Dr. Kawin Setsompop

Professor

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Biography

Kawin Setsompop is a Professor of Radiology at Stanford University and a member of the Administrative Appointments. His research focuses on developing novel MRI acquisition methods and creating imaging technologies to improve understanding of brain structure and function for healthcare applications. After earning a Master’s degree in Engineering Science from Oxford University and a PhD in Electrical Engineering and Computer Science from MIT, he joined the A.A. Martinos Center for Biomedical Imaging as a postdoctoral fellow. Under his leadership, the group pioneered several widely-used MRI acquisition technologies, many of which have been successfully translated into FDA-approved clinical products for companies like Siemens, GE, Phillips, and Bruker. These technologies are essential for daily neuroscientific studies and clinical applications.

Research Interests

Magnetic Resonance Imaging Neuroimaging Biomedical Imaging Image Reconstruction Techniques

Patents

Sparse approximate encoding Wave-CAIPI: preconditioner noise reduction

US11035920 2021-06-15

Systems methods for fast magnetic resonance image reconstruction using heirarchically semiseparable solver

US10126397

System method reconstructing ghost-free images data acquired using simultaneous multislice magnetic resonance imaging

US10175328B2

Systems methods for statistical reconstruction of magnetic resonance fingerprinting data

US10241176B2

Hierrarchical mapping framework for coil compression magnetic resonance image reconstruction

US10310042B2

Systems methods for generalized slice dithered enhanced resolution magnetic resonance imaging

US10324149B2

System method for simultaneous multislice excitation using combined multiband periodic slice

US10345409B2

Systems methods for joint trajectory parallel magnetic resonance imaging optimization for auto-calibrated image reconstruction

US10408910B2

Method for increasing signal-to-noise ratio in magnetic resonance imaging using per-voxel noise

US10429475B2

Simultaneous multislice MRI random gradient encoding

US10436866B2

System method for simultaneous multislice magnetic resonance fingerprinting with variable radio frequency encoding

US10598747

Systems methods for removing background phase variations in diffusion-weighted magnetic resonance imaging

US10605882

Accelerated magnetic resonance imaging using tilted reconstruction kernel phase encoded point spread function encoded k-space

US10871534

Noise suppression wave-CAIPI

US10895622

Accelerated diffusion-weighted magnetic resonance imaging self-navigated, phase corrected titled kernel reconstruction phase encode point spread function encoded k-space

US10901061

Systems methods for slice dithered enhanced resolution simultaneous multislice magnetic resonance imaging

US10908248

Method for simultaneous time-interleaved multislice magnetic resonance imaging

US11009675

Method for echo planar time-resolved magnetic resonance imaging

US11022665

Multi-contrast MRI image reconstruction using machine learning

US11181598

Motion corrected blipped CAIPIRINHA SMS

US11249162

Reconstruction of magnetic-resonance datasets using machine learning

US11360176

Multi-shot echo planar imaging machine learning

US11391803

Phase estimation for retrospective motion correction

US11486953

Reconstruction Magnetic-Resonance Datasets using Machine Learning

US20200249301

Multi-contrast MRI Imaging Reconstruction using Machine Learning

US20200341094

Courses

BMP 269B EE 369B BMP 399 RAD 399 RAD 199 ENGR 199W EE 369C BMP 211 RAD 211

Requirements for Stanford University

Doctorate Program
Requirements
GPA Requirement
Required:3.5
TOEFL
Listening
Required:26
Reading
Required:26
Writing
Required:26
Speaking
Required:26
Total
Required:100
GRE General
Verbal
Required:160
Quantitative
Required:165
Analytical Writing
Required:4.5
Overall
Required:4.5
Prerequisites
Bachelor degree from an accredited institution Strong background in mathematics and programming
Application Checklist
  • Statement of Purpose
  • Three letters of recommendation
  • Official transcripts
  • Resume/CV
Specialization Notes

The Computer Science department emphasizes research potential. GRE General is currently optional but recommended for some tracks.