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Wang J, Salerno M. Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T. Magn Reson Med 2024. [PMID: 38576068 DOI: 10.1002/mrm.30106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 04/06/2024]
Abstract
PURPOSE To develop and evaluate a deep learning (DL) -based rapid image reconstruction and motion correction technique for high-resolution Cartesian first-pass myocardial perfusion imaging at 3T with whole-heart coverage for both single-slice (SS) and simultaneous multi-slice (SMS) acquisitions. METHODS 3D physics-driven unrolled network architectures were utilized for the reconstruction of high-resolution Cartesian perfusion imaging. The SS and SMS multiband (MB) = 2 networks were trained from 135 slices from 20 subjects. Structural similarity index (SSIM), peak SNR (PSNR), and normalized RMS error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5, excellent; 1, poor). For respiratory motion correction, a 2D U-Net based motion corrected network was proposed, and the temporal fidelity and second-order derivative were calculated to assess the performance of the motion correction. RESULTS Excellent performance was demonstrated in the proposed technique with high SSIM and PSNR, and low NRMSE. Image quality scores were (4.3 [4.3, 4.4], 4.5 [4.4, 4.6], 4.3 [4.3, 4.4], and 4.5 [4.3, 4.5]) for SS DL and SS L1-SENSE, MB = 2 DL and MB = 2 SMS-L1-SENSE, respectively, showing no statistically significant difference (p > 0.05 for SS and SMS) between (SMS)-L1-SENSE and the proposed DL technique. The network inference time was around 4 s per dynamic perfusion series with 40 frames while the time of (SMS)-L1-SENSE with GPU acceleration was approximately 30 min. CONCLUSION The proposed DL-based image reconstruction and motion correction technique enabled rapid and high-quality reconstruction for SS and SMS MB = 2 high-resolution Cartesian first-pass perfusion imaging at 3T.
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Affiliation(s)
- Junyu Wang
- Department of Cardiovascular Medicine, Stanford University, Stanford, California, USA
| | - Michael Salerno
- Department of Cardiovascular Medicine, Stanford University, Stanford, California, USA
- Department of Radiology, Cardiovascular Imaging, Stanford University, Stanford, California, USA
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Wang J, Awad M, Zhou R, Wang Z, Wang X, Feng X, Yang Y, Meyer C, Kramer CM, Salerno M. High-resolution spiral real-time cardiac cine imaging with deep learning-based rapid image reconstruction and quantification. NMR IN BIOMEDICINE 2024; 37:e5051. [PMID: 37926525 DOI: 10.1002/nbm.5051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 08/20/2023] [Accepted: 09/08/2023] [Indexed: 11/07/2023]
Abstract
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) and deep learning (DL)-based segmentation approach to quantify the left ventricular ejection fraction (LVEF) for high-resolution spiral real-time cine imaging, including 2D balanced steady-state free precession imaging at 1.5 T and gradient echo (GRE) imaging at 1.5 and 3 T. A 3D U-Net-based image reconstruction network and 2D U-Net-based image segmentation network were proposed and evaluated. Low-rank plus sparse (L+S) served as the reference for the image reconstruction network and manual contouring of the left ventricle was the reference of the segmentation network. To assess the image reconstruction quality, structural similarity index, peak signal-to-noise ratio, normalized root-mean-square error, and blind grading by two experienced cardiologists (5: excellent; 1: poor) were performed. To assess the segmentation performance, quantification of the LVEF on GRE imaging at 3 T was compared with the quantification from manual contouring. Excellent performance was demonstrated by the proposed technique. In terms of image quality, there was no difference between L+S and the proposed DESIRE technique. For quantification analysis, the proposed DL method was not different to the manual segmentation method (p > 0.05) in terms of quantification of LVEF. The reconstruction time for DESIRE was ~32 s (including nonuniform fast Fourier transform [NUFFT]) per dynamic series (40 frames), while the reconstruction time of L+S with GPU acceleration was approximately 3 min. The DL segmentation takes less than 5 s. In conclusion, the proposed DL-based image reconstruction and quantification techniques enabled 1-min image reconstruction for the whole heart and quantification with automatic reconstruction and quantification of the left ventricle function for high-resolution spiral real-time cine imaging with excellent performance.
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Affiliation(s)
- Junyu Wang
- Department of Medicine, Cardiovascular Medicine, Stanford University, Stanford, California, USA
| | - Marina Awad
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Ruixi Zhou
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Department of Radiation Oncology, City of Hope, Duarte, California, USA
| | - Xitong Wang
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Xue Feng
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Yang Yang
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Craig Meyer
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M Kramer
- Department of Medicine, Division of Cardiovascular, University of Virginia Health System, Charlottesville, Virginia, USA
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Michael Salerno
- Department of Medicine, Cardiovascular Medicine, Stanford University, Stanford, California, USA
- Department of Radiology, Cardiovascular Imaging, Stanford University, Stanford, California, USA
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Contijoch F, Rasche V, Seiberlich N, Peters DC. The future of CMR: All-in-one vs. real-time CMR (Part 2). J Cardiovasc Magn Reson 2024; 26:100998. [PMID: 38237901 DOI: 10.1016/j.jocmr.2024.100998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 01/10/2024] [Indexed: 02/20/2024] Open
Abstract
Cardiac Magnetic Resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically "all-in-one" CMR, where several contrasts and/or motion states are acquired simultaneously, and "real-time" CMR, in which the examination is accelerated to avoid the need for breathholding and/or cardiac gating. The goal of this two-part manuscript is to describe these two different types of emerging rapid CMR protocols. To this end, the vision of all-in-one and real-time imaging are described, along with techniques which have been devised and tested along the pathway of clinical implementation. The pros and cons of the different methods are presented, and the remaining open needs of each are detailed. Part 1 tackles the "All-in-One" approaches, and Part 2 focuses on the "Real-Time" approaches along with an overall summary of these emerging methods.
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Affiliation(s)
| | - Volker Rasche
- Ulm University Medical Center, Department of Internal Medicine II, Ulm, Germany
| | - Nicole Seiberlich
- Michigan Institute for Imaging Technology and Translation, Department of Radiology, University of Michigan, Ann Arbor, MI, USA
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Morales MA, Manning WJ, Nezafat R. Present and Future Innovations in AI and Cardiac MRI. Radiology 2024; 310:e231269. [PMID: 38193835 PMCID: PMC10831479 DOI: 10.1148/radiol.231269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/21/2023] [Accepted: 10/26/2023] [Indexed: 01/10/2024]
Abstract
Cardiac MRI is used to diagnose and treat patients with a multitude of cardiovascular diseases. Despite the growth of clinical cardiac MRI, complicated image prescriptions and long acquisition protocols limit the specialty and restrain its impact on the practice of medicine. Artificial intelligence (AI)-the ability to mimic human intelligence in learning and performing tasks-will impact nearly all aspects of MRI. Deep learning (DL) primarily uses an artificial neural network to learn a specific task from example data sets. Self-driving scanners are increasingly available, where AI automatically controls cardiac image prescriptions. These scanners offer faster image collection with higher spatial and temporal resolution, eliminating the need for cardiac triggering or breath holding. In the future, fully automated inline image analysis will most likely provide all contour drawings and initial measurements to the reader. Advanced analysis using radiomic or DL features may provide new insights and information not typically extracted in the current analysis workflow. AI may further help integrate these features with clinical, genetic, wearable-device, and "omics" data to improve patient outcomes. This article presents an overview of AI and its application in cardiac MRI, including in image acquisition, reconstruction, and processing, and opportunities for more personalized cardiovascular care through extraction of novel imaging markers.
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Affiliation(s)
- Manuel A. Morales
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Warren J. Manning
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
| | - Reza Nezafat
- From the Department of Medicine, Cardiovascular Division (M.A.M.,
W.J.M., R.N.), and Department of Radiology (W.J.M.), Beth Israel Deaconess
Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA
02215
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Zhang Y, He W, Yang L, Xuan L, Wu J, He Y, Guo Y, Xu Z. Efficient imaging using spiral acquisitions on a portable 50-mT MR head scanner. NMR IN BIOMEDICINE 2023; 36:e4988. [PMID: 37381057 DOI: 10.1002/nbm.4988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/30/2023]
Abstract
Ultralow-field (ULF) magnetic resonance imaging (MRI) can suffer from inferior image quality because of low signal-to-noise ratio (SNR). As an efficient way to cover the k-space, the spiral acquisition technique has shown great potential in improving imaging SNR efficiency at ULF. The current study aimed to address the problems of noise and blurring cancelation in the ULF case with spiral trajectory, and we proposed a spiral-out sequence for brain imaging using a portable 50-mT MRI system. The proposed sequence consisted of three modules: noise calibration, field map acquisition, and imaging. In the calibration step, transfer coefficients were obtained between signals from primary and noise-pick-up coils to perform electromagnetic interference (EMI) cancelation. Embedded field map acquisition was performed to correct accumulated phase error due to main field inhomogeneity. Considering imaging SNR, a lower bandwidth for data sampling was adopted in the sequence design because the 50-mT scanner is in a low SNR regime. Image reconstruction proceeded with sampled data by leveraging system imperfections, such as gradient delays and concomitant fields. The proposed method can provide images with higher SNR efficiency compared with its Cartesian counterparts. An improvement in temporal SNR of approximately 23%-44% was measured via phantom and in vivo experiments. Distortion-free images with a noise suppression rate of nearly 80% were obtained by the proposed technique. A comparison was also made with a state-of-the-art EMI cancelation algorithm used in the ULF-MRI system. SNR efficiency-enhanced spiral acquisitions were investigated for ULF-MR scanners and future studies could focus on various image contrasts based on our proposed approach to widen ULF applications.
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Affiliation(s)
- Yuxiang Zhang
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Wei He
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Lei Yang
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Liang Xuan
- School of Electrical Engineering, Chongqing University, Chongqing, China
| | - Jiamin Wu
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
- Harbin Institute of Technology, Harbin, China
| | - Yucheng He
- Shenzhen Academy of Aerospace Technology, Shenzhen, China
| | - Yi Guo
- Chongqing University Central Hospital, Chongqing, China
| | - Zheng Xu
- School of Electrical Engineering, Chongqing University, Chongqing, China
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Demirel OB, Yaman B, Shenoy C, Moeller S, Weingärtner S, Akçakaya M. Signal intensity informed multi-coil encoding operator for physics-guided deep learning reconstruction of highly accelerated myocardial perfusion CMR. Magn Reson Med 2023; 89:308-321. [PMID: 36128896 PMCID: PMC9617789 DOI: 10.1002/mrm.29453] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 07/21/2022] [Accepted: 08/21/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE To develop a physics-guided deep learning (PG-DL) reconstruction strategy based on a signal intensity informed multi-coil (SIIM) encoding operator for highly-accelerated simultaneous multislice (SMS) myocardial perfusion cardiac MRI (CMR). METHODS First-pass perfusion CMR acquires highly-accelerated images with dynamically varying signal intensity/SNR following the administration of a gadolinium-based contrast agent. Thus, using PG-DL reconstruction with a conventional multi-coil encoding operator leads to analogous signal intensity variations across different time-frames at the network output, creating difficulties in generalization for varying SNR levels. We propose to use a SIIM encoding operator to capture the signal intensity/SNR variations across time-frames in a reformulated encoding operator. This leads to a more uniform/flat contrast at the output of the PG-DL network, facilitating generalizability across time-frames. PG-DL reconstruction with the proposed SIIM encoding operator is compared to PG-DL with conventional encoding operator, split slice-GRAPPA, locally low-rank (LLR) regularized reconstruction, low-rank plus sparse (L + S) reconstruction, and regularized ROCK-SPIRiT. RESULTS Results on highly accelerated free-breathing first pass myocardial perfusion CMR at three-fold SMS and four-fold in-plane acceleration show that the proposed method improves upon the reconstruction methods use for comparison. Substantial noise reduction is achieved compared to split slice-GRAPPA, and aliasing artifacts reduction compared to LLR regularized reconstruction, L + S reconstruction and PG-DL with conventional encoding. Furthermore, a qualitative reader study indicated that proposed method outperformed all methods. CONCLUSION PG-DL reconstruction with the proposed SIIM encoding operator improves generalization across different time-frames /SNRs in highly accelerated perfusion CMR.
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Affiliation(s)
- Omer Burak Demirel
- Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisMinnesotaUSA,Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Burhaneddin Yaman
- Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisMinnesotaUSA,Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Chetan Shenoy
- Department of Medicine (Cardiology)University of MinnesotaMinneapolisMinnesotaUSA
| | - Steen Moeller
- Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | | | - Mehmet Akçakaya
- Department of Electrical and Computer EngineeringUniversity of MinnesotaMinneapolisMinnesotaUSA,Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
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Wang J, Weller DS, Kramer CM, Salerno M. DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging. NMR IN BIOMEDICINE 2022; 35:e4661. [PMID: 34939246 DOI: 10.1002/nbm.4661] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/01/2021] [Accepted: 11/16/2021] [Indexed: 06/14/2023]
Abstract
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast and accurate image reconstruction for both single-slice (SS) and simultaneous multislice (SMS) acquisitions. Three-dimensional U-Net-based image enhancement architectures were evaluated for high-resolution spiral perfusion imaging at 3 T. The SS and SMS MB = 2 networks were trained on SS perfusion images from 156 slices from 20 subjects. Structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and normalized root mean square error (NRMSE) were assessed, and prospective images were blindly graded by two experienced cardiologists (5: excellent; 1: poor). Excellent performance was demonstrated for the proposed technique. For SS, SSIM, PSNR, and NRMSE were 0.977 [0.972, 0.982], 42.113 [40.174, 43.493] dB, and 0.102 [0.080, 0.125], respectively, for the best network. For SMS MB = 2 retrospective data, SSIM, PSNR, and NRMSE were 0.961 [0.950, 0.969], 40.834 [39.619, 42.004] dB, and 0.107 [0.086, 0.133], respectively, for the best network. The image quality scores were 4.5 [4.1, 4.8], 4.5 [4.3, 4.6], 3.5 [3.3, 4], and 3.5 [3.3, 3.8] for SS DESIRE, SS L1-SPIRiT, MB = 2 DESIRE, and MB = 2 SMS-slice-L1-SPIRiT, respectively, showing no statistically significant difference (p = 1 and p = 1 for SS and SMS, respectively) between L1-SPIRiT and the proposed DESIRE technique. The network inference time was ~100 ms per dynamic perfusion series with DESIRE, while the reconstruction time of L1-SPIRiT with GPU acceleration was ~ 30 min. It was concluded that DESIRE enabled fast and high-quality image reconstruction for both SS and SMS MB = 2 whole-heart high-resolution spiral perfusion imaging.
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Affiliation(s)
- Junyu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Daniel S Weller
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Christopher M Kramer
- Department of Medicine, Cardiovascular Division, University of Virginia Health System, Charlottesville, Virginia, USA
- Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Michael Salerno
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
- Department of Medicine, Cardiovascular Division, University of Virginia Health System, Charlottesville, Virginia, USA
- Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA
- Departments of Medicine and Radiology, Stanford University Medical Center, Stanford, California, USA
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