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Akademický článek

Coil sketching for computationally efficient MR iterative reconstruction.

Autor: Oscanoa JA; Department of Bioengineering, Stanford University, Stanford, California, USA.; Department of Radiology, Stanford University, Stanford, California, USA., Ong F; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Iyer SS; Department of Electrical Engineering and Computer Science, Massachussetts Institute of Technology, Cambridge, Massachussetts, USA., Li Z; Department of Radiology, Stanford University, Stanford, California, USA., Sandino CM; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Ozturkler B; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Ennis DB; Department of Radiology, Stanford University, Stanford, California, USA., Pilanci M; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Vasanawala SS; Department of Radiology, Stanford University, Stanford, California, USA.

Publikováno v: Magnetic resonance in medicine [Magn Reson Med] 2024 Feb; Vol. 91 (2), pp. 784-802. Date of Electronic Publication: 2023 Oct 17.

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Akademický článek

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

Autor: Desai AD; Department of Electrical Engineering, Stanford University, Stanford, California, USA.; Department of Radiology, Stanford University, Stanford, California, USA., Ozturkler BM; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Sandino CM; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Boutin R; Department of Radiology, Stanford University, Stanford, California, USA., Willis M; Department of Radiology, Stanford University, Stanford, California, USA., Vasanawala S; Department of Radiology, Stanford University, Stanford, California, USA., Hargreaves BA; Department of Electrical Engineering, Stanford University, Stanford, California, USA.; Department of Radiology, Stanford University, Stanford, California, USA., Ré C; Department of Computer Science, Stanford University, Stanford, California, USA., Pauly JM; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Chaudhari AS; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Biomedical Data Science, Stanford University, Stanford, California, USA.

Publikováno v: Magnetic resonance in medicine [Magn Reson Med] 2023 Nov; Vol. 90 (5), pp. 2052-2070. Date of Electronic Publication: 2023 Jul 10.

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Deep Learning Initialized Compressed Sensing (Deli-CS) in Volumetric Spatio-Temporal Subspace Reconstruction.

Autor: Iyer SS; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, MA, USA.; Department of Radiology, Stanford University, CA, USA., Schauman SS; Department of Radiology, Stanford University, CA, USA., Sandino CM; Department of Electrical Engineering, Stanford University, CA, USA., Yurt M; Department of Electrical Engineering, Stanford University, CA, USA., Cao X; Department of Radiology, Stanford University, CA, USA., Liao C; Department of Radiology, Stanford University, CA, USA., Ruengchaijatuporn N; Center of Excellence in Computational Molecular Biology, Chulalongkorn University, Bangkok, Thailand.; Center for Artificial Intelligence in Medicine, Chulalongkorn University, Bangkok, Thailand., Chatnuntawech I; National Nanotechnology Center, National Science and Technology Development Agency, Pathum Thani, Thailand., Tong E; Department of Radiology, Stanford University, CA, USA., Setsompop K; Department of Radiology, Stanford University, CA, USA.; Department of Electrical Engineering, Stanford University, CA, USA.

Publikováno v: BioRxiv : the preprint server for biology [bioRxiv] 2023 Mar 28. Date of Electronic Publication: 2023 Mar 28.

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Akademický článek

Accelerated two-dimensional phase-contrast for cardiovascular MRI using deep learning-based reconstruction with complex difference estimation.

Autor: Oscanoa JA; Department of Bioengineering, Stanford University, Stanford, California, USA.; Department of Radiology, Stanford University, Stanford, California, USA., Middione MJ; Department of Radiology, Stanford University, Stanford, California, USA., Syed AB; Department of Radiology, Stanford University, Stanford, California, USA.; Cardiovascular Institute, Stanford University, Stanford, California, USA., Sandino CM; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Vasanawala SS; Department of Radiology, Stanford University, Stanford, California, USA., Ennis DB; Department of Radiology, Stanford University, Stanford, California, USA.; Cardiovascular Institute, Stanford University, Stanford, California, USA.

Publikováno v: Magnetic resonance in medicine [Magn Reson Med] 2023 Jan; Vol. 89 (1), pp. 356-369. Date of Electronic Publication: 2022 Sep 12.

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Akademický článek

Upstream Machine Learning in Radiology.

Autor: Sandino CM; Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA., Cole EK; Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA., Alkan C; Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA., Chaudhari AS; Department of Biomedical Data Science, 1201 Welch Road, Stanford, CA 94305, USA; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Loening AM; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Hyun D; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Dahl J; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Imran AA; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Wang AS; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Vasanawala SS; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA. Electronic address: admin_vasanawala@stanford.edu.

Publikováno v: Radiologic clinics of North America [Radiol Clin North Am] 2021 Nov; Vol. 59 (6), pp. 967-985.

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Akademický článek

Free-breathing Accelerated Cardiac MRI Using Deep Learning: Validation in Children and Young Adults.

Autor: Zucker EJ; From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.)., Sandino CM; From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.)., Kino A; From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.)., Lai P; From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.)., Vasanawala SS; From the Department of Radiology, Stanford University School of Medicine, 725 Welch Rd, Stanford, CA 94305 (E.J.Z., A.K., S.S.V.); Department of Electrical Engineering, Stanford University, Stanford, Calif (C.M.S.); and Global MR Applications and Workflow, GE Healthcare, Menlo Park, Calif (P.L.).

Publikováno v: Radiology [Radiology] 2021 Sep; Vol. 300 (3), pp. 539-548. Date of Electronic Publication: 2021 Jun 15.

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Akademický článek

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices.

Autor: Chaudhari AS; Department of Radiology, Stanford University, Stanford, California, USA., Sandino CM; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Cole EK; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Larson DB; Department of Radiology, Stanford University, Stanford, California, USA., Gold GE; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Orthopaedic Surgery, Stanford University, Stanford, California, USA.; Department of Bioengineering, Stanford University, Stanford, California, USA., Vasanawala SS; Department of Radiology, Stanford University, Stanford, California, USA., Lungren MP; Department of Radiology, Stanford University, Stanford, California, USA., Hargreaves BA; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Electrical Engineering, Stanford University, Stanford, California, USA.; Department of Biomedical Informatics, Stanford University, Stanford, California, USA., Langlotz CP; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Biomedical Informatics, Stanford University, Stanford, California, USA.

Publikováno v: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2021 Aug; Vol. 54 (2), pp. 357-371. Date of Electronic Publication: 2020 Aug 24.

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Akademický článek

Free-breathing R 2 ∗ mapping of hepatic iron overload in children using 3D multi-echo UTE cones MRI.

Autor: Kee Y; Departments of Radiology and Electrical Engineering, Stanford University, Magnetic Resonance Systems Research Lab (MRSRL), Stanford, California, USA., Sandino CM; Departments of Radiology and Electrical Engineering, Stanford University, Magnetic Resonance Systems Research Lab (MRSRL), Stanford, California, USA., Syed AB; Departments of Radiology and Electrical Engineering, Stanford University, Magnetic Resonance Systems Research Lab (MRSRL), Stanford, California, USA., Cheng JY; Departments of Radiology and Electrical Engineering, Stanford University, Magnetic Resonance Systems Research Lab (MRSRL), Stanford, California, USA., Shimakawa A; Global MR Applications and Workflow, GE Healthcare, Menlo Park, California, USA., Colgan TJ; Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, Madison, Wisconsin, USA., Hernando D; Departments of Radiology and Medical Physics, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, Madison, Wisconsin, USA., Vasanawala SS; Departments of Radiology and Electrical Engineering, Stanford University, Magnetic Resonance Systems Research Lab (MRSRL), Stanford, California, USA.

Publikováno v: Magnetic resonance in medicine [Magn Reson Med] 2021 May; Vol. 85 (5), pp. 2608-2621. Date of Electronic Publication: 2021 Jan 11.

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Akademický článek

Diffusion-weighted double-echo steady-state with a three-dimensional cones trajectory for non-contrast-enhanced breast MRI.

Autor: Moran CJ; Department of Radiology, Stanford University, Stanford, California, USA., Cheng JY; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Sandino CM; Department of Electrical Engineering, Stanford University, Stanford, California, USA., Carl M; Global MR Application and Workflow, GE Healthcare, San Diego, California, USA., Alley MT; Department of Radiology, Stanford University, Stanford, California, USA., Rosenberg J; Department of Radiology, Stanford University, Stanford, California, USA., Daniel BL; Department of Radiology, Stanford University, Stanford, California, USA., Pittman SM; Department of Radiology, Stanford University, Stanford, California, USA., Rosen EL; Department of Radiology, Stanford University, Stanford, California, USA., Hargreaves BA; Department of Radiology, Stanford University, Stanford, California, USA.; Department of Electrical Engineering, Stanford University, Stanford, California, USA.

Publikováno v: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2021 May; Vol. 53 (5), pp. 1594-1605. Date of Electronic Publication: 2020 Dec 31.

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Akademický článek

Accelerating cardiac cine MRI using a deep learning-based ESPIRiT reconstruction.

Autor: Sandino CM; Department of Electrical Engineering, Stanford University, Stanford, CA, USA., Lai P; Applied Sciences Laboratory, GE Healthcare, Menlo Park, CA, USA., Vasanawala SS; Department of Radiology, Stanford University, Stanford, CA, USA., Cheng JY; Department of Radiology, Stanford University, Stanford, CA, USA.

Publikováno v: Magnetic resonance in medicine [Magn Reson Med] 2021 Jan; Vol. 85 (1), pp. 152-167. Date of Electronic Publication: 2020 Jul 22.

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