, Dr. van Voorst gained additional degrees in Finance and Data Science. As a PhD student, Dr. van Voorst focused on cost-effectiveness modeling and developed machine learning and deep learning algorithms with applications in acute ischemic stroke imaging. In his current research, Dr. van Voorst develops artificial intelligence algorithms to automatically extract information from arteries and veins in radiological stroke imaging.
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Chong Wang
Postdoctoral Scholar, Radiology
BioI am currently a Postdoctoral Scholar in the Department of Radiology at Stanford University School of Medicine, affiliated with the Center for Artificial Intelligence in Medicine and Imaging (AIMI). My research mainly focuses on AI and foundation models in healthcare, with an emphasis on developing trustworthy, robust, and efficient AI solutions for medical imaging. I earned my Ph.D degree, honored with the Doctoral Research Medal, in Computer Science from the Australian Institute for Machine Learning (AIML), The University of Adelaide.
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Jie Wang
Postdoctoral Scholar, Radiology
BioDr. Jie Wang is deeply passionate about magnetic nanotechnology, including magnetic resonance imaging (MRI), magnetic particle imaging (MPI), magnetic nanoparticles (MNPs), magnetic nanofluid hyperthermia (MNFH), magnetic biosensors, etc., for biomedical applications. His dissertation focuses on MRI-guided magnetic hyperthermia for cancer theranostics. Currently, his research interests include developing enzyme-activable nanoparticles for brain cancer theranostics and employing multi-modal imaging modalities to investigate the interaction between nanoparticles and biosystems (nano-bio interaction) within tumor microenvironment.
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Philipp Wesp
Postdoctoral Scholar, Radiology
BioI am a postdoctoral researcher investigating interpretable machine learning (ML) and large language model (LLM) applications in clinical radiology. My current research focuses on two complementary areas: understanding what human-interpretable concepts self-supervised vision foundation models learn through mechanistic interpretability techniques like sparse autoencoders, and developing LLM-based systems, including agentic workflows and retrieval augmented generation (RAG) architectures, that leverage unstructured hospital data to improve radiological workflows. I earned my PhD from LMU Munich, where I focused on clinically motivated machine learning applications in medical imaging in the Department of Radiology.
My work is partially funded by a Walter Benjamin Fellowship from the DFG (German Research Foundation). -
McKenzie White
Postdoctoral Scholar, Radiology
BioI work at the intersection of machine learning, medical imaging, and biomechanics. I'm committed to developing tools that bridge gaps between computational methods, musculoskeletal research, and clinical care - enabling more precise analyses, efficient workflows, and improved surgical decision-making.
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Wesley Williams
Postdoctoral Scholar, Radiology
Current Research and Scholarly InterestsFirstly, a goal of mine is to fashion a novel scatter-based parameter for PET reconstruction algorithms to improve image resolution via determining a more detailed scatter/true ratio estimate via binning the photons that have scattered once, twice, and perhaps, many more times.
Secondly, AI drug discovery application towards radiotracers may quicken experimentation by determining the formulations worth trying. Moreover, it may be able to characterize efficacy (biodistribution) (self-update).
