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Zhang JL, Lee VS. Renal perfusion imaging by MRI. J Magn Reson Imaging 2019; 52:369-379. [PMID: 31452303 DOI: 10.1002/jmri.26911] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Accepted: 08/14/2019] [Indexed: 12/13/2022] Open
Abstract
Renal perfusion can be quantitatively assessed by multiple magnetic resonance imaging (MRI) methods, including dynamic contrast enhanced (DCE), arterial spin labeling (ASL), and diffusion-weighted imaging with intravoxel incoherent motion (IVIM) analysis. In this review we summarize the advances in the field of renal-perfusion MRI over the past 5 years. The review starts with a brief introduction of relevant MRI methods, followed by a discussion of recent technical developments. In the main section of the review, we examine the clinical and preclinical applications for three disease populations: chronic kidney disease, renal transplant, and renal tumors. The DCE method has been routinely used for assessing renal tumors but not other renal diseases. As a noncontrast alternative, ASL was extensively explored in both preclinical and clinical applications and showed much promise. Protocol standardization for the methods is desperately needed, and then large-scale clinical trials for the methods can be initiated prior to their broad clinical use. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:369-379.
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Affiliation(s)
- Jeff L Zhang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Vivian S Lee
- Verily Life Sciences, Cambridge, Massachusetts, USA
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Pandey A, Yoruk U, Keerthivasan M, Galons JP, Sharma P, Johnson K, Martin DR, Altbach MI, Bilgin A, Saranathan M. Multiresolution imaging using golden angle stack-of-stars and compressed sensing for dynamic MR urography. J Magn Reson Imaging 2017; 46:303-311. [PMID: 28176396 DOI: 10.1002/jmri.25576] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 11/21/2016] [Indexed: 12/30/2022] Open
Abstract
PURPOSE To develop a novel multiresolution MRI methodology for accurate estimation of glomerular filtration rate (GFR) in vivo. MATERIALS AND METHODS A three-dimensional golden-angle radial stack-of-stars (SoS) trajectory was used for data acquisition on a 3 Tesla MRI scanner. Multiresolution reconstruction and analysis was performed using arterial input function reconstructed at 1-s. temporal resolution and renal dynamic data reconstructed using compressed sensing (CS) with 4-s temporal resolution. The method was first validated using simulations and the clinical utility of the technique was evaluated by comparing the GFR estimates from the proposed method to the estimated GFR (eGFR) obtained from serum creatinine for 10 subjects. RESULTS The 4-s temporal resolution CS images minimized streaking artifacts and noise while the 1-s temporal resolution AIF minimized errors in GFR estimates. A paired t-test showed that there was no statistically significant difference between MRI based total GFR values and serum creatinine based eGFR estimates (P = 0.92). CONCLUSION We have demonstrated the feasibility of multiresolution MRI using a golden angle radial stack-of-stars scheme to accurately estimate GFR as well as produce diagnostic quality dynamic images in vivo. LEVEL OF EVIDENCE 1 Technical Efficacy: Stage 3 J. MAGN. RESON. IMAGING 2017;46:303-311.
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Affiliation(s)
- Abhishek Pandey
- Electrical & Computer Engineering, University of Arizona, Tucson, Arizona, USA.,Medical Imaging, University of Arizona, Tucson, Arizona, USA
| | - Umit Yoruk
- Radiology, Stanford University, Stanford, California, USA
| | - Mahesh Keerthivasan
- Electrical & Computer Engineering, University of Arizona, Tucson, Arizona, USA.,Medical Imaging, University of Arizona, Tucson, Arizona, USA
| | | | - Puneet Sharma
- Medical Imaging, University of Arizona, Tucson, Arizona, USA
| | - Kevin Johnson
- Siemens Medical Solution USA, Inc, Malvern, Pennsylvania, USA
| | - Diego R Martin
- Medical Imaging, University of Arizona, Tucson, Arizona, USA
| | - Maria I Altbach
- Medical Imaging, University of Arizona, Tucson, Arizona, USA
| | - Ali Bilgin
- Electrical & Computer Engineering, University of Arizona, Tucson, Arizona, USA.,Medical Imaging, University of Arizona, Tucson, Arizona, USA.,Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
| | - Manojkumar Saranathan
- Medical Imaging, University of Arizona, Tucson, Arizona, USA.,Biomedical Engineering, University of Arizona, Tucson, Arizona, USA
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Yang ACY, Kretzler M, Sudarski S, Gulani V, Seiberlich N. Sparse Reconstruction Techniques in Magnetic Resonance Imaging: Methods, Applications, and Challenges to Clinical Adoption. Invest Radiol 2016; 51:349-64. [PMID: 27003227 PMCID: PMC4948115 DOI: 10.1097/rli.0000000000000274] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
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Affiliation(s)
- Alice Chieh-Yu Yang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
| | - Madison Kretzler
- Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, USA
| | - Sonja Sudarski
- Institute for Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Medical Faculty Mannheim - Heidelberg University, Heidelberg, Germany
| | - Vikas Gulani
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
| | - Nicole Seiberlich
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, USA
- Department of Radiology, University Hospitals of Cleveland, Cleveland, USA
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