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Rezaeian P, Shufelt C, Wei J, Pacheco C, Cook-Wiens G, Berman D, Tamarappoo B, Thomson L, Nelson M, Anderson R, Petersen J, Handberg E, Pepine C, Merz CB. Arterial stiffness assessment in coronary microvascular dysfunction and heart failure with preserved ejection fraction: An initial report from the WISE-CVD continuation study. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2024; 41:100390. [PMID: 38600957 PMCID: PMC11004063 DOI: 10.1016/j.ahjo.2024.100390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 03/29/2024] [Indexed: 04/12/2024]
Abstract
Background Heart failure with preserved ejection fraction (HFpEF) is the most common cardiac complication in patients with coronary microvascular dysfunction (CMD), yet its underlying pathways remain unclear. Aortic pulse-wave velocity (aPWV) is an indicator of large artery stiffness and a predictor for cardiovascular disease. However, aPWV in CMD and HFpEF is not well characterized and may provide understanding of disease progression. Methods Among participants without obstructive coronary artery disease, we evaluated 51 women with suspected CMD and 20 women and men with evidence of HFpEF. All participants underwent aPWV measurement (SphygmoCor, Atcor Medical) with higher aPWV indicating greater vascular stiffness. Cardiac magnetic resonance imaging (CMRI) assessed left ventricular (LV) ejection fraction, CMD via myocardial perfusion reserve index (MPRI), and ventricular remodeling via LV mass-volume ratio. . Statistical analysis was performed using Wilcoxon rank sum tests, Pearson correlations and linear regression analysis. Results Compared to the suspected CMD group, the HFpEF participants were older (65 ± 12 vs 56 ± 11 yrs., p = 0.002) had higher BMI (31.0 ± 4.3 vs 27.8 ± 6.7 kg/m2, p = 0.013), higher aPWV (10.5 ± 2.0 vs 8.0 ± 1.6 m/s, p = 0.05) and lower MPRI (1.5 ± 0.3 vs1.8 ± 0.3, p = 0.02), but not remodeling. In a model adjusted for cardiovascular risk factors, the HFpEF group had a lower LVEF (estimate -4.78, p = 0.0437) than the suspected CMD group. Conclusions HFpEF participants exhibit greater arterial stiffness and lower myocardial perfusion reserve, with lower LVEF albeit not remodeling, compared to suspected CMD participants. These findings suggest arterial stiffness may contribute to progression from CMD to HFpEF. Prospective work is needed and ongoing.
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Affiliation(s)
- P. Rezaeian
- Torrance Memorial Medical Center-A Cedars-Sinai Affiliate, Torrance, CA, USA
| | - C.L. Shufelt
- Division of General Internal Medicine, Mayo Clinic, Jacksonville, FL, USA
| | - J. Wei
- Barbra Streisand Women's Heart Center, Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
| | - C. Pacheco
- Hôspital Pierre-Boucher, Centre Hospitalier de l'Université de Montréal, Université de Montreal, QC, Canada
| | - G. Cook-Wiens
- Torrance Memorial Medical Center-A Cedars-Sinai Affiliate, Torrance, CA, USA
| | - D. Berman
- Taper Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - B. Tamarappoo
- Taper Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - L.E. Thomson
- Taper Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - M.D. Nelson
- The University of Texas at Arlington, Arlington, TX, USA
| | - R.D. Anderson
- Division of Cardiology, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - J. Petersen
- Division of Cardiology, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - E.M. Handberg
- Division of Cardiology, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - C.J. Pepine
- Division of Cardiology, Department of Medicine, University of Florida, Gainesville, FL, USA
| | - C.N. Bairey Merz
- Barbra Streisand Women's Heart Center, Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA
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Yalcinkaya DM, Youssef K, Heydari B, Simonetti O, Dharmakumar R, Raman S, Sharif B. Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets. ARXIV 2023:arXiv:2308.13488v2. [PMID: 37664410 PMCID: PMC10473819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score ( p < 0.001 ) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.
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Affiliation(s)
- Dilek M Yalcinkaya
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Elmore Family School of Electrical & Computer Engineering, Purdue University, West Lafayette, IN, USA
| | - Khalid Youssef
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
| | - Bobak Heydari
- Stephenson Cardiac Imaging Centre, University of Calgary, Alberta, Canada
| | - Orlando Simonetti
- Department of Internal Medicine, Division of Cardiovascular Medicine, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, USA
| | - Rohan Dharmakumar
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
- Weldon School of Biomedical Eng., Purdue University, West Lafayette, IN, USA
| | - Subha Raman
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
- Weldon School of Biomedical Eng., Purdue University, West Lafayette, IN, USA
| | - Behzad Sharif
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
- Weldon School of Biomedical Eng., Purdue University, West Lafayette, IN, USA
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3
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Yalcinkaya DM, Youssef K, Heydari B, Simonetti O, Dharmakumar R, Raman S, Sharif B. Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14222:453-462. [PMID: 38204763 PMCID: PMC10775176 DOI: 10.1007/978-3-031-43898-1_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p < 0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.
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Affiliation(s)
- Dilek M Yalcinkaya
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
| | - Khalid Youssef
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
| | - Bobak Heydari
- Stephenson Cardiac Imaging Centre, University of Calgary, Alberta, Canada
| | - Orlando Simonetti
- Department of Internal Medicine, Division of Cardiovascular Medicine, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, USA
| | - Rohan Dharmakumar
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Subha Raman
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
| | - Behzad Sharif
- Laboratory for Translational Imaging of Microcirculation, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA
- Krannert Cardiovascular Research Center, IUSM/IU Health Cardiovascular Institute, Indianapolis, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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Hutchens JA, Johnson TR, Payne RM. Myocardial Perfusion Reserve in Children with Friedreich Ataxia. Pediatr Cardiol 2021; 42:1834-1840. [PMID: 34245318 DOI: 10.1007/s00246-021-02675-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 06/30/2021] [Indexed: 11/27/2022]
Abstract
Children with Friedreich's ataxia (FA) are at risk of perioperative morbidity and mortality from severe unpredictable heart failure. There is currently no clear way of identifying patients at highest risk. We used myocardial perfusion reserve (MPR), an MRI technique used to assess the maximal myocardial blood flow above baseline, to help determine potential surgical risk in FA subjects. In total, seven children with genetically confirmed FA, ages 8-17 years, underwent MPR stress testing using regadenoson. Six of the seven demonstrated impaired endocardial perfusion during coronary hyperemia. The same six were also found to have evidence of ongoing myocardial damage as illustrated by cardiac troponin I leak (range 0.04-0.17 ng/mL, normal < 0.03 ng/mL). None of the patients had a reduced ejection fraction (range 59-74%) or elevated insulin level (range 2.46-14.23 mCU/mL). This retrospective study shows that children with FA develop MPR defects early in the disease process. It also suggests MPR may be a sensitive tool to evaluate underlying cardiac compromise and could be of use in directing surgical management decisions in children with FA.
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Affiliation(s)
| | - Tiffanie R Johnson
- Indiana University School of Medicine, Indianapolis, IN, USA.,Division of Pediatric Cardiology, Riley Hospital for Children, Indiana University School of Medicine, 1044 West Walnut St, Room R4-302b, Indianapolis, IN, 46202, USA
| | - R Mark Payne
- Indiana University School of Medicine, Indianapolis, IN, USA. .,Division of Pediatric Cardiology, Riley Hospital for Children, Indiana University School of Medicine, 1044 West Walnut St, Room R4-302b, Indianapolis, IN, 46202, USA. .,Herman B Wells Center for Pediatric Research, Indianapolis, IN, USA.
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Unal HB, Beaulieu T, Rivero LZ, Dharmakumar R, Sharif B. Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4079-4085. [PMID: 34892125 PMCID: PMC9989969 DOI: 10.1109/embc46164.2021.9630270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces the accuracy of diagnosis in patients with suspected ischemic heart disease. The main causes for DRA are known to be Gibbs ringing and bulk motion of the heart. The goal of this work is to propose a deep-learning-enabled automatic approach for the detection of motion-induced DRAs in FPP cMRI datasets. To this end, we propose a new algorithm that can detect the DRA in individual time frames by analyzing multiple reconstructions of the same time frame (k-space data) with varying temporal windows. In addition to DRA detection, our approach is also capable of suppressing the extent and severity of DRAs as a byproduct of the same reconstruction-analysis process. In this proof-of-concept study, our proposed method showed a good performance for automatic detection of subendocardial DRAs in stress perfusion cMRI studies of patients with suspected ischemic heart disease. To the best of our knowledge, this is the first approach that performs deep-learning-enabled detection and suppression of DRAs in cMRI.Clinical Relevance- Our approach enables clinicians to provide a more accurate diagnosis of ischemic heart disease by detecting and suppressing subendocardial dark-rim artifacts in first-pass perfusion cMRI datasets.
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Youssef K, Heydari B, Rivero LZ, Beaulieu T, Cheema K, Dharmakumar R, Sharif B. A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:4045-4051. [PMID: 34892118 PMCID: PMC9989970 DOI: 10.1109/embc46164.2021.9629630] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Quantitative analysis of dynamic contrast-enhanced cardiovascular MRI (cMRI) datasets enables the assessment of myocardial blood flow (MBF) for objective evaluation of ischemic heart disease in patients with suspected coronary artery disease. State-of-the-art MBF quantification techniques use constrained deconvolution and are highly sensitive to noise and motion-induced errors, which can lead to unreliable outcomes in the setting of high-resolution MBF mapping. To overcome these limitations, recent iterative approaches incorporate spatial-smoothness constraints to tackle pixel-wise MBF mapping. However, such iterative methods require a computational time of up to 30 minutes per acquired myocardial slice, which is a major practical limitation. Furthermore, they cannot enforce robustness to residual nonrigid motion which can occur in clinical stress/rest studies of patients with arrhythmia. We present a non-iterative patch-wise deep learning approach for pixel-wise MBF quantification wherein local spatio-temporal features are learned from a large dataset of myocardial patches acquired in clinical stress/rest cMRI studies. Our approach is scanner-independent, computationally efficient, robust to noise, and has the unique feature of robustness to motion-induced errors. Numerical and experimental results obtained using real patient data demonstrate the effectiveness of our approach.Clinical Relevance- The proposed patch-wise deep learning approach significantly improves the reliability of high-resolution myocardial blood flow quantification in cMRI by improving its robustness to noise and nonrigid myocardial motion and is up to 300-fold faster than state-of-the-art iterative approaches.
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7
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Sharif B, Motwani M, Arsanjani R, Dharmakumar R, Fish MB, Germano G, Li D, Berman DS, Slomka P. Impact of incomplete ventricular coverage on diagnostic performance of myocardial perfusion imaging. Int J Cardiovasc Imaging 2017; 34:661-669. [PMID: 29197024 PMCID: PMC5859027 DOI: 10.1007/s10554-017-1265-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 10/26/2017] [Indexed: 12/24/2022]
Abstract
In the context of myocardial perfusion imaging (MPI) with cardiac magnetic resonance (CMR), there is ongoing debate on the merits of using technically complex acquisition methods to achieve whole-heart spatial coverage, rather than conventional 3-slice acquisition. An adequately powered comparative study is difficult to achieve given the requirement for two separate stress CMR studies in each patient. The aim of this work is to draw relevant conclusions from SPECT MPI by comparing whole-heart versus simulated 3-slice coverage in a large existing dataset. SPECT data from 651 patients with suspected coronary artery disease who underwent invasive angiography were analyzed. A computational approach was designed to model 3-slice MPI by retrospective subsampling of whole- heart data. For both whole-heart and 3-slice approaches, the diagnostic performance and the stress total perfusion deficit (TPD) score-a measure of ischemia extent/severity-were quantified and compared. Diagnostic accuracy for the 3-slice and whole-heart approaches were similar (area under the curve: 0.843 vs. 0.855, respectively; P = 0.07). The majority (54%) of cases missed by 3-slice imaging had primarily apical ischemia. Whole-heart and 3-slice TPD scores were strongly correlated (R2 = 0.93, P < 0.001) but 3-slice TPD showed a small yet significant bias compared to whole-heart TPD (- 1.19%; P < 0.0001) and the 95% limits of agreement were relatively wide (- 6.65% to 4.27%). Incomplete ventricular coverage typically acquired in 3-slice CMR MPI does not significantly affect the diagnostic accuracy. However, 3-slice MPI may fail to detect severe apical ischemia and underestimate the extent/severity of perfusion defects. Our results suggest that caution is required when comparing the ischemic burden between 3-slice and whole-heart datasets, and corroborate the need to establish prognostic thresholds specific to each approach.
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Affiliation(s)
- Behzad Sharif
- Laboratory for Translational Imaging of Microcirculation, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
- David Geffen School of Medicine at UCLA, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
| | - Manish Motwani
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
| | - Reza Arsanjani
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Division of Cardiovascular Medicine, Mayo Clinic, 13400 E Shea Blvd, Scottsdale, AZ, 85259, USA
| | - Rohan Dharmakumar
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- David Geffen School of Medicine at UCLA, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Mathews B Fish
- Oregon Heart and Vascular Institute, Sacred Heart Medical Center, 3311 Riverbend Dr, Springfield, OR, 97477, USA
| | - Guido Germano
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- David Geffen School of Medicine at UCLA, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- David Geffen School of Medicine at UCLA, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Daniel S Berman
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA
- David Geffen School of Medicine at UCLA, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA
| | - Piotr Slomka
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
- Departments of Imaging and Medicine, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
- David Geffen School of Medicine at UCLA, University of California Los Angeles, 757 Westwood Plaza, Los Angeles, CA, 90095, USA.
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Kellman P, Hansen MS, Nielles-Vallespin S, Nickander J, Themudo R, Ugander M, Xue H. Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification. J Cardiovasc Magn Reson 2017; 19:43. [PMID: 28385161 PMCID: PMC5383963 DOI: 10.1186/s12968-017-0355-5] [Citation(s) in RCA: 175] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 03/23/2017] [Indexed: 01/03/2023] Open
Abstract
BACKGROUND Quantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium. METHODS A dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress. RESULTS For phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 ± 1.2 for stress and 4.4 ± 1.2 mmol/L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 ± 6.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. The estimated perfusion flow was 3.9 ± 0.38 and 1.03 ± 0.19 ml/min/g using the BTEX model and 3.4 ± 0.39 and 0.95 ± 0.16 using a Fermi model, for stress and rest, respectively. CONCLUSIONS A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.
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Affiliation(s)
- Peter Kellman
- National Heart, Lung, and Blood Institute, National Institutes of Health, DHHS, 10 Center Drive MSC-1061, Bethesda, MD 20892 USA
| | - Michael S. Hansen
- National Heart, Lung, and Blood Institute, National Institutes of Health, DHHS, 10 Center Drive MSC-1061, Bethesda, MD 20892 USA
| | - Sonia Nielles-Vallespin
- National Heart, Lung, and Blood Institute, National Institutes of Health, DHHS, 10 Center Drive MSC-1061, Bethesda, MD 20892 USA
| | - Jannike Nickander
- Department of Clinical Physiology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Raquel Themudo
- Department of Clinical Physiology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Martin Ugander
- Department of Clinical Physiology, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Hui Xue
- National Heart, Lung, and Blood Institute, National Institutes of Health, DHHS, 10 Center Drive MSC-1061, Bethesda, MD 20892 USA
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Hedgire SS, Osborne M, Verdini DJ, Ghoshhajra BB. Updates on Stress Imaging Testing and Myocardial Viability With Advanced Imaging Modalities. CURRENT TREATMENT OPTIONS IN CARDIOVASCULAR MEDICINE 2017; 19:26. [PMID: 28316034 DOI: 10.1007/s11936-017-0525-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OPINION STATEMENT Non-invasive stress testing plays a key role in diagnosis and risk stratification in patients with coronary artery disease. Technical advances in CT, MRI, and PET have lead to increased utility of these modalities in myocardial perfusion imaging. The aim of the review is to provide a succinct update on CT, PET, and MRI for myocardial stress perfusion imaging.
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Affiliation(s)
- Sandeep S Hedgire
- Department of Radiology, Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Michael Osborne
- Cardiac MR PET-CT Program, Division of Cardiology and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02144, USA
| | - Daniel J Verdini
- Department of Radiology, Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA
| | - Brian B Ghoshhajra
- Department of Radiology, Division of Cardiovascular Imaging, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA, 02114, USA.
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