Radiological Sciences Laboratory Internship Projects

2020 RSL Undergraduate Research Summer Internship Program: 

Research Experience for Undergradutes (REU)

Due to Covid- 19 safety concerns, the RSL REU program has been restructured.  With the exception of limited number of continuing students working remotely, all participation in the program is cancelled.

Thank you for your interest in RSL- we look forward to inviting interns back next summer!  Prospecitve 2021 participants should look over the projects below and explore our lab websites.  Summer 2021 information will be available starting in late fall. 

Questions?

Please direct any questions concerning the REU Program, including the application process to bbonini@stanford.edu

Click HERE to view 2018 REU Program Highlights

Click HERE to view 2019 REU Program Highlights

PROJECTS

Check back in the Fall for 2021 updates

Faculty Mentor: Adam Wang, PhD

Internship Mentor: Adam Wang, PhD; TBN

IDENTIFIER: WangLab

Type of Laboratory Research: Dry lab

My group develops technologies for advanced x-ray and CT imaging, including novel system design, model-based image reconstruction, spectral imaging, and radiation transport methods. We develop these technologies in the Zeego Lab (a robotic C-arm that supports interventional imaging, animal studies, and more) and the Tabletop X-Ray CT Lab (for prototyping novel systems, custom geometries, and phantom experiments).

Project: 

The REU project will help us develop computational and visualization tools for understanding scattered dose in an interventional radiology environment. Clinicians routinely use x-ray imaging such as fluoroscopy and cone-beam CT, which exposes them to scattered radiation. Currently, there are few tools to monitor and display their real-time exposure to radiation. In this project, the student will perform Monte Carlo simulation of x-ray transport and use real-time dosimeters to verify the simulations.

Required skills:

Seeking highly motivated students enthusiastic about research. Suitable for students with EE, BioE, Physics, CS, or similar backgrounds. Familiarity with Matlab, Python, or C++ programming languages.

https://med.stanford.edu/wanggroup.html

Faculty Mentor: Daniel Spielman, PhD

Internship Mentor: Daniel Spielman, TBN

IDENTIFIER: SpielmanLab

Title: Magnetic resonance methods for measuring brain energy metabolism and oxidative stress.

Beyond anatomy, magnetic resonance imaging (MRI) and spectroscopy (MRS) provide unique opportunities for assessing metabolism within the body.   You will be joining a multidisciplinary research team with expertise spanning physics, electrical engineering, biochemistry, and clinical sciences, to help develop novel acquisition and analysis tools for assessing critical in vivo processes.  The specific project is the development of new data analysis methods and machine learning tools for the improved measurement of energy metabolism and oxidative stress in the human brain.  Primary applications of this work include brain tumor diagnosis, treatment monitoring, and prediction of response to therapy, and improved understanding and treatment of neurologic conditions including traumatic brain injury, Alzheimer's disease, schizophrenia, and autism.

Required Skills:

   Passion for research and willingness to take on new challenges

   Programming skills, e.g. Matlab

   https://med.stanford.edu/spielmangroup.html

Faculty Mentor: Kim Butts Pauly, PhD

Internship Mentor: Ningrui Li

IDENTIFIER: KBPLab

Type of Laboratory Research: Dry lab

Our lab studies novel therapies of focused ultrasound, a versatile technology with many applications, including non-invasively treating neurological diseases and cancer. We use image guidance to better plan, monitor, and evaluate treatment efficacy as well as to understand basic science mechanisms. By leveraging our own lab's scientific and engineering expertise and working in collaboration with other labs, our goal is to improve these focused ultrasound therapies as they become more commonly used in the clinic.

Title: Developing computational methods for safer and more effective transcranial ultrasound therapies

Project:

The skull is a formidable barrier to consistently safe and effective focused ultrasound therapies. Variations in skull geometry and composition across patients result in differences in the acoustic intensity, shape, and location of the focal spot! The goal of this project is to use computational methods to simulate acoustic transmission through the skull. Simulation results will validated by comparing them to physical measurements made using real skulls. However, the project aims are flexible and can be tailored to the interests of the student. For example, students interested in image processing or machine learning could forgo simulation methods in favor of developing deep learning models for predicting acoustic transmission through the skull.

Required Skills:

Programming skills in MATLAB and/or Python

https://med.stanford.edu/kbplab.html

Faculty Mentor: Jennifer McNab, Ph.D. 

Internship Mentor: Jennifer McNab, Ph.D., Christoph Leuze, Ph.D.

IDENTIFIER: McNabLab

Type of Laboratory Research: Dry lab or combination of wet and dry lab

Title: Developing Next Generation Neuronavigation Technology

Our mission is to develop magnetic resonance imaging (MRI) techniques that probe human brain tissue microstructure. This requires new MRI contrast mechanisms, strategic encoding and reconstruction schemes, physiological monitoring, brain tissue modeling and validation. Applications of these methods include neuronavigation, neurosurgical planning and the development of improved biomarkers for brain development, degeneration, disease and injury. In this project we will develop visualization techniques of MRI data using a see-through mixed reality display. MRI data will be processed and analyzed to extract relevant information such as functional or structural networks. The different types of MRI scans such as structural MRI, diffusion MRI and fMRI will then be visualized on the see-through display. 

https://med.stanford.edu/mcnablab.html

Faculty Mentor: Jeremy Dahl, PhD

Internship Mentor: Rehman Ali

IDENTIFIER: DahlLab

Internship Positions: 1 position available, undergraduate

Type of Laboratory Research: Dry lab

Our laboratory develops novel imaging algorithms and systems based on ultrasound.  Projects in our lab consist of novel beamforming algorithms to reduce image noise and improve overall image quality, development of high-sensitivity flow algorithms for the detection of small vessels in the placenta and neonatal brain, developement of speed of sound estimation techniques for improved image quality and quantification of disease, ultrasound molecular imaging for the early detection of cancer, ultrasound-guided drug delivery, fabrication of novel intravascular ultrasound transducers to perform radiation force imaging, and low-cost 3D imaging based on augmentation of 2D ultrasound systems.  The makeup of our laboratory personnel include engineers and physicists, and we collaborate extensively with molecular and cancer biologists, chemists, and clinicians.

Project:

We are currently developing techniques using pulse-echo ultrasound to measure the speed of sound of tissue.  Because the speed of sound is directly correlated with the fat concentration in the tissue, we aim to use this technology to measure steatosis of the liver in the diagnosis of Non-alcoholic Fatty Liver Disease (NAFLD).  Typical tasks involved in this project include (1) simulations of ultrasound fields and wave propagation, (2) experimental utilizing ultrasound systems to acquire raw ultrasound data from various media, (3) coding, post-processing , and tomographic image reconstruction using the raw ultrasound data to obtain sound speed estimates.

Required Skills:

Those best suited for our REU project are highly motivated and are very willing to learn new topics.  The project involves activities that are most familiar to electrical and biomedical engineers, however any highly motivated individual is welcome to apply.

http://med.stanford.edu/ultrasound.html

Faculty Mentor: Jeremy Dahl, PhD

Internship Mentor: Carl Herickhoff, PhD + IMMERS group

IDENTIFIER: immersgroup

Internship Positions: 1 position available, undergraduate

Type of Laboratory Research: Dry lab

Title: Augmented-Reality 3D Ultrasound Integration for Interventional Guidance

The Dahl lab develops ultrasound imaging technologies, from new beamforming algorithms to novel device designs (see http://ultrasound.stanford.edu), and the IMMERS group enables medical applications of augmented-reality (AR) to solve real challenges in healthcare (see http://immers.stanford.edu).

Project Overview: This project involves 3D reconstruction and augmented-reality (AR) visualization of ultrasound image volumes using a low-cost sensor-enabled probe. The project will have two major phases: the first phase involves calibration (of the trajectory measured by the low-cost sensors with that measured by a high-precision OptiTrack) and exporting and verifying reconstructed 3D image volumes; the second phase involves integrating the data stream into an augmented-reality display (Microsoft HoloLens or Magic Leap One) for interactive, real-time visualization of live-acquired ultrasound in 3D—which will be a powerful tool for interventional procedure guidance.

Required Skills: some proficiency at coding (C/C++ primarily); any experience with OpenGL, Matlab, handling multiple data streams, and AR/VR tools (e.g., Unity) would be a plus.

Faculty Mentor: Daniel Ennis, Ph.D.

Internship Mentor: TBN

IDENTIFIER: EnnisLab

Type of Laboratory Research: Dry lab

The Cardiac Magnetic Resonance (CMR) Group is led by Dr. Ennis in the Department of Radiology at Stanford University. His team develops translational cardiac and cardiovascular MRI techniques to improve clinical diagnosis. Major research projects for REU students focus on: 1) Developing MRI and 3D printing methods to estimate changes in heart and blood vessel stiffness; 2) Developing MRI methods that validate blood flow and motion measurements using 3D printed constructs; and 3) Develop software interfaces and analysis methods that enable the analysis of blood flow and heart motion from MRI data. 

Example Projects: 

1) Develop and refine image processing methods for measuring heart motion from MRI data.

2) Develop methods for 3D printing soft materials, then test their material stiffness using MRI.

3) Develop methods for 3D printing synthetic blood vessels, then measure blood flow using MRI.

Required Skills: Experience in building/making/crafting. An interest in image processing using computer programming (Matlab or Python). Curiosity, motivation, and problem-solving. An interest in cardiovascular physiology, bioengineering, and medical imaging.

http://med.stanford.edu/cmrgroup.html

Faculty Mentor: Gary Glover, PhD
Internship Mentor: Patricia Lan

IDENTIFIER: GloverLab

Type of Laboratory Research: Dry lab

Title:  Developing fMRI targets for HIFU ablative therapy in patients with essential tremor.

Synopsis: Patients with essential tremor have debilitating involuntary hyperactive motor movements, but can often be treated using (non-invasive) high intensity focused ultrasound (HiFU) to ablate the ventral intermediate thalamic nucleus (VIM) thought to be responsible for the tremor.  The VIM is a small deep-brain region that is presently targeted by anatomic measurements. During the treatment, ablation efficacy is currently monitored by visual observation of tremor intensity, and the target location is adjusted  during treatment to improve the ablative reduction in tremor.  During this process, other parts of the thalamus responsible for speech and motor kinesis can be inadvertently damaged.

In this project, we will optimize an fMRI technique to more accurately target the VIM using an accelerometer to monitor the patient’s tremor during a pre-therapy scan.  The fMRI target locations will be compared with the conventional targets and the final HIFU locations after the treatment to see whether the fMRI target would have been closer. The development will involve scanning normal volunteers to optimize the fMRI protocols and analysis methods, then scanning patients pre-treatment to determine the target for HiFU treatment, and finally analyzing the accuracy of the fMRI targets relative to the outcome after treatment with the conventional treatment plan.

Required Skills: Facility with fMRI scanning desired but not essential, basic signal processing and familiarity with Matlab.

http://rsl.stanford.edu/glover/

Faculty Mentor: Garry Gold, MD

Internship Mentor: Garry Gold, Feliks Kogan

IDENTIFIER: GoldLab

Type of Laboratory Research: Combination of wet and dry lab

The JOINT group works on orthopedic imaging, on projects designed to detect osteoarthritis, measure biomechanics, and improve diagnosis.  Our work includes basic physics and engineering, image acquisition, processing and analysis and testing methods in human subjects.  We use many computational methods including image analysis and machine learning.

 Projects

1)Segmentation of tissue, to aid in training of neural networks for classification

2) Study the effects of loading and exercise in human subjects

3) Better characterizes sports injuries and musculoskeletal disease with imaging

4) Developing improved imaging tools for joints, including weight bearing CT and PET-MR.

Required Skills:

Basic Biology, Physics, and Chemistry.  Computer skills are desired, such as Horos or Osirix.  Pre-medical students are welcome.

http://med.stanford.edu/jointgroup.html

Faculty Mentor:  Bruce Daniel MD

Internship Mentor:  Bruce Daniel MD, Brian Hargreaves Ph.D., Steffi Perkins MSBME

IDENTIFIER: BahLab

Type of laboratory research:  Dry lab.

Overview of lab work:   The IMMERS lab is the experimental arm of the Incubator for Medical Mixed and Extended Reality at Stanford.  Mixed-reality, sometimes called “Augmented-Reality” refers to the creation of virtual digital objects and content that are inserted into the real-world.  Our lab focuses on using state of the art head-mounted systems, including Microsoft HoloLens, and Magic Leap One, to bring 3D content based on medical imaging data into the workspace of physicians and patients, with the goal of improving patient care through better diagnosis, planning and execution of medical procedures.  From brain to breast to lungs to joints, we are leveraging the unique power of mixed-reality to tackle a wide variety of clinical scenarios.  Our approach centers on close collaboration between engineers, scientists, and expert clinicians and doctors.  We are team and often work together supporting one anothers projects.

Title: IMMERS Lab

Project:   The overarching goal of the project is to get facile with creating and evaluating medical mixed reality technologies.  One particular use case that we need help with is the evaluation of the anatomy of organs being considered for donation for transplant.   We want to know if 3D visualization can help surgeons make better plans for transplant surgery.  But there are a number of other projects spanning from basic mixed reality alignment problems through intraoperative use that REU students can potential join as well, depending on skills and interest.

Required Skills: Curiosity, altruism, friendliness, integrity and self-motivation are essential.  Basic familiarity with computers is a must.  Experience with the Unity programming environment and C# programming would help, but are not essential.  

IMMERS.stanford.edu

Faculty Mentor: Michael Moseley, PhD, Greg Zaharchuk, MD
Internship Mentor: Michael Moseley, Greg Zaharchuk

IDENTIFIER: CAFNLab

Type of Laboratory Research: Dry lab

The Center for Advanced Functional Neuroimaging (CAFN), directed by Greg Zaharchuk, MD, PhD and Michael Moseley, PhD, is part of Radiological Sciences Lab in the Department of Radiology at Stanford University's School of Medicine. We develop novel Magnetic Resonance Imaging (MRI) techniques to better understand human brain functions, delineate brain structures, and diagnose brain diseases. We drive key clinical areas of neuroimaging focusing on disease processes in stroke, brain tumors, and other cerebrovascular diseases using tissue perfusion mapping (PWI), diffusion MRI (DWI), as well as new fields of mapping the brain connectivity: diffusion tensor imaging (DTI), susceptibility-weighted MRI (SWI), and Quantitative Susceptibility Mapping (QSM). CAFN also develops and uses high-resolution quantitative diffusion tensors and perfusion maps to explore and map complex brain structure and function in active mental tasking, revealing new key findings in the developing and aging brain function for blood flow, tissue integrity, and cognition.

CAFN collaborates with researchers from the Stanford Stroke Center, Departments of Neurology, Neurosurgery, Psychiatry and Psychology, UC Berkeley, Lucille Packard Children’s Hospital, Palo Alto VA Medical Center and other research institutions to discover new ways of approaching the brain’s most complex problems. We also use many computational methods including Deep Learning and machine learning for improved neuroimaging.

Projects: Analysis of clinical brain images for segmentation of tissue and training of Deep Learning and neural networks for disease characterization.

Required Skills:

Matlab and/or python programming.

http://med.stanford.edu/cafn.html

Faculty Mentor: Raag Airan, MD, PhD

Internship Mentor: Raag Airan, MD, PhD

IDENTIFIER: AiranLab

Type of Laboratory Research: Combination of wet and dry lab

Overview of lab work: Our goal is to develop and clinically implement new technologies for high-precision and noninvasive interventions upon the nervous system. Every few millimeters of the brain is functionally distinct, and different parts of the brain may have counteracting responses to therapy. To meet this challenge, we have developed techniques that allow targeted delivery of drugs to the right part of the nervous system at the right time, by combining technologies like focused ultrasound and nanotechnology. 

Necessary skills: Our current projects utilize the full range of potential lab-based skills: materials science, analytical chemistry, animal surgery and handling, behavioral neuroscience, neurophysiology, functional neuroimaging, work with large animal translational models, hardware design and manufacturing, and computational analysis/modeling. The primary skills needed are a strong motivation/work ethic and willingness to get ones hands dirty (potentially literally) with experimental work.

URL: http://airan-lab.stanford.edu

Faculty Mentor: Feliks Kogan, PhD

Internship Mentor: Feliks Kogan

IDENTIFIER: KoganLab

Type of Laboratory Research: Dry Lab

Title: Evaluation of Bone Metabolism after Knee Loading

Description:  Our group works on the development and translation of novel imaging to study musculoskeletal function and detection of musculoskeletal disease at the earliest stage. In particular, we are developing a non-invasive method to evaluate the vascular and metabolic response of bone to acute stress. Bone stress is known to induce bone remodeling which, when abnormal may lead to skeletal fragility and bone and joint disease. Numerous methods can evaluate long-term bone adaptation through structure and mineral density. However, the acute response of loading in bone is still poorly understood, largely due to a dearth of methods to non-invasively measure bone physiology and function in humans in vivo.

In this project, we will develop new methods to study how the knee joint responds to an acute load induced by exercise. We will compare the metabolic response of bone measured with PET imaging to joint structure and microstructure imaged with MRI. This will include identification and segmentation of knee anatomy, analysis of quantitative MRI data, and development of new algorithms to study PET contrast agent uptake.

Required Skills: An interest in problem solving. Matlab or other programming skills are helpful.

http://med.stanford.edu/jointgroup.html

Faculty Mentor: Michael Zeineh, MD

Internship Mentor: TBD

IDENTIFIER: ZLab

Type of Laboratory Research: Dry lab or combination of wet and dry lab

Our lab focuses on translating cutting-edge imaging methods into clinical practice for the study of human brain. 

Our main research is concerned with brain changes during neurodegenerative diseases such as Alzheimer's disease and mild traumatic brain injury. We study Alzheimer's using the full arsenal of imaging: ultra-high resolution MRI coupled with novel histological methods including x-ray microscopy at SLAC (fluorescence, spectroscopy, scattering/diffraction) and electron microscopy. We bring this armamentarium full circle to living human imaging with high field MRI and combined PET-MR. We similarly study the effect of trauma to the brain in high-impact sports like football, evaluating the changes that occur to the brain in living players over time in response to head impacts. Finally, we are looking for imaging signatures of chronic fatigue syndrome to understand the source of their ongoing fatigue.

The student should expect to get exposed to a wide range of the newest methodologies used to image and study the brain, both living and post-mortem, get familiar with brain anatomy, and understand the consequences of disease or impact on our brains.

Motivated students of any scientific background and at any point in their studies* are welcome to apply, basic coding skills (Matlab/bash/python) and an interest in neuroscience are required.

*non-Stanford students must have 2 years of college experience

https://med.stanford.edu/zeinehlab.html