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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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Hoh T, Vishnevskiy V, Polacin M, Manka R, Fuetterer M, Kozerke S. Free-breathing motion-informed locally low-rank quantitative 3D myocardial perfusion imaging. Magn Reson Med 2022; 88:1575-1591. [PMID: 35713206 PMCID: PMC9544898 DOI: 10.1002/mrm.29295] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 03/30/2022] [Accepted: 04/19/2022] [Indexed: 12/30/2022]
Abstract
Purpose To propose respiratory motion‐informed locally low‐rank reconstruction (MI‐LLR) for robust free‐breathing single‐bolus quantitative 3D myocardial perfusion CMR imaging. Simulation and in‐vivo results are compared to locally low‐rank (LLR) and compressed sensing reconstructions (CS) for reference. Methods Data were acquired using a 3D Cartesian pseudo‐spiral in‐out k‐t undersampling scheme (R = 10) and reconstructed using MI‐LLR, which encompasses two stages. In the first stage, approximate displacement fields are derived from an initial LLR reconstruction to feed a motion‐compensated reference system to a second reconstruction stage, which reduces the rank of the inverse problem. For comparison, data were also reconstructed with LLR and frame‐by‐frame CS using wavelets as sparsifying transform (ℓ1‐wavelet). Reconstruction accuracy relative to ground truth was assessed using synthetic data for realistic ranges of breathing motion, heart rates, and SNRs. In‐vivo experiments were conducted in healthy subjects at rest and during adenosine stress. Myocardial blood flow (MBF) maps were derived using a Fermi model. Results Improved uniformity of MBF maps with reduced local variations was achieved with MI‐LLR. For rest and stress, intra‐volunteer variation of absolute and relative MBF was lower in MI‐LLR (±0.17 mL/g/min [26%] and ±1.07 mL/g/min [33%]) versus LLR (±0.19 mL/g/min [28%] and ±1.22 mL/g/min [36%]) and versus ℓ1‐wavelet (±1.17 mL/g/min [113%] and ±6.87 mL/g/min [115%]). At rest, intra‐subject MBF variation was reduced significantly with MI‐LLR. Conclusion The combination of pseudo‐spiral Cartesian undersampling and dual‐stage MI‐LLR reconstruction improves free‐breathing quantitative 3D myocardial perfusion CMR imaging under rest and stress condition. Click here for author‐reader discussions
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Affiliation(s)
- Tobias Hoh
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Valery Vishnevskiy
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Malgorzata Polacin
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Robert Manka
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Zurich, Switzerland.,Department of Cardiology, University Heart Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Maximilian Fuetterer
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
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Tourais J, Scannell CM, Schneider T, Alskaf E, Crawley R, Bosio F, Sanchez-Gonzalez J, Doneva M, Schülke C, Meineke J, Keupp J, Smink J, Breeuwer M, Chiribiri A, Henningsson M, Correia T. High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration. Front Cardiovasc Med 2022; 9:884221. [PMID: 35571164 PMCID: PMC9099052 DOI: 10.3389/fcvm.2022.884221] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 04/04/2022] [Indexed: 11/21/2022] Open
Abstract
Introduction To develop and test the feasibility of free-breathing (FB), high-resolution quantitative first-pass perfusion cardiac MR (FPP-CMR) using dual-echo Dixon (FOSTERS; Fat-water separation for mOtion-corrected Spatio-TEmporally accelerated myocardial peRfuSion). Materials and Methods FOSTERS was performed in FB using a dual-saturation single-bolus acquisition with dual-echo Dixon and a dynamically variable Cartesian k-t undersampling (8-fold) approach, with low-rank and sparsity constrained reconstruction, to achieve high-resolution FPP-CMR images. FOSTERS also included automatic in-plane motion estimation and T2* correction to obtain quantitative myocardial blood flow (MBF) maps. High-resolution (1.6 x 1.6 mm2) FB FOSTERS was evaluated in eleven patients, during rest, against standard-resolution (2.6 x 2.6 mm2) 2-fold SENSE-accelerated breath-hold (BH) FPP-CMR. In addition, MBF was computed for FOSTERS and spatial wavelet-based compressed sensing (CS) reconstruction. Two cardiologists scored the image quality (IQ) of FOSTERS, CS, and standard BH FPP-CMR images using a 4-point scale (1–4, non-diagnostic – fully diagnostic). Results FOSTERS produced high-quality images without dark-rim and with reduced motion-related artifacts, using an 8x accelerated FB acquisition. FOSTERS and standard BH FPP-CMR exhibited excellent IQ with an average score of 3.5 ± 0.6 and 3.4 ± 0.6 (no statistical difference, p > 0.05), respectively. CS images exhibited severe artifacts and high levels of noise, resulting in an average IQ score of 2.9 ± 0.5. MBF values obtained with FOSTERS presented a lower variance than those obtained with CS. Discussion FOSTERS enabled high-resolution FB FPP-CMR with MBF quantification. Combining motion correction with a low-rank and sparsity-constrained reconstruction results in excellent image quality.
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Affiliation(s)
- Joao Tourais
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
- Department of Imaging Physics, Magnetic Resonance Systems Lab, Delft University of Technology, Delft, Netherlands
| | - Cian M. Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | - Ebraham Alskaf
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Richard Crawley
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Filippo Bosio
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | | | | | | | | | | | - Jouke Smink
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
- Department of MR R&D – Clinical Science, Philips Healthcare, Best, Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Markus Henningsson
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linkoping University, Linkoping, Sweden
- Center for Medical Image Science and Visualization (CMIV), Linkoping University, Linkoping, Sweden
| | - Teresa Correia
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Centre for Marine Sciences (CCMAR), Faro, Portugal
- *Correspondence: Teresa Correia
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Fan L, Hong K, Hsu LY, Carr JC, Allen BD, Lee DC, Kim D. Optimal saturation recovery time for minimizing the underestimation of arterial input function in quantitative cardiac perfusion MRI. Magn Reson Med 2022; 88:832-839. [PMID: 35377476 PMCID: PMC9321550 DOI: 10.1002/mrm.29240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 02/04/2022] [Accepted: 02/28/2022] [Indexed: 11/07/2022]
Abstract
Purpose The purpose of this study was to determine an optimal saturation‐recovery time (TS) for minimizing the underestimation of arterial input function (AIF) in quantitative cardiac perfusion MRI without multiple gadolinium injections per subject. Methods We scanned 18 subjects (mean age = 59 ± 14 years, 9/9 males/females) to acquire resting perfusion data and 1 additional subject (age = 38 years, male) to obtain stress‐rest perfusion data using a 5‐fold accelerated pulse sequence with radial k‐space sampling and applied k‐space weighted image contrast (KWIC) filters on the same k‐space data to retrospectively reconstruct five AIF images with effective TS ranging from 10 to 21.2 ms (2.8 ms steps). Undersampled images were reconstructed using a compressed sensing framework with temporal‐total‐variation and temporal‐principal‐component as 2 orthogonal sparsifying transforms. The image processing steps included, same motion correction across five different AIF images, signal normalization by the proton‐density‐weighted‐image, signal‐to‐T1 conversion using a Bloch equation, T1‐to‐gadolinium‐concentration conversion assuming fast water exchange, T2* correction to the AIF, and gadolinium‐concentration to myocardial blood flow (MBF) conversion based on a Fermi model. Results Among five TS values, the shortest TS (10 ms) produced significantly (P < 0.05) higher peak AIF and lower resting MBF (13.73 mM, 0.73 mL g−1 min−1) than 12.8 ms (11.24 mM, 0.89 mL g−1 min−1), 15.6 ms (9.56 mM, 1.05 mL g−1 min−1), 18.4 ms (8.55 mM, 1.17 mL g−1 min−1), and 21.2 ms (7.95 mM, 1.27 mL g−1 min−1). Similarly, shorter TS reduced underestimation of AIF (or overestimation of MBF) for both during stress and at rest, but this effect was canceled in myocardial‐perfusion‐reserve (MPR). Conclusion This study demonstrates that TS of 10 ms reduces the underestimation of AIF and, hence, the overestimation of MBF compared with longer TS values (12.8‐21.2 ms).
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Affiliation(s)
- Lexiaozi Fan
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA
| | - Kyungpyo Hong
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Li-Yueh Hsu
- Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - James C Carr
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Daniel C Lee
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Daniel Kim
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA
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Hong K, Schiffers F, DiCarlo AL, Rigsby CK, Haji-Valizadeh H, Lee DC, Markl M, Katsaggelos AK, Kim D. Accelerating compressed sensing reconstruction of subsampled radial k-space data using geometrically-derived density compensation. Phys Med Biol 2021; 66. [PMID: 34607316 DOI: 10.1088/1361-6560/ac2c9d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 10/04/2021] [Indexed: 11/12/2022]
Abstract
Objective.To accelerate compressed sensing (CS) reconstruction of subsampled radial k-space data using a geometrically-derived density compensation function (gDCF) without significant loss in image quality.Approach.We developed a theoretical framework to calculate a gDCF based on Nyquist distance along the radial and circumferential directions of a discrete polar coordinate system. Our gDCF was compared against standard DCF (e.g. ramp filter) and another commonly used DCF (modified Shepp-Logan (SL) filter). The resulting image quality produced by each DCF was quantified using normalized root-mean-square-error (NRMSE), blur metric (1 = blurriest; 0 = sharpest), and structural similarity index (SSIM; 1 = perfect match; 0 = no match) compared with the reference. For filtered backprojection (FBP) of phantom data obtained at the Nyquist sampling rate, Cartesian k-space sampling was used as the reference. For CS reconstruction of subsampled cardiac magnetic resonance imaging datasets (real-time cardiac cine data with 11 projections per frame,n = 20 patients; cardiac perfusion data with 30 projections per frame,n = 19 patients), CS reconstruction without DCF was used as the reference.Main results.The NRMSE, SSIM, and blur metrics of the phantom data were good for all DCFs, confirming that our gDCF produces uniform densities at the upper limit (Nyquist). For CS reconstruction of subsampled real-time cine and cardiac perfusion datasets, the image quality metrics (SSIM, NRMSE) were significantly (p < 0.05) higher for our gDCF than other DCFs, and the reconstruction time was significantly (p < 0.05) faster for our gDCF (reference) than no DCF (11.9%-52.9% slower), standard DCF (23.9%-57.6% slower), and modified SL filter (13.5%-34.8% slower).Significance.The proposed gDCF accelerates CS reconstruction of subsampled radial k-space data without significant loss in image quality compared with no DCF as the reference.
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Affiliation(s)
- KyungPyo Hong
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Florian Schiffers
- Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, United States of America
| | - Amanda L DiCarlo
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Cynthia K Rigsby
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Medical Imaging, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, United States of America
| | - Hassan Haji-Valizadeh
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Daniel C Lee
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Michael Markl
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
| | - Aggelos K Katsaggelos
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Computer Science, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, United States of America.,Department of Electrical and Computer Engineering, McCormick School of Engineering and Applied Science, Northwestern University, Evanston, Illinois, United States of America
| | - Daniel Kim
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois, United States of America
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6
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Haji-Valizadeh H, Guo R, Kucukseymen S, Cai X, Rodriguez J, Pierce P, Goddu B, Manning W, Nezafat R. Artifact reduction in free-breathing, free-running myocardial perfusion imaging with interleaved non-selective RF excitations. Magn Reson Med 2021; 86:954-963. [PMID: 33764599 DOI: 10.1002/mrm.28765] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 02/13/2021] [Accepted: 02/15/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To reduce inflow and motion artifacts in free-breathing, free-running, steady-state spoiled gradient echo T1 -weighted (SPGR) myocardial perfusion imaging. METHOD Unsaturated spins from inflowing blood or out-of-plane motion cause flashing artifacts in free-running SPGR myocardial perfusion. During free-running SPGR, 1 non-selective RF excitation was added after every 3 slice-selective RF excitations to suppress inflow artifacts by forcing magnetization in neighboring regions to steady-state. Bloch simulations and phantom experiments were performed to evaluate the impact of the flip angle and non-selective RF frequency on inflowing spins and tissue contrast. Free-running perfusion with (n = 11) interleaved non-selective RF or without (n = 11) were studied in 22 subjects (age = 60.2 ± 14.3 years, 11 male). Perfusion images were graded on a 5-point Likert scale for conspicuity of wall enhancement, inflow/motion artifact, and streaking artifact and compared using Wilcoxon sum-rank testing. RESULT Numeric simulation showed that 1 non-selective RF excitation applied after every 3 slice-selective RF excitations produced superior out-of-plane signal suppression compared to 1 non-selective RF excitation applied after every 6 or 9 slice-selective RF excitations. In vitro experiments showed that a 30° flip angle produced near-optimal myocardial contrast. In vivo experiments demonstrated that the addition of interleaved non-selective RF significantly (P < .01) improved conspicuity of wall enhancement (mean score = 4.4 vs. 3.2) and reduced inflow/motion (mean score = 4.5 vs. 2.5) and streaking (mean score = 3.9 vs. 2.4) artifacts. CONCLUSION Non-selective RF excitations interleaved between slice-selective excitations can reduce image artifacts in free-breathing, ungated perfusion images. Further studies are warranted to assess the diagnostic accuracy of the proposed solution for evaluating myocardial ischemia.
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Affiliation(s)
- Hassan Haji-Valizadeh
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Rui Guo
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Selcuk Kucukseymen
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Xiaoying Cai
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.,Siemens Medical Solutions Inc., Boston, Massachusetts, USA
| | - Jennifer Rodriguez
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Patrick Pierce
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Beth Goddu
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Warren Manning
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.,Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Reza Nezafat
- Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
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Fan L, Allen BD, Culver AE, Hsu LY, Hong K, Benefield BC, Carr JC, Lee DC, Kim D. A theoretical framework for retrospective T 2 ∗ correction to the arterial input function in quantitative myocardial perfusion MRI. Magn Reson Med 2021; 86:1137-1144. [PMID: 33759238 DOI: 10.1002/mrm.28760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 02/10/2021] [Accepted: 02/12/2021] [Indexed: 12/22/2022]
Abstract
PURPOSE To develop and evaluate a flexible, Bloch-equation based framework for retrospective T 2 ∗ correction to the arterial input function (AIF) obtained with quantitative cardiac perfusion pulse sequences. METHODS Our framework initially calculates the gadolinium concentration [Gd] based on T1 measurements alone. Next, T 2 ∗ is estimated from this initial calculation of [Gd] while assuming fast water exchange and using the literature native T2 and static magnetic field variation (ΔB0 ) values. Finally, the [Gd] is recalculated after performing T 2 ∗ correction to the Bloch equation signal model. Using this approach, we performed T 2 ∗ correction to historical phantom and in vivo, dual-imaging perfusion data sets from 3 different patient groups obtained using different pulse sequences and imaging parameters. Images were processed to quantify both the AIF and resting myocardial blood flow (MBF). We also performed a sensitivity analysis of our T 2 ∗ correction to ±20% variations in native T2 and ΔB0 . RESULTS Compared with the ground truth [Gd] of phantom, the normalized root-means-square-error (NRMSE) in measured [Gd] was 5.1%, 1.3%, and 0.6% for uncorrected, our corrected, and Kellman's corrected, respectively. For in vivo data, both the peak AIF (7.0 ± 3.0 mM vs. 8.6 ± 7.1 mM, 7.2 ± 0.9 mM vs. 8.6 ± 1.7 mM, 7.7 ± 1.8 mM vs. 10.3 ± 5.1 mM, P < .001) and resting MBF (1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.2 ± 0.1 mL/g/min vs. 0.9 ± 0.1 mL/g/min, P < .001) values were significantly different between uncorrected and corrected for all 3 patient groups. Both the peak AIF and resting MBF values varied by <5% over the said variations in native T2 and ΔB0 . CONCLUSION Our theoretical framework enables retrospective T 2 ∗ correction to the AIF obtained with dual-imaging, cardiac perfusion pulse sequences.
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Affiliation(s)
- Lexiaozi Fan
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA
| | - Bradley D Allen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Austin E Culver
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Li-Yueh Hsu
- Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, USA
| | - Kyungpyo Hong
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Brandon C Benefield
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - James C Carr
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Daniel C Lee
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Daniel Kim
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.,Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA
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8
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Wang J, Yang Y, Weller DS, Zhou R, Van Houten M, Sun C, Epstein FH, Meyer CH, Kramer CM, Salerno M. High spatial resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage at 3 T. Magn Reson Med 2021; 86:648-662. [PMID: 33709415 DOI: 10.1002/mrm.28701] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/16/2020] [Accepted: 01/04/2021] [Indexed: 11/10/2022]
Abstract
PURPOSE To develop and evaluate a high spatial resolution (1.25 × 1.25 mm2 ) spiral first-pass myocardial perfusion imaging technique with whole-heart coverage at 3T, to better assess transmural differences in perfusion between the endocardium and epicardium, to quantify the myocardial ischemic burden, and to improve the detection of obstructive coronary artery disease. METHODS Whole-heart high-resolution spiral perfusion pulse sequences and corresponding motion-compensated reconstruction techniques for both interleaved single-slice (SS) and simultaneous multi-slice (SMS) acquisition with or without outer-volume suppression (OVS) were developed. The proposed techniques were evaluated in 34 healthy volunteers and 8 patients (55 data sets). SS and SMS images were reconstructed using motion-compensated L1-SPIRiT and SMS-Slice-L1-SPIRiT, respectively. Images were blindly graded by 2 experienced cardiologists on a 5-point scale (5, excellent; 1, poor). RESULTS High-quality perfusion imaging was achieved for both SS and SMS acquisitions with or without OVS. The SS technique without OVS had the highest scores (4.5 [4, 5]), which were greater than scores for SS with OVS (3.5 [3.25, 3.75], P < .05), MB = 2 without OVS (3.75 [3.25, 4], P < .05), and MB = 2 with OVS (3.75 [2.75, 4], P < .05), but significantly higher than those for MB = 3 without OVS (4 [4, 4], P = .95). SMS image quality was improved using SMS-Slice-L1-SPIRiT as compared to SMS-L1-SPIRiT (P < .05 for both reviewers). CONCLUSION We demonstrated the successful implementation of whole-heart spiral perfusion imaging with high resolution at 3T. Good image quality was achieved, and the SS without OVS showed the best image quality. Evaluation in patients with expected ischemic heart disease is warranted.
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Affiliation(s)
- Junyu Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Yang Yang
- Biomedical Engineering and Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA.,Department of Medicine, Cardiovascular Division, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Daniel S Weller
- Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Ruixi Zhou
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Matthew Van Houten
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Changyu Sun
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA
| | - Frederick H Epstein
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.,Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, Virginia, USA
| | - Craig H Meyer
- Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.,Radiology and Medical Imaging, University of Virginia Health System, 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
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Le TT, Ang BWY, Bryant JA, Chin CY, Yeo KK, Wong PEH, Ho KW, Tan JWC, Lee PT, Chin CWL, Cook SA. Multiparametric exercise stress cardiovascular magnetic resonance in the diagnosis of coronary artery disease: the EMPIRE trial. J Cardiovasc Magn Reson 2021; 23:17. [PMID: 33658056 PMCID: PMC7931509 DOI: 10.1186/s12968-021-00705-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/06/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Stress cardiovascular magnetic resonance (CMR) offers assessment of ventricular function, myocardial perfusion and viability in a single examination to detect coronary artery disease (CAD). We developed an in-scanner exercise stress CMR (ExCMR) protocol using supine cycle ergometer and aimed to examine the diagnostic value of a multiparametric approach in patients with suspected CAD, compared with invasive fractional flow reserve (FFR) as the reference gold standard. METHODS In this single-centre prospective study, patients who had symptoms of angina and at least one cardiovascular disease risk factor underwent both ExCMR and invasive angiography with FFR. Rest-based left ventricular function (ejection fraction, regional wall motion abnormalities), tissue characteristics and exercise stress-derived (perfusion defects, inducible regional wall motion abnormalities and peak exercise cardiac index percentile-rank) CMR parameters were evaluated in the study. RESULTS In the 60 recruited patients with intermediate CAD risk, 50% had haemodynamically significant CAD based on FFR. Of all the CMR parameters assessed, the late gadolinium enhancement, stress-inducible regional wall motion abnormalities, perfusion defects and peak exercise cardiac index percentile-rank were independently associated with FFR-positive CAD. Indeed, this multiparametric approach offered the highest incremental diagnostic value compared to a clinical risk model (χ2 for the diagnosis of FFR-positive increased from 7.6 to 55.9; P < 0.001) and excellent performance [c-statistic area under the curve 0.97 (95% CI: 0.94-1.00)] in discriminating between FFR-normal and FFR-positive patients. CONCLUSION The study demonstrates the clinical potential of using in-scanner multiparametric ExCMR to accurately diagnose CAD. TRIAL REGISTRATION ClinicalTrials.gov, NCT03217227, Registered 11 July 2017-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03217227?id=NCT03217227&draw=2&rank=1&load=cart.
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Affiliation(s)
- Thu-Thao Le
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore.
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore.
| | - Briana W Y Ang
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
| | - Jennifer A Bryant
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
| | - Chee Yang Chin
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Khung Keong Yeo
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Philip E H Wong
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Kay Woon Ho
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Jack W C Tan
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Phong Teck Lee
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Calvin W L Chin
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Stuart A Cook
- National Heart Research Institute Singapore, National Heart Centre Singapore, 5 Hospital Drive, Singapore, 169609, Singapore
- Cardiovascular Sciences ACP, Duke-NUS Graduate Medical School, Singapore, Singapore
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
- National Heart and Lung Institute, Imperial College, London, UK
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McElroy S, Ferrazzi G, Nazir MS, Kunze KP, Neji R, Speier P, Stäb D, Forman C, Razavi R, Chiribiri A, Roujol S. Combined simultaneous multislice bSSFP and compressed sensing for first-pass myocardial perfusion at 1.5 T with high spatial resolution and coverage. Magn Reson Med 2020; 84:3103-3116. [PMID: 32530064 PMCID: PMC7611375 DOI: 10.1002/mrm.28345] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 05/14/2020] [Accepted: 05/14/2020] [Indexed: 02/02/2023]
Abstract
PURPOSE To implement and evaluate a pseudorandom undersampling scheme for combined simultaneous multislice (SMS) balanced SSFP (bSSFP) and compressed-sensing (CS) reconstruction to enable myocardial perfusion imaging with high spatial resolution and coverage at 1.5 T. METHODS A prospective pseudorandom undersampling scheme that is compatible with SMS-bSSFP phase-cycling requirements and CS was developed. The SMS-bSSFP CS with pseudorandom and linear undersampling schemes were compared in a phantom. A high-resolution (1.4 × 1.4 mm2 ) six-slice SMS-bSSFP CS perfusion sequence was compared with a conventional (1.9 × 1.9 mm2 ) three-slice sequence in 10 patients. Qualitative assessment of image quality, perceived SNR, and number of diagnostic segments and quantitative measurements of sharpness, upslope index, and contrast ratio were performed. RESULTS In phantom experiments, pseudorandom undersampling resulted in residual artifact (RMS error) reduction by a factor of 7 compared with linear undersampling. In vivo, the proposed sequence demonstrated higher perceived SNR (2.9 ± 0.3 vs. 2.2 ± 0.6, P = .04), improved sharpness (0.35 ± 0.03 vs. 0.32 ± 0.05, P = .01), and a higher number of diagnostic segments (100% vs. 94%, P = .03) compared with the conventional sequence. There were no significant differences between the sequences in terms of image quality (2.5 ± 0.4 vs. 2.8 ± 0.2, P = .08), upslope index (0.11 ± 0.02 vs. 0.10 ± 0.01, P = .3), or contrast ratio (3.28 ± 0.35 vs. 3.36 ± 0.43, P = .7). CONCLUSION A pseudorandom k-space undersampling compatible with SMS-bSSFP and CS reconstruction has been developed and enables cardiac MR perfusion imaging with increased spatial resolution and myocardial coverage, increased number of diagnostic segments and perceived SNR, and no difference in image quality, upslope index, and contrast ratio.
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Affiliation(s)
- Sarah McElroy
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Giulio Ferrazzi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Muhummad Sohaib Nazir
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Karl P. Kunze
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Radhouene Neji
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
- MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom
| | - Peter Speier
- Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany
| | - Daniel Stäb
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | | | - Reza Razavi
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
| | - Sébastien Roujol
- School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King’s College London, London, United Kingdom
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Mendes JK, Adluru G, Likhite D, Fair MJ, Gatehouse PD, Tian Y, Pedgaonkar A, Wilson B, DiBella EVR. Quantitative 3D myocardial perfusion with an efficient arterial input function. Magn Reson Med 2020; 83:1949-1963. [PMID: 31670858 PMCID: PMC7047561 DOI: 10.1002/mrm.28050] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 10/02/2019] [Accepted: 10/03/2019] [Indexed: 12/21/2022]
Abstract
PURPOSE The purpose of this study was to further develop and combine several innovative sequence designs to achieve quantitative 3D myocardial perfusion. These developments include an optimized 3D stack-of-stars readout (150 ms per beat), efficient acquisition of a 2D arterial input function, tailored saturation pulse design, and potential whole heart coverage during quantitative stress perfusion. THEORY AND METHODS All studies were performed free-breathing on a Prisma 3T MRI scanner. Phantom validation was used to verify sequence accuracy. A total of 21 subjects (3 patients with known disease) were scanned, 12 with a rest only protocol and 9 with both stress (regadenoson) and rest protocols. First pass quantitative perfusion was performed with gadoteridol (0.075 mmol/kg). RESULTS Implementation and quantitative perfusion results are shown for healthy subjects and subjects with known coronary disease. Average rest perfusion for the 15 included healthy subjects was 0.79 ± 0.19 mL/g/min, the average stress perfusion for 6 healthy subject studies was 2.44 ± 0.61 mL/g/min, and the average global myocardial perfusion reserve ratio for 6 healthy subjects was 3.10 ± 0.24. Perfusion deficits for 3 patients with ischemia are shown. Average resting heart rate was 59 ± 7 bpm and the average stress heart rate was 81 ± 10 bpm. CONCLUSION This work demonstrates that a quantitative 3D myocardial perfusion sequence with the acquisition of a 2D arterial input function is feasible at high stress heart rates such as during stress. T1 values and gadolinium concentrations of the sequence match the reference standard well in a phantom, and myocardial rest and stress perfusion and myocardial perfusion reserve values are consistent with those published in literature.
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Affiliation(s)
- Jason Kraig Mendes
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
| | - Ganesh Adluru
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
| | - Devavrat Likhite
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
| | - Merlin J Fair
- Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Peter D Gatehouse
- Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Ye Tian
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
| | - Apoorva Pedgaonkar
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
| | - Brent Wilson
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
| | - Edward VR DiBella
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City UT, USA
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Fan L, Shen D, Haji-Valizadeh H, Naresh NK, Carr JC, Freed BH, Lee DC, Kim D. Rapid dealiasing of undersampled, non-Cartesian cardiac perfusion images using U-net. NMR IN BIOMEDICINE 2020; 33:e4239. [PMID: 31943431 PMCID: PMC7165063 DOI: 10.1002/nbm.4239] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 11/15/2019] [Accepted: 11/18/2019] [Indexed: 05/25/2023]
Abstract
Compressed sensing (CS) is a promising method for accelerating cardiac perfusion MRI to achieve clinically acceptable image quality with high spatial resolution (1.6 × 1.6 × 8 mm3 ) and extensive myocardial coverage (6-8 slices per heartbeat). A major disadvantage of CS is its relatively lengthy processing time (~8 min per slice with 64 frames using a graphics processing unit), thereby making it impractical for clinical translation. The purpose of this study was to implement and test whether an image reconstruction pipeline including a neural network is capable of reconstructing 6.4-fold accelerated, non-Cartesian (radial) cardiac perfusion k-space data at least 10 times faster than CS, without significant loss in image quality. We implemented a 3D (2D + time) U-Net and trained it with 132 2D + time datasets (coil combined, zero filled as input; CS reconstruction as reference) with 64 time frames from 28 patients (8448 2D images in total). For testing, we used 56 2D + time coil-combined, zero-filled datasets (3584 2D images in total) from 12 different patients as input to our trained U-Net, and compared the resulting images with CS reconstructed images using quantitative metrics of image quality and visual scores (conspicuity of wall enhancement, noise, artifacts; each score ranging from 1 (worst) to 5 (best), with 3 defined as clinically acceptable) evaluated by readers. Including pre- and post-processing steps, compared with CS, U-Net significantly reduced the reconstruction time by 14.4-fold (32.1 ± 1.4 s for U-Net versus 461.3 ± 16.9 s for CS, p < 0.001), while maintaining high data fidelity (structural similarity index = 0.914 ± 0.023, normalized root mean square error = 1.7 ± 0.3%, identical mean edge sharpness of 1.2 mm). The median visual summed score was not significantly different (p = 0.053) between CS (14; interquartile range (IQR) = 0.5) and U-Net (12; IQR = 0.5). This study shows that the proposed pipeline with a U-Net is capable of reconstructing 6.4-fold accelerated, non-Cartesian cardiac perfusion k-space data 14.4 times faster than CS, without significant loss in data fidelity or image quality.
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Affiliation(s)
- Lexiaozi Fan
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
| | - Daming Shen
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
| | - Hassan Haji-Valizadeh
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
| | | | - James C. Carr
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Benjamin H. Freed
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Daniel C. Lee
- Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Daniel Kim
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL
- Department of Biomedical Engineering, Northwestern University, Evanston, IL
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Hong K, Collins JD, Freed BH, Fan L, Arai AE, Hsu LY, Lee DC, Kim D. Accelerated Wideband Myocardial Perfusion Pulse Sequence with Compressed Sensing Reconstruction for Myocardial Blood Flow Quantification in Patients with a Cardiac Implantable Electronic Device. Radiol Cardiothorac Imaging 2020; 2:e190114. [PMID: 32420548 DOI: 10.1148/ryct.2020190114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 09/17/2019] [Accepted: 10/02/2019] [Indexed: 11/11/2022]
Abstract
Purpose To develop an accelerated wideband cardiac perfusion pulse sequence and test whether it can produce diagnostically acceptable image quality and whether it can be used to reliably quantify myocardial blood flow (MBF) in patients with a cardiac implantable electronic device (CIED). Materials and Methods A fivefold-accelerated wideband perfusion pulse sequence was developed using compressed sensing to sample one arterial input function plane and three myocardial perfusion (MP) planes per heartbeat in patients with a CIED with heart rates as high as 102 beats per minute. Resting perfusion scans were performed in 10 patients with a CIED and in 10 patients with no device as a control group. Two clinical readers compared the resulting images and retrospective images of the 10 patients with a CIED, which were obtained by using a previously described twofold-accelerated wideband perfusion pulse sequence with temporal generalized autocalibrating partially parallel acquisition. Summed visual score (SVS) was defined as the sum of conspicuity, artifact, and noise scores individually ranging from 1 (worst) to 5 (best). Resting MBF in the remote zones was quantified using Fermi deconvolution. Results Median SVS was significantly different (P < .05) between the prospective and retrospective CIED groups (13 vs nine) and between the nondevice group and the retrospective CIED group (13.5 vs nine); all median SVSs were nine or greater (clinically acceptable cut point). The median resting MBF in remote zones was not significantly different (P = .27) between patients with a CIED (1.1 mL/min/g; median left ventricular ejection fraction [LVEF], 52.5%) and patients with no device (1.3 mL/min/g; median LVEF, 64.0%). Mean MBF values were consistent with those (mean resting MBF range, 1.0-1.2 mL/min/g) reported by two prior state-of-the-art cardiac perfusion MRI studies. Conclusion The proposed scan yielded diagnostically acceptable image quality and enabled reliable quantification of MBF with three MP planes per heartbeat in patients with a CIED with heart rates as high as 102 beats per minute. Supplemental material is available for this article. © RSNA, 2020.
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Affiliation(s)
- KyungPyo Hong
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Jeremy D Collins
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Benjamin H Freed
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Lexiaozi Fan
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Andrew E Arai
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Li-Yueh Hsu
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Daniel C Lee
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
| | - Daniel Kim
- Department of Radiology (K.P.H., L.F., D.K.) and Division of Cardiology, Department of Internal Medicine (B.H.F., D.C.L.), Northwestern University Feinberg School of Medicine, 737 N Michigan Ave, Suite 1600, Chicago, IL 60611; Department of Radiology, Mayo Clinic, Rochester, Minn (J.D.C.); Laboratory for Advanced Cardiovascular Imaging, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Md (A.E.A., L.Y.H.); and Department of Biomedical Engineering, Northwestern University, Evanston, Ill (L.F., D.K.)
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