Larson Advance Imaging Group

Our research group takes an engineering-driven approach to develop advanced MRI methods. We have projects to develop new image contrast methods (e.g. lung imaging, myelin imaging), combine MRI with other technologies (e.g. hyperpolarized metabolic contrast agents, PET/MRI, radiation therapy planning), and develop intelligent MRI scanners. We use tools such as signal processing, real-time software, and deep learning, and apply these methods for human studies in oncology, pulmonology, and neurology. We are based in Byers Hall at the UCSF Mission Bay campus, as a part of the Quantitative Biosciences Institute (QBI) at UCSF. The primary facilities available for research include 3T and 7T MRI systems, Hyperpolarizers, an electronics shop, and a machine shop, all of which are part of the Surbeck Laboratory for Advanced Imaging and are supported in part by the NIH-funded Hyperpolarized MRI Technology Resource Center. We are also actively involved in development of technology for PET/MR systems, using the time-of-flight PET with 3-Tesla MRI at China Basin in collaboration with the UCSF Radiology Physics Research Laboratory.

Larson Advance Imaging Group Research Resources

We are looking for strong PhD graduate student and postdoctoral candidates, please contact if interested

Hyperpolarized carbon-13 metabolic MRI

This technology uses non-toxic, non-ionizing contrast agents to provide unique metabolism information, and is currently in clinical trials. Our team develops data acquisition, image reconstruction, and data analysis methods for this technology for a broad range of collaborators and applications. We are also extremely excited to be leading new patient studies of this technology in renal cancers and heart disease.

Semi-solid tissue MRI: Myelin, tendons, and Lung

Our team develops specialized acquisition and reconstruction methods to provide signal in MRI from semi-solid tissues such as tendons, bone, lung, and myelin, all of which are invisible with conventional MRI. For lung MRI, we are pursuing development of functional imaging biomarkers and translation into pediatric studies to reduce radiation dose compared to CT. For myelin MRI, we are developing quantitative imaging methods and translating into multiple sclerosis studies.

PET/MRI

Hybrid PET/MRI systems combine the functional information from PET tracers with the soft-tissue contrast from MRI. Our team is working on a range of technology developments for motion management, quantitative imaging, and multi-modal data analysis with this modality.

Advanced Imaging Techniques for Radiation Oncology

Applications of MRI in radiotherapy have increased significantly over the past decade due to the high level of soft tissue provided, often allowing for better visualization of tumors and organs at risk versus computed tomography (CT). The ability to use MRI for quantifying certain parameters of biological tissue used for calculating the dose delivered during radiotherapy is a critical step towards MRI-only treatment planning.

Our group is working to develop specialized MR techniques to accurately estimate parameters used for radiotherapy dose calculation. For example, inaccuracies in calculating material stopping power ratios contribute to “range uncertainties” in proton radiotherapy and is a limitation in treating with this modality. Collaborating with colleagues in the Department of Radiation Oncology, we recently developed and validated the “UC Method” for calculating proton stopping power ratios using a combination of specific MRI sequences and CT, which was shown to be of equal or superior accuracy than the clinical standard for the tissue types evaluated.

Figure1 : Axial representations of a) an example phantom configuration containing tissue substitute and calibration materials with corresponding images of b) zero-echo time (¹H) proton density-weighted MRI, c) Dixon water-only MRI, d) kilovoltage CT, and e) megavoltage CT.

Figure 2: Results of stopping power ratios computed using the UC Method with MRI and kilovoltage CT (left), UC Method with MRI and megavoltage CT (middle), and the clinical standard stoichiometric method (right) compared to physical measurements.

References:

Scholey, J.E., Chandramohan, D., Naren, T., Liu, W., Larson, P.E.Z. and Sudhyadhom, A. (2020), A methodology for improved accuracy in stopping power estimation using MRI and CT. Med Phys. https://doi.org/10.1002/mp.14555