Brian Hargreaves
Stanford University · Rheumatology
Active 1998–2024
About
Brian Hargreaves is a Professor of Radiology (Radiological Sciences Laboratory) and, by courtesy, of Electrical Engineering and of Bioengineering at Stanford University. He is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI). His research focuses on the application of artificial intelligence in medicine and imaging, contributing to the advancement of AI-driven healthcare solutions. As a faculty member at Stanford, he is involved in various educational and research initiatives aimed at integrating AI technologies into medical imaging and radiology, supporting the development of innovative tools and methodologies in the field.
Research topics
- Computer Science
- Artificial Intelligence
- Medicine
- Physics
- Telecommunications
- Anatomy
- Engineering
- Radiology
- Materials science
- Computer vision
- Optics
- Pathology
Selected publications
3D Diffusion-Prepared FLASH for Prostate MRI Near Metallic Implants
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024 · 1 citations
Senior authorCorresponding- Computer Science
- Materials science
- Computer Science
Motivation: Diffusion weighted imaging (DWI) is critical for prostate MRI, but echo planar imaging (EPI) DWI suffers from severe distortion in patients with hip implants or excessive bowel gas, often rendering images unusable. Goal(s): To develop 3D DWI of the prostate with minimal distortion from nearby metal. Approach: We compared a diffusion prepared, phase-navigated, fast low-angle shot (FLASH) readout sequence to EPI DWI to assess image quality, distortion correction and diffusion contrast. Results: Phantom experiments demonstrate the reduced FOV, distortion correction and ADC accuracy. Images in human subjects avoid the severe signal loss and distortion of EPI DWI in the subject with metal. Impact: We showed that 3D diffusion-prepared FLASH enables DWI where EPI often fails near hip replacements. Increased robustness of prostate imaging protocols to the presence of metal and/or bowel gas may obviate the need for contrast injection in these patients.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
- Computer Science
- Artificial Intelligence
- Computer Science
Motivation: DWI is effective for cancer imaging, but conventional EPI suffers from geometric distortion and chemical shift artifacts. Conventional fat suppression techniques are sensitive to the large B0 and B1+ inhomogeneities in the body. Residual fat causes artifacts and is a confounding factor in using DWI for cancer diagnosis. Goal(s): Perform robust fat/water separation in distortion-free DWI. Approach: A diffusion-weighted EPTI acquisition and joint reconstruction method is used. Separation is performed using chemical shift encoding along the temporal dimension. A distortion-less FSE-based phase navigator is used to resolve shot-to-shot phase. Results: The proposed method is validated in vivo in the brain, head&neck, and breast. Impact: Using the proposed navigated EPTI sequence, we demonstrated fat/water separated DWI that is robust to B0 variation in the body. This will enable more reliable use of DWI to assess cancer and other abnormalities, complementing or replacing contrast-enhanced imaging.
Osteoarthritis and Cartilage · 2020 · 4 citations
- Artificial Intelligence
- Computer Science
- Artificial Intelligence
Magnetic Resonance in Medicine · 2020 · 43 citations
- Computer Science
- Artificial Intelligence
- Computer Science
PURPOSE: To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning. METHODS: An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation. RESULTS: The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts. CONCLUSION: A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.
Journal of Magnetic Resonance Imaging · 2020 · 31 citations
- Artificial Intelligence
- Computer Science
- Nuclear medicine
BACKGROUND: Diffusion-weighted imaging (DWI) has shown promise to screen for breast cancer without a contrast injection, but image distortion and low spatial resolution limit standard single-shot DWI. Multishot DWI methods address these limitations but introduce shot-to-shot phase variations requiring correction during reconstruction. PURPOSE: To investigate the performance of two multishot DWI reconstruction methods, multiplexed sensitivity encoding (MUSE) and shot locally low-rank (shot-LLR), compared to single-shot DWI in the breast. STUDY TYPE: Prospective. POPULATION: A total of 45 women who consented to have multishot DWI added to a clinically indicated breast MRI. FIELD STRENGTH/SEQUENCES: -weighted imaging, and contrast-enhanced MRI at 3T. ASSESSMENT: Three blinded observers scored images for 1) general image quality (perceived signal-to-noise ratio [SNR], ghosting, distortion), 2) lesion features (discernment and morphology), and 3) perceived resolution. Apparent diffusion coefficient (ADC) of the lesion was also measured and compared between methods. STATISTICAL TESTS: Image quality features and perceived resolution were assessed with a mixed-effects logistic regression. Agreement among observers was estimated with a Krippendorf's alpha using linear weighting. Lesion feature ratings were visualized using histograms, and correlation coefficients of lesion ADC between different methods were calculated. RESULTS: MUSE and shot-LLR images were rated to have significantly better perceived resolution (P < 0.001), higher SNR (P < 0.005), and a lower level of distortion (P < 0.05) with respect to single-shot DWI. Shot-LLR showed reduced ghosting artifacts with respect to both MUSE (P < 0.001) and single-shot DWI (P < 0.001). Eight-shot DWI had improved perceived SNR and perceived resolution with respect to four-shot DWI (P < 0.005). DATA CONCLUSION: Multishot DWI enables increased resolution and improved image quality with respect to single-shot DWI in the breast. Shot-LLR reconstructs multishot DWI with minimal ghosting artifacts. The improvement of multishot DWI in image quality increases with an increased number of shots. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 2.
Recent grants
High-Resolution Breast MRI at 3.0T
NIH · $6.9M · 2020–2024
NIH · $438k · 2012
MRI near Total Joint replacements
NIH · $2.4M · 2014–2020
Frequent coauthors
- 30 shared
Vera A. Khokhlova
Lomonosov Moscow State University
- 26 shared
Cyril Lafon
Inserm
- 20 shared
Mickaël Tanter
Inserm
- 20 shared
Oleg A. Sapozhnikov
University of Washington Applied Physics Laboratory
- 20 shared
Ari Partanen
Profound Medical (Canada)
- 18 shared
Alexander Volovick
InSightec (Israel)
- 18 shared
David Melodelima
Laboratoire des applications Thérapeutiques des Ultrasons
- 18 shared
Benoit Larrat
CEA Paris-Saclay
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