Larson Group - Advanced Imaging Technologies

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.

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

Hyperpolarized carbon-13 metabolic MRI

Enables non-invasive, direct measurements of metabolism using "Hyperpolarized" contrast agents, with applications such as cancer staging and monitoring treatment response. Clinical trials are ongoing worldwide with this technology.

Semi-Solid MRI

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

PET/MRI

Hybrid PET/MRI systems are recently introduced commercially, and combine the functional information from PET tracers with the soft-tissue contrast from MRI. We are working on motion management, quantitatifive imaging, and lung imaging for this emerging 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

Publications

Larson Group Publications on Pubmed

We have fun with spin physics and RF pulses

News

Larson Group News

Team Members

.

	Peder Larson, PhD Associate Professor Principal Investigator [email protected] Dr. Peder Larson got his PhD under Dwight Nishimura from the Department of Electrical Engineering at Stanford University on "MRI of Semi-solid Tissues" in 2007. His research interests are in RF pulse design, pulse sequence development, novel imaging strategies, and optimized reconstruction methods for MRI, with an emphasis on applications in Hyperpolarized carbon-13 agents and semi-solid tissue imaging with ultrashort echo time (UTE) methods.
	Nick Dwork Postdoctoral Scholar Dr. Nicholas Dwork completed his PhD in Electrical Engineering from Stanford University with Prof. John Pauly working on acquisition and reconstruction of MRI and optical imaging data. He is working on novel methods for hyperpolarized C-13 MRI acquisition and reconstruction.
	Manuska Vaidya Postdoctoral Scholar Dr. Vaidya received her PhD at the NYU School of Medicine under the mentorship of Ricardo Lattanzi, Graham Wiggins, and Dan Sodickson. She has extensive experience in RF coil design and evaluation of MRI evaluations, and is currently working on improved RF hardware and pulse sequences for brain tumor imaging with hyperpolarized carbon-13 MRI.
	Abhejit Rajagopal Postdoctoral Scholar Dr. Rajagopal received his PhD in 2019 from UC Santa Barbara under the mentorship of Shivkumar Chandrasekaran and Hua Lee. His thesis focused on the role of approximation theory in imaging and recognition algorithms. Since joining UCSF, Dr. Rajagopal has been working on machine learning techniques for enhanced PET/MRI reconstruction, prostate cancer grading, and techniques for quantifying generalization in deep learning.
	Xiaoxi Liu Postdoctoral Scholar Dr. Xiaoxi Liu received her PhD in Diagnostic Radiology from the University of Hong Kong with Dr. Hing Chiu Chang. She has extensive experience in sequence design with Philips pulse programming environment and imaging reconstruction in the diffusion imaging field. She is currently working on pulse sequences and imaging reconstruction for liver tumor imaging with hyperpolarized carbon-13 MRI.
	Kirti Magudia Clinical Fellow Dr. Magudia received her graduate training in the Weill Cornell/ Rockefeller/ Sloan-Kettering Tri-Institutional MD-PhD Program, where her PhD thesis focused on developing a 3D cell culture model of colon epithelial tumorigenesis. She completed her Radiology residency at Brigham and Women's Hospital, where she worked at the MGH & BWH Center for Clinical Data Science applying machine learning methods to define population-based normal values of body composition. She is currently a Clinical Fellow at UCSF and working on appyling deep learning for improved porstate cancer classification.
	Andrew Leynes Bioengineering Graduate Student Andrew Leynes is a graduate of the UCSF Masters of Biomedical Imaging Program and completed his PET/MR Master’s Thesis on “Tissue Segmentation and Classification for PET/MR MR-based Attenuation Correction using Zero-Echo Time (ZTE) MRI” in 2015. He is working on advanced methods for quantitative PET/MRI, high-field (7T) MRI, and RF coil design projects.
	Nikhil Deveshwar Graduate Student Nikhil Deveshwar is a graduate of the UCSF Master's of Biomedical Imaging program who is working as a research assistant. His work is focused on analysis of myelin imaging methods as measured by UTE and diffusion MRI.
	Sule Sahin Bioengineering Graduate Student Sule Sahin is a graduate student in the UC Berkeley - UCSF Graduate Group in Bioengineering. She is currently working on quantification and modelling of hyperpolarized carbon-13 imaging studies.
	Jessica Scholey Bioengineering Graduate Student Jess received her master's degree in Medical Physics and clinical residency training in Radiation Oncology from the University of Pennsylvania. She is currently a Board-Certified Medical Physicist and Bioengineering PhD student, where she focuses on MRI-applications in Radiation Oncology, specifically implementing sequence-based and deep learning-based approaches used for Radiotherapy dose calculation and incorporating these approaches into the clinical workflow.
	Fei Tan Bioengineering Graduate Student Fei Tan is a graduate student in the UC Berkeley - UCSF Bioengineering program. Her work focuses on pulmonary ventilation analysis with ultrashort echo (UTE) proton MR.

Alumni

	Peng Cao, PhD Postdoctoral Scholar Dr. Peng Cao was a postdoctoral scholar in the Larson Group at UCSF from 2014-2018, where he worked on a broad range of advanced imaging projects including advanced hyperpolarized carbon-13 MRS and MRI methods. He became an Assistant Professor at The University of Hong Kong in 2018.
	Jeremy Gordon, PhD Postdoctoral Scholar & Senior Development Engineer Dr. Jeremy Gordon came to UCSF from UW-Madison as a postdoctoral scholar in 2013 and he pioneered imaging methods and experimental protocols for human hyperpolarized carbon-13 metabolic MRI studies. He became an Assistant Professor at UCSF in 2020.
	Shuyu Tang Bioengineering Graduate Student Shuyu Tang was a graduate student in the UC Berkeley - UCSF Graduate Group in Bioengineering, and a graduate of the UCSF Master's of Biomedical Imaging program. He completed his thesis “Improved Acquisition Methods for Hyperpolarized Carbon-13 Magnetic Resonance Imaging” in 2019 and became an MRI research scientist in industry.
	Xucheng Zhu Bioengineering Graduate Student Xucheng Zhu was a graduate student in the UC Berkeley - UCSF Graduate Group in Bioengineering. He worked on Motion Correction for lung imaging and dynamic hyperpolarized imaging strategies. He completed his thesis “Advanced 1H Lung MRI” in 2020 and became an MRI research scientist in industry.
	Wenwen Jiang Bioengineering Graduate Student Wenwen Jiang got her PhD in 2017 in the UC Berkeley - UCSF Graduate Group in Bioengineering, working jointly with Prof. Larson at UCSF and Prof. Michael Lustig at UC Berkeley on "Rapid and Robust Non-Cartesian Magnetic Resonance Imaging Methods". Upon graduation, she became an imaging scientist in industry.
	Tanguy Boucneau Visiting Graduate Student Tanguy Boucneau was a visiting graduate student in our group from the Ecole Normale Superior (ENS)-Cachan in France, working on ultrashort echo time (UTE) MRI methods for imaging myelin in the brain. He subsequently completed his PhD at the Unviersité Paris-Saclay.
	Dharshan Chandramohan Associate Specialist Dharshan Chandramohan worked on quantitative cancer imaging methods using PET/MRI and to improve radiation therapy planning. He continued on to medical school in 2020.
	Sonam Machingal Biomedical Imaging Graduate Student Sonam is a graduate of the UCSF Master’s of Biomedical Imaging Program. She completed her Master’s thesis on “Sampling Strategies for Hyperpolarized Carbon-13 Dynamic Imaging” in 2014 under Dr. Larson.
	Naeim Bahrami Biomedical Imaging Graduate Student Naeim is a graduate of the the UCSF Master's of Biomedical Imaging program. He completed a Master’s thesis on “Modeling Hyperpolarized 13C Pyruvate and Urea Concentration Kinetics With Multibanded RF Excitation MRI In Prostate Cancer” in 2013, and went on to receive his PhD at Virginia Tech.
	Yan Ann Xing Biomedical Imaging Graduate Student Ann was in the inaugural class of the UCSF Master's of Biomedical Imaging program. She completed her Master's thesis on "Optimal Variable Flip Angle Schemes For Dynamic Acquisition Of Exchanging Hyperpolarized Substrates" in 2012 under Dr. Larson.
	Qing Dai Biomedical Imaging Graduate Student Qing is a graduate of the the UCSF Master's of Biomedical Imaging program. He completed a Master’s thesis on “Clear Cell Renal Cell Carcinoma: Deep Learning-Based Prediction of Tumor Grade from Contrast-Enhanced CT” in 2019, and worked in the group for one year before returning to UCLA to pursue his PhD.

Search form

You are here