Pub Date : 2024-06-01Epub Date: 2024-03-01DOI: 10.1016/j.jocmr.2024.101031
Tobias Hoh, Isabel Margolis, Jonathan Weine, Thomas Joyce, Robert Manka, Miriam Weisskopf, Nikola Cesarovic, Maximilian Fuetterer, Sebastian Kozerke
Background: Automatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data.
Methods: Thirty-six high-resolution (0.7×0.7×2.0 mm3) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δxtrain = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δxtest = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δxtrain = 0.7 to 1.7 mm) was tested.
Results: The prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and - 0.5 - 0.0 p.p. (2.00 - 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 - 1.69) for all investigated test resolutions.
Conclusion: A mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.
{"title":"Impact of late gadolinium enhancement image acquisition resolution on neural network based automatic scar segmentation.","authors":"Tobias Hoh, Isabel Margolis, Jonathan Weine, Thomas Joyce, Robert Manka, Miriam Weisskopf, Nikola Cesarovic, Maximilian Fuetterer, Sebastian Kozerke","doi":"10.1016/j.jocmr.2024.101031","DOIUrl":"10.1016/j.jocmr.2024.101031","url":null,"abstract":"<p><strong>Background: </strong>Automatic myocardial scar segmentation from late gadolinium enhancement (LGE) images using neural networks promises an alternative to time-consuming and observer-dependent semi-automatic approaches. However, alterations in data acquisition, reconstruction as well as post-processing may compromise network performance. The objective of the present work was to systematically assess network performance degradation due to a mismatch of point-spread function between training and testing data.</p><p><strong>Methods: </strong>Thirty-six high-resolution (0.7×0.7×2.0 mm<sup>3</sup>) LGE k-space datasets were acquired post-mortem in porcine models of myocardial infarction. The in-plane point-spread function and hence in-plane resolution Δx was retrospectively degraded using k-space lowpass filtering, while field-of-view and matrix size were kept constant. Manual segmentation of the left ventricle (LV) and healthy remote myocardium was performed to quantify location and area (% of myocardium) of scar by thresholding (≥ SD5 above remote). Three standard U-Nets were trained on training resolutions Δx<sub>train</sub> = 0.7, 1.2 and 1.7 mm to predict endo- and epicardial borders of LV myocardium and scar. The scar prediction of the three networks for varying test resolutions (Δx<sub>test</sub> = 0.7 to 1.7 mm) was compared against the reference SD5 thresholding at 0.7 mm. Finally, a fourth network trained on a combination of resolutions (Δx<sub>train</sub> = 0.7 to 1.7 mm) was tested.</p><p><strong>Results: </strong>The prediction of relative scar areas showed the highest precision when the resolution of the test data was identical to or close to the resolution used during training. The median fractional scar errors and precisions (IQR) from networks trained and tested on the same resolution were 0.0 percentage points (p.p.) (1.24 - 1.45), and - 0.5 - 0.0 p.p. (2.00 - 3.25) for networks trained and tested on the most differing resolutions, respectively. Deploying the network trained on multiple resolutions resulted in reduced resolution dependency with median scar errors and IQRs of 0.0 p.p. (1.24 - 1.69) for all investigated test resolutions.</p><p><strong>Conclusion: </strong>A mismatch of the imaging point-spread function between training and test data can lead to degradation of scar segmentation when using current U-Net architectures as demonstrated on LGE porcine myocardial infarction data. Training networks on multi-resolution data can alleviate the resolution dependency.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10981112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140021863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-03-22DOI: 10.1016/j.jocmr.2024.101039
Andrew Phair, Anastasia Fotaki, Lina Felsner, Thomas J Fletcher, Haikun Qi, René M Botnar, Claudia Prieto
Background: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort.
Methods: The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST).
Results: Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant.
Conclusion: The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.
{"title":"A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease.","authors":"Andrew Phair, Anastasia Fotaki, Lina Felsner, Thomas J Fletcher, Haikun Qi, René M Botnar, Claudia Prieto","doi":"10.1016/j.jocmr.2024.101039","DOIUrl":"10.1016/j.jocmr.2024.101039","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acquisition involve long and unpredictable scan times and methods that accelerate scans via k-space undersampling often rely on long iterative reconstructions. Deep-learning-based reconstruction methods have recently attracted much interest due to their capacity to provide fast reconstructions while often outperforming existing state-of-the-art methods. In this study, we sought to adapt and validate a non-rigid motion-corrected model-based deep learning (MoCo-MoDL) reconstruction framework for 3D whole-heart MRI in a CHD patient cohort.</p><p><strong>Methods: </strong>The previously proposed deep-learning reconstruction framework MoCo-MoDL, which incorporates a non-rigid motion-estimation network and a denoising regularization network within an unrolled iterative reconstruction, was trained in an end-to-end manner using 39 CHD patient datasets. Once trained, the framework was evaluated in eight CHD patient datasets acquired with seven-fold prospective undersampling. Reconstruction quality was compared with the state-of-the-art non-rigid motion-corrected patch-based low-rank reconstruction method (NR-PROST) and against reference images (acquired with three-or-four-fold undersampling and reconstructed with NR-PROST).</p><p><strong>Results: </strong>Seven-fold undersampled scan times were 2.1 ± 0.3 minutes and reconstruction times were ∼30 seconds, approximately 240 times faster than an NR-PROST reconstruction. Image quality comparable to the reference images was achieved using the proposed MoCo-MoDL framework, with no statistically significant differences found in any of the assessed quantitative or qualitative image quality measures. Additionally, expert image quality scores indicated the MoCo-MoDL reconstructions were consistently of a higher quality than the NR-PROST reconstructions of the same data, with the differences in 12 of the 22 scores measured for individual vascular structures found to be statistically significant.</p><p><strong>Conclusion: </strong>The MoCo-MoDL framework was applied to an adult CHD patient cohort, achieving good quality 3D whole-heart images from ∼2-minute scans with reconstruction times of ∼30 seconds.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10993190/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140193875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: The prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up.
Methods: The study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission.
Results: Among 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5-19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8-50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91-0.96; p < 0.001) and native T1 (HR, 1.01; 95% CI, 1.00-1.01; p = 0.030) were independent predictors of MACE. Follow-up LVEF ≥ 40% or stable LVEF < 40% with T1 ≤ 1273 ms indicated low risk (annual event rate < 4%), while stable LVEF < 40% and T1 > 1273 ms or LVEF < 40% with deterioration indicated high risk (annual event rate > 15%).
Conclusions: Follow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.
{"title":"Prognostic value of mid-term cardiovascular magnetic resonance follow-up in patients with non-ischemic dilated cardiomyopathy: a prospective cohort study.","authors":"Yuanwei Xu, Yangjie Li, Shiqian Wang, Ke Wan, Yinxi Tan, Weihao Li, Jie Wang, Jiajun Guo, Saeed Ghaithan, Wei Cheng, Jiayu Sun, Qing Zhang, Yuchi Han, Yucheng Chen","doi":"10.1016/j.jocmr.2024.101002","DOIUrl":"10.1016/j.jocmr.2024.101002","url":null,"abstract":"<p><strong>Background: </strong>The prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up.</p><p><strong>Methods: </strong>The study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission.</p><p><strong>Results: </strong>Among 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5-19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8-50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91-0.96; p < 0.001) and native T1 (HR, 1.01; 95% CI, 1.00-1.01; p = 0.030) were independent predictors of MACE. Follow-up LVEF ≥ 40% or stable LVEF < 40% with T1 ≤ 1273 ms indicated low risk (annual event rate < 4%), while stable LVEF < 40% and T1 > 1273 ms or LVEF < 40% with deterioration indicated high risk (annual event rate > 15%).</p><p><strong>Conclusions: </strong>Follow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10926272/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139491369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-02-24DOI: 10.1016/j.jocmr.2024.101030
Kevin Bouaou, Thomas Dietenbeck, Gilles Soulat, Ioannis Bargiotas, Sophia Houriez-Gombaud-Saintonge, Alain De Cesare, Umit Gencer, Alain Giron, Elena Jiménez, Emmanuel Messas, Didier Lucor, Emilie Bollache, Elie Mousseaux, Nadjia Kachenoura
Background: Ascending thoracic aortic aneurysm (ATAA) is a silent and threatening dilation of the ascending aorta (AscAo). Maximal aortic diameter which is currently used for ATAA patients management and surgery planning has been shown to inadequately characterize risk of dissection in a large proportion of patients. Our aim was to propose a comprehensive quantitative evaluation of aortic morphology and pressure-flow-wall associations from four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) data in healthy aging and in patients with ATAA.
Methods: We studied 17 ATAA patients (64.7 ± 14.3 years, 5 females) along with 17 age- and sex-matched healthy controls (59.7 ± 13.3 years, 5 females) and 13 younger healthy subjects (33.5 ± 11.1 years, 4 females). All subjects underwent a CMR exam, including 4D flow and three-dimensional anatomical images of the aorta. This latter dataset was used for aortic morphology measurements, including AscAo maximal diameter (iDMAX) and volume, indexed to body surface area. 4D flow MRI data were used to estimate 1) cross-sectional local AscAo spatial (∆PS) and temporal (∆PT) pressure changes as well as the distance (∆DPS) and time duration (∆TPT) between local pressure peaks, 2) AscAo maximal wall shear stress (WSSMAX) at peak systole, and 3) AscAo flow vorticity amplitude (VMAX), duration (VFWHM), and eccentricity (VECC).
Results: Consistency of flow and pressure indices was demonstrated through their significant associations with AscAo iDMAX (WSSMAX:r = -0.49, p < 0.001; VECC:r = -0.29, p = 0.045; VFWHM:r = 0.48, p < 0.001; ∆DPS:r = 0.37, p = 0.010; ∆TPT:r = -0.52, p < 0.001) and indexed volume (WSSMAX:r = -0.63, VECC:r = -0.51, VFWHM:r = 0.53, ∆DPS:r = 0.54, ∆TPT:r = -0.63, p < 0.001 for all). Intra-AscAo cross-sectional pressure difference, ∆PS, was significantly and positively associated with both VMAX (r = 0.55, p = 0.002) and WSSMAX (r = 0.59, p < 0.001) in the 30 healthy subjects (48.3 ± 18.0 years). Associations remained significant after adjustment for iDMAX, age, and systolic blood pressure. Superimposition of ATAA patients to normal aging trends between ∆PS and WSSMAX as well as VMAX allowed identifying patients with substantially high pressure differences concomitant with AscAo dilation.
Conclusion: Local variations in pressures within ascending aortic cross-sections derived from 4D flow MRI were associated with flow changes, as quantified by vorticity, and with stress exerted by blood on the aortic wall, as quantified by wall shear stress. Such flow-wall and pressure interactions might help for the identification
{"title":"Four-dimensional flow cardiovascular magnetic resonance aortic cross-sectional pressure changes and their associations with flow patterns in health and ascending thoracic aortic aneurysm.","authors":"Kevin Bouaou, Thomas Dietenbeck, Gilles Soulat, Ioannis Bargiotas, Sophia Houriez-Gombaud-Saintonge, Alain De Cesare, Umit Gencer, Alain Giron, Elena Jiménez, Emmanuel Messas, Didier Lucor, Emilie Bollache, Elie Mousseaux, Nadjia Kachenoura","doi":"10.1016/j.jocmr.2024.101030","DOIUrl":"10.1016/j.jocmr.2024.101030","url":null,"abstract":"<p><strong>Background: </strong>Ascending thoracic aortic aneurysm (ATAA) is a silent and threatening dilation of the ascending aorta (AscAo). Maximal aortic diameter which is currently used for ATAA patients management and surgery planning has been shown to inadequately characterize risk of dissection in a large proportion of patients. Our aim was to propose a comprehensive quantitative evaluation of aortic morphology and pressure-flow-wall associations from four-dimensional (4D) flow cardiovascular magnetic resonance (CMR) data in healthy aging and in patients with ATAA.</p><p><strong>Methods: </strong>We studied 17 ATAA patients (64.7 ± 14.3 years, 5 females) along with 17 age- and sex-matched healthy controls (59.7 ± 13.3 years, 5 females) and 13 younger healthy subjects (33.5 ± 11.1 years, 4 females). All subjects underwent a CMR exam, including 4D flow and three-dimensional anatomical images of the aorta. This latter dataset was used for aortic morphology measurements, including AscAo maximal diameter (iD<sub>MAX</sub>) and volume, indexed to body surface area. 4D flow MRI data were used to estimate 1) cross-sectional local AscAo spatial (∆P<sub>S</sub>) and temporal (∆P<sub>T</sub>) pressure changes as well as the distance (∆D<sub>PS</sub>) and time duration (∆T<sub>PT</sub>) between local pressure peaks, 2) AscAo maximal wall shear stress (WSS<sub>MAX</sub>) at peak systole, and 3) AscAo flow vorticity amplitude (V<sub>MAX</sub>), duration (V<sub>FWHM</sub>), and eccentricity (V<sub>ECC</sub>).</p><p><strong>Results: </strong>Consistency of flow and pressure indices was demonstrated through their significant associations with AscAo iD<sub>MAX</sub> (WSS<sub>MAX</sub>:r = -0.49, p < 0.001; V<sub>ECC</sub>:r = -0.29, p = 0.045; V<sub>FWHM</sub>:r = 0.48, p < 0.001; ∆D<sub>PS</sub>:r = 0.37, p = 0.010; ∆T<sub>PT</sub>:r = -0.52, p < 0.001) and indexed volume (WSS<sub>MAX</sub>:r = -0.63, V<sub>ECC</sub>:r = -0.51, V<sub>FWHM</sub>:r = 0.53, ∆D<sub>PS</sub>:r = 0.54, ∆T<sub>PT</sub>:r = -0.63, p < 0.001 for all). Intra-AscAo cross-sectional pressure difference, ∆P<sub>S</sub>, was significantly and positively associated with both V<sub>MAX</sub> (r = 0.55, p = 0.002) and WSS<sub>MAX</sub> (r = 0.59, p < 0.001) in the 30 healthy subjects (48.3 ± 18.0 years). Associations remained significant after adjustment for iD<sub>MAX</sub>, age, and systolic blood pressure. Superimposition of ATAA patients to normal aging trends between ∆P<sub>S</sub> and WSS<sub>MAX</sub> as well as V<sub>MAX</sub> allowed identifying patients with substantially high pressure differences concomitant with AscAo dilation.</p><p><strong>Conclusion: </strong>Local variations in pressures within ascending aortic cross-sections derived from 4D flow MRI were associated with flow changes, as quantified by vorticity, and with stress exerted by blood on the aortic wall, as quantified by wall shear stress. Such flow-wall and pressure interactions might help for the identification ","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":6.4,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139972024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-01-28DOI: 10.1016/j.jocmr.2024.101003
Datta Singh Goolaub, Christopher K Macgowan
Background: Non-Cartesian magnetic resonance imaging trajectories at golden angle increments have the advantage of allowing motion correction and gating using intermediate real-time reconstructions. However, when the acquired data are cardiac binned for cine imaging, trajectories can cluster together at certain heart rates (HR) causing image artifacts. Here, we demonstrate an approach to reduce clustering by inserting additional angular increments within the trajectory, and optimizing them while still allowing for intermediate reconstructions.
Methods: Three acquisition models were simulated under constant and variable HR: golden angle (Mtrd), random additional angles (Mrnd), and optimized additional angles (Mopt). The standard deviations of trajectory angular differences (STAD) were compared through their interquartile ranges (IQR) and the Kolmogorov-Smirnov test (significance level: p = 0.05). Agreement between an image reconstructed with uniform sampling and images from Mtrd, Mrnd, and Mopt was analyzed using the structural similarity index measure (SSIM). Mtrd and Mopt were compared in three adults at high, low, and no HR variability.
Results: STADs from Mtrd were significantly different (p < 0.05) from Mopt and Mrnd. STAD (IQR × 10-2 rad) showed that Mopt (0.5) and Mrnd (0.5) reduced clustering relative to Mtrd (1.9) at constant HR. For variable HR, Mopt (0.5) and Mrnd (0.5) outperformed Mtrd (0.9). The SSIM (IQR) showed that Mopt (0.011) produced the best image quality, followed by Mrnd (0.014), and Mtrd (0.030). Mopt outperformed Mtrd at reduced HR variability in in-vivo studies. At high HR variability, both models performed well.
Conclusion: This approach reduces clustering in k-space and improves image quality.
背景:使用黄金角增量的非笛卡尔磁共振成像轨迹的优点是可以利用中间实时重建进行回溯运动校正和基于图像的选通。然而,当获取的数据被心脏分档用于 CINE 成像时,会发现在特定心率下轨迹会聚集在一起,并在 k 空间中留下较大的未采样间隙,从而导致图像伪影。在这项工作中,我们(1)展示了一种通过在轨迹中周期性插入额外角度旋转来减少聚类的方法,(2)使用粒子群优化来优化这些额外角度,同时仍然允许重要的中间重建:模拟了恒定和可变心率下的三种采集模型:传统黄金角度(Mtrd)、随机附加角度(Mrnd)和优化附加角度(Mopt)。为了分析聚类情况,计算了轨迹角差的标准偏差(STAD)。通过四分位数间范围和 Kolmogorov-Smirnov 检验(显著性水平:P = 0.05)对 STAD 的分布进行比较。通过计算结构相似性指数(SSIM)及其四分位数间范围,分析了采用均匀采样重建的参考图像与通过 Mtrd、Mrnd 和 Mopt 获得的图像之间的一致性。然后对 3 名健康成人在 3 种心率变异水平(高、低和无)下的 Mtrd 和 Mopt 进行了比较:结果:Mtrd 的 STAD 分布与 Mopt 和 Mrnd 的 STAD 分布有显著差异(p < 0.05)。STAD(四分位数间距 x 10-2rad)显示,与 Mtrd(1.9)相比,在恒定心率下,Mopt(0.5)和 Mrnd(0.5)减少了聚类。同样,在心率可变的情况下,Mopt (0.5) 和 Mrnd (0.5) 也优于 Mtrd (0.9)。建议的方法降低了聚类风险。相对于地面实况重建的 SSIM(四分位间范围)显示,Mopt(0.011)生成的图像质量最好,其次是 Mrnd(0.014),而 Mtrd(0.030)生成的图像质量最差。体内研究表明,在心率变异性降低的情况下,Mopt 的图像质量优于 Mtrd,而且聚类风险也降低了。在心率变异性较高的情况下,两种模型都表现良好:这种方法减少了 k 空间中的聚类现象,在不影响采集时间的情况下提高了图像质量。
{"title":"Reducing clustering of readouts in non-Cartesian cine magnetic resonance imaging.","authors":"Datta Singh Goolaub, Christopher K Macgowan","doi":"10.1016/j.jocmr.2024.101003","DOIUrl":"10.1016/j.jocmr.2024.101003","url":null,"abstract":"<p><strong>Background: </strong>Non-Cartesian magnetic resonance imaging trajectories at golden angle increments have the advantage of allowing motion correction and gating using intermediate real-time reconstructions. However, when the acquired data are cardiac binned for cine imaging, trajectories can cluster together at certain heart rates (HR) causing image artifacts. Here, we demonstrate an approach to reduce clustering by inserting additional angular increments within the trajectory, and optimizing them while still allowing for intermediate reconstructions.</p><p><strong>Methods: </strong>Three acquisition models were simulated under constant and variable HR: golden angle (M<sub>trd</sub>), random additional angles (M<sub>rnd</sub>), and optimized additional angles (M<sub>opt</sub>). The standard deviations of trajectory angular differences (STAD) were compared through their interquartile ranges (IQR) and the Kolmogorov-Smirnov test (significance level: p = 0.05). Agreement between an image reconstructed with uniform sampling and images from M<sub>trd</sub>, M<sub>rnd</sub>, and M<sub>opt</sub> was analyzed using the structural similarity index measure (SSIM). M<sub>trd</sub> and M<sub>opt</sub> were compared in three adults at high, low, and no HR variability.</p><p><strong>Results: </strong>STADs from M<sub>trd</sub> were significantly different (p < 0.05) from M<sub>opt</sub> and M<sub>rnd</sub>. STAD (IQR × 10<sup>-2</sup> rad) showed that M<sub>opt</sub> (0.5) and M<sub>rnd</sub> (0.5) reduced clustering relative to M<sub>trd</sub> (1.9) at constant HR. For variable HR, M<sub>opt</sub> (0.5) and M<sub>rnd</sub> (0.5) outperformed M<sub>trd</sub> (0.9). The SSIM (IQR) showed that M<sub>opt</sub> (0.011) produced the best image quality, followed by M<sub>rnd</sub> (0.014), and M<sub>trd</sub> (0.030). M<sub>opt</sub> outperformed M<sub>trd</sub> at reduced HR variability in in-vivo studies. At high HR variability, both models performed well.</p><p><strong>Conclusion: </strong>This approach reduces clustering in k-space and improves image quality.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139642209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-01-10DOI: 10.1016/j.jocmr.2023.100003
Xiaowu Sun, Li-Hsin Cheng, Sven Plein, Pankaj Garg, Rob J van der Geest
Background: 4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly.
Methods: We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results.
Results: Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation.
Conclusions: Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition.
{"title":"Deep learning based automated left ventricle segmentation and flow quantification in 4D flow cardiac MRI.","authors":"Xiaowu Sun, Li-Hsin Cheng, Sven Plein, Pankaj Garg, Rob J van der Geest","doi":"10.1016/j.jocmr.2023.100003","DOIUrl":"10.1016/j.jocmr.2023.100003","url":null,"abstract":"<p><strong>Background: </strong>4D flow MRI enables assessment of cardiac function and intra-cardiac blood flow dynamics from a single acquisition. However, due to the poor contrast between the chambers and surrounding tissue, quantitative analysis relies on the segmentation derived from a registered cine MRI acquisition. This requires an additional acquisition and is prone to imperfect spatial and temporal inter-scan alignment. Therefore, in this work we developed and evaluated deep learning-based methods to segment the left ventricle (LV) from 4D flow MRI directly.</p><p><strong>Methods: </strong>We compared five deep learning-based approaches with different network structures, data pre-processing and feature fusion methods. For the data pre-processing, the 4D flow MRI data was reformatted into a stack of short-axis view slices. Two feature fusion approaches were proposed to integrate the features from magnitude and velocity images. The networks were trained and evaluated on an in-house dataset of 101 subjects with 67,567 2D images and 3030 3D volumes. The performance was evaluated using various metrics including Dice, average surface distance (ASD), end-diastolic volume (EDV), end-systolic volume (ESV), LV ejection fraction (LVEF), LV blood flow kinetic energy (KE) and LV flow components. The Monte Carlo dropout method was used to assess the confidence and to describe the uncertainty area in the segmentation results.</p><p><strong>Results: </strong>Among the five models, the model combining 2D U-Net with late fusion method operating on short-axis reformatted 4D flow volumes achieved the best results with Dice of 84.52% and ASD of 3.14 mm. The best averaged absolute and relative error between manual and automated segmentation for EDV, ESV, LVEF and KE was 19.93 ml (10.39%), 17.38 ml (22.22%), 7.37% (13.93%) and 0.07 mJ (5.61%), respectively. Flow component results derived from automated segmentation showed high correlation and small average error compared to results derived from manual segmentation.</p><p><strong>Conclusions: </strong>Deep learning-based methods can achieve accurate automated LV segmentation and subsequent quantification of volumetric and hemodynamic LV parameters from 4D flow MRI without requiring an additional cine MRI acquisition.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-01-17DOI: 10.1016/j.jocmr.2024.100999
Motoki Nakazawa, Hidenari Matsumoto, Debiao Li, Piotr J Slomka, Damini Dey, Sebastien Cadet, Koji Isodono, Daisuke Irie, Satoshi Higuchi, Hiroki Tanisawa, Hidefumi Ohya, Ryoji Kitamura, Yoshiaki Komori, Tetsuichi Hondera, Ikumi Sato, Hsu-Lei Lee, Anthony G Christodoulou, Yibin Xie, Toshiro Shinke
Background: High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T1-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR).
Methods: In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIPvol). Specifically, HIPvol was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit.
Results: The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIPvol outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIPvol offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIPvol as an independent predictor of PMI (odds ratio, 1.15 per 10-μL increase; 95% CI, 1.01-1.30, p = 0.035).
Conclusions: Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.
{"title":"Rapid three-dimensional quantification of high-intensity plaques from coronary atherosclerosis T<sub>1</sub>-weighted characterization to predict periprocedural myocardial injury.","authors":"Motoki Nakazawa, Hidenari Matsumoto, Debiao Li, Piotr J Slomka, Damini Dey, Sebastien Cadet, Koji Isodono, Daisuke Irie, Satoshi Higuchi, Hiroki Tanisawa, Hidefumi Ohya, Ryoji Kitamura, Yoshiaki Komori, Tetsuichi Hondera, Ikumi Sato, Hsu-Lei Lee, Anthony G Christodoulou, Yibin Xie, Toshiro Shinke","doi":"10.1016/j.jocmr.2024.100999","DOIUrl":"10.1016/j.jocmr.2024.100999","url":null,"abstract":"<p><strong>Background: </strong>High-intensity plaque (HIP) on magnetic resonance imaging (MRI) has been documented as a powerful predictor of periprocedural myocardial injury (PMI) following percutaneous coronary intervention (PCI). Despite the recent proposal of three-dimensional HIP quantification to enhance the predictive capability, the conventional pulse sequence, which necessitates the separate acquisition of anatomical reference images, hinders accurate three-dimensional segmentation along the coronary vasculature. Coronary atherosclerosis T<sub>1</sub>-weighted characterization (CATCH) enables the simultaneous acquisition of inherently coregistered dark-blood plaque and bright-blood coronary artery images. We aimed to develop a novel HIP quantification approach using CATCH and to ascertain its superior predictive performance compared to the conventional two-dimensional assessment based on plaque-to-myocardium signal intensity ratio (PMR).</p><p><strong>Methods: </strong>In this prospective study, CATCH MRI was conducted before elective stent implantation in 137 lesions from 125 patients. On CATCH images, dedicated software automatically generated tubular three-dimensional volumes of interest on the dark-blood plaque images along the coronary vasculature, based on the precisely matched bright-blood coronary artery images, and subsequently computed PMR and HIP volume (HIP<sub>vol</sub>). Specifically, HIP<sub>vol</sub> was calculated as the volume of voxels with signal intensity exceeding that of the myocardium, weighted by their respective signal intensities. PMI was defined as post-PCI cardiac troponin-T > 5 × the upper reference limit.</p><p><strong>Results: </strong>The entire analysis process was completed within 3 min per lesion. PMI occurred in 44 lesions. Based on the receiver operating characteristic curve analysis, HIP<sub>vol</sub> outperformed PMR for predicting PMI (C-statistics, 0.870 [95% CI, 0.805-0.936] vs. 0.787 [95% CI, 0.706-0.868]; p = 0.001). This result was primarily driven by the higher sensitivity HIP<sub>vol</sub> offered: 0.886 (95% CI, 0.754-0.962) vs. 0.750 for PMR (95% CI, 0.597-0.868; p = 0.034). Multivariable analysis identified HIP<sub>vol</sub> as an independent predictor of PMI (odds ratio, 1.15 per 10-μL increase; 95% CI, 1.01-1.30, p = 0.035).</p><p><strong>Conclusions: </strong>Our semi-automated method of analyzing coronary plaque using CATCH MRI provided rapid HIP quantification. Three-dimensional assessment using this approach had a better ability to predict PMI than conventional two-dimensional assessment.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139491370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-01-17DOI: 10.1016/j.jocmr.2024.100997
Anthony G Christodoulou, Gastao Cruz, Ayda Arami, Sebastian Weingärtner, Jessica Artico, Dana Peters, Nicole Seiberlich
Cardiovascular 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. To this end, the vision of each is 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 will tackle the "all-in-one" approaches, and Part 2 the "real-time" approaches along with an overall summary of these emerging methods.
{"title":"The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1).","authors":"Anthony G Christodoulou, Gastao Cruz, Ayda Arami, Sebastian Weingärtner, Jessica Artico, Dana Peters, Nicole Seiberlich","doi":"10.1016/j.jocmr.2024.100997","DOIUrl":"10.1016/j.jocmr.2024.100997","url":null,"abstract":"<p><p>Cardiovascular 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. To this end, the vision of each is 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 will tackle the \"all-in-one\" approaches, and Part 2 the \"real-time\" approaches along with an overall summary of these emerging methods.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139491374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-01-09DOI: 10.1016/j.jocmr.2023.100005
Kavitha Vimalesvaran, Sameer Zaman, James P Howard, Nikoo Aziminia, Marilena Giannoudi, Henry Procter, Marta Varela, Fatmatulzehra Uslu, Ben Ariff, Nick Linton, Eylem Levelt, Anil A Bharath, Graham D Cole
Background: Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis. We investigated whether this effect could be quantified and used to predict AS severity in comparison to existing gold-standard measurements.
Methods: Multi-centre, multi-vendor retrospective analysis of patients with AS undergoing CMR and transthoracic echocardiography (TTE). Blood signal intensity was measured in a ∼1 cm2 region of interest (ROI) in the aorta and left ventricle (LV) in the 3-chamber bSSFP cine. Because signal intensity varied across patients and scanner vendors, a ratio of the mean signal intensity in the aorta ROI to the LV ROI (Ao:LV) was used. This ratio was compared using Pearson correlations against TTE parameters of AS severity: aortic valve peak velocity, mean pressure gradient and the dimensionless index. The study also assessed whether field strength (1.5 T vs. 3 T) and patient characteristics (presence of bicuspid aortic valves (BAV), dilated aortic root and low flow states) altered this signal relationship.
Results: 314 patients (median age 69 [IQR 57-77], 64% male) who had undergone both CMR and TTE were studied; 84 had severe AS, 78 had moderate AS, 66 had mild AS and 86 without AS were studied as a comparator group. The median time between CMR and TTE was 12 weeks (IQR 4-26). The Ao:LV ratio at 1.5 T strongly correlated with peak velocity (r = -0.796, p = 0.001), peak gradient (r = -0.772, p = 0.001) and dimensionless index (r = 0.743, p = 0.001). An Ao:LV ratio of < 0.86 was 84% sensitive and 82% specific for detecting AS of any severity and a ratio of 0.58 was 83% sensitive and 92% specific for severe AS. The ability of Ao:LV ratio to predict AS severity remained for patients with bicuspid aortic valves, dilated aortic root or low indexed stroke volume. The relationship between Ao:LV ratio and AS severity was weaker at 3 T.
Conclusions: The Ao:LV ratio, derived from bSSFP 3-chamber cine images, shows a good correlation with existing measures of AS severity. It demonstrates utility at 1.5 T and offers an easily calculable metric that can be used at the time of scanning or automated to identify on an adaptive basis which patients benefit from dedicated imaging to assess which patients should have additional sequences to assess AS.
背景:心血管磁共振(CMR)成像是评估主动脉瓣狭窄(AS)严重程度、并存主动脉疾病和并发心肌异常的重要工具。获取这些额外信息需要调整方案和增加扫描时间,但对于大多数没有主动脉瓣狭窄的患者来说并非必要。我们观察到,升主动脉血液在平衡稳态自由前扑(bSSFP)三腔Cine上的相对信号强度在主动脉明显狭窄的患者中通常会降低。与现有的黄金标准测量方法相比,我们研究了这种影响是否可以量化并用于预测强直性脊柱炎的严重程度:方法:对接受 CMR 和经胸超声心动图 (TTE) 检查的 AS 患者进行多中心、多供应商回顾性分析。在三腔 bSSFP cine 中测量主动脉和左心室 (LV) 中约 1 平方厘米感兴趣区 (ROI) 的血液信号强度。由于不同患者和扫描仪供应商的信号强度不同,因此采用了主动脉 ROI 与左心室 ROI 的平均信号强度比值(Ao:LV)。利用皮尔逊相关性将这一比率与 AS 严重程度的 TTE 参数(主动脉瓣峰值速度、平均压力梯度和无量纲指数)进行比较。研究还评估了场强(1.5T 与 3T)和患者特征(是否存在双尖瓣、主动脉根部扩张和低血流状态)是否会改变这种信号关系:研究对象包括314名同时接受CMR和TTE检查的患者(中位年龄69岁[IQR 57-77],64%为男性);其中84名重度AS患者、78名中度AS患者、66名轻度AS患者和86名无AS患者作为对比组。CMR 和 TTE 的中位间隔时间为 12 周(IQR 4-26)。1.5T 下的 Ao:LV 比值与峰值速度(r = -0.796,p=0.001)、峰值梯度(r = -0.772,p=0.001)和无量纲指数(r = 0.743,p = 0.001)密切相关。Ao:LV 比值的结论:从 bSSFP 三腔 cine 图像中得出的 Ao:LV 比值与现有的 AS 严重程度测量指标有很好的相关性。它在 1.5T 下显示出实用性,并提供了一个易于计算的指标,可在扫描时使用或自动使用,以适应性地确定哪些患者可从专用成像中获益,从而评估哪些患者应使用额外序列来评估 AS。
{"title":"Aortic stenosis assessment from the 3-chamber cine: Ratio of balanced steady-state-free-precession (bSSFP) blood signal between the aorta and left ventricle predicts severity.","authors":"Kavitha Vimalesvaran, Sameer Zaman, James P Howard, Nikoo Aziminia, Marilena Giannoudi, Henry Procter, Marta Varela, Fatmatulzehra Uslu, Ben Ariff, Nick Linton, Eylem Levelt, Anil A Bharath, Graham D Cole","doi":"10.1016/j.jocmr.2023.100005","DOIUrl":"10.1016/j.jocmr.2023.100005","url":null,"abstract":"<p><strong>Background: </strong>Cardiovascular magnetic resonance (CMR) imaging is an important tool for evaluating the severity of aortic stenosis (AS), co-existing aortic disease, and concurrent myocardial abnormalities. Acquiring this additional information requires protocol adaptations and additional scanner time, but is not necessary for the majority of patients who do not have AS. We observed that the relative signal intensity of blood in the ascending aorta on a balanced steady state free precession (bSSFP) 3-chamber cine was often reduced in those with significant aortic stenosis. We investigated whether this effect could be quantified and used to predict AS severity in comparison to existing gold-standard measurements.</p><p><strong>Methods: </strong>Multi-centre, multi-vendor retrospective analysis of patients with AS undergoing CMR and transthoracic echocardiography (TTE). Blood signal intensity was measured in a ∼1 cm<sup>2</sup> region of interest (ROI) in the aorta and left ventricle (LV) in the 3-chamber bSSFP cine. Because signal intensity varied across patients and scanner vendors, a ratio of the mean signal intensity in the aorta ROI to the LV ROI (Ao:LV) was used. This ratio was compared using Pearson correlations against TTE parameters of AS severity: aortic valve peak velocity, mean pressure gradient and the dimensionless index. The study also assessed whether field strength (1.5 T vs. 3 T) and patient characteristics (presence of bicuspid aortic valves (BAV), dilated aortic root and low flow states) altered this signal relationship.</p><p><strong>Results: </strong>314 patients (median age 69 [IQR 57-77], 64% male) who had undergone both CMR and TTE were studied; 84 had severe AS, 78 had moderate AS, 66 had mild AS and 86 without AS were studied as a comparator group. The median time between CMR and TTE was 12 weeks (IQR 4-26). The Ao:LV ratio at 1.5 T strongly correlated with peak velocity (r = -0.796, p = 0.001), peak gradient (r = -0.772, p = 0.001) and dimensionless index (r = 0.743, p = 0.001). An Ao:LV ratio of < 0.86 was 84% sensitive and 82% specific for detecting AS of any severity and a ratio of 0.58 was 83% sensitive and 92% specific for severe AS. The ability of Ao:LV ratio to predict AS severity remained for patients with bicuspid aortic valves, dilated aortic root or low indexed stroke volume. The relationship between Ao:LV ratio and AS severity was weaker at 3 T.</p><p><strong>Conclusions: </strong>The Ao:LV ratio, derived from bSSFP 3-chamber cine images, shows a good correlation with existing measures of AS severity. It demonstrates utility at 1.5 T and offers an easily calculable metric that can be used at the time of scanning or automated to identify on an adaptive basis which patients benefit from dedicated imaging to assess which patients should have additional sequences to assess AS.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211219/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139424896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01Epub Date: 2024-02-01DOI: 10.1016/j.jocmr.2024.101005
Wei-Hui Xie, Bing-Hua Chen, Dong-Aolei An, Rui Wu, Ruo-Yang Shi, Yan Zhou, Heng-Fei Cui, Lei Zhao, Lian-Ming Wu
Background: The prognostic value of left ventricular (LV) myocardial trabecular complexity on cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) remains unknown. This study aimed to evaluate the prognostic value of LV myocardial trabecular complexity using fractal analysis in patients with DCM.
Methods: Consecutive patients with DCM who underwent CMR between March 2017 and November 2021 at two hospitals were prospectively enrolled. The primary endpoints were defined as the combination of all-cause death and heart failure hospitalization. The events of cardiac death alone were defined as the secondary endpoints.LV trabeculae complexity was quantified by measuring the fractal dimension (FD) of the endocardial border based on fractal geometry on CMR. Cox proportional hazards regression and Kaplan-Meier survival analysis were used to examine the association between variables and outcomes. The incremental prognostic value of FD was assessed in nested models.
Results: A total of 403 patients with DCM (49.31 ± 14.68 years, 69% male) were recruited. After a median follow-up of 43 months (interquartile range, 28-55 months), 87 and 24 patients reached the primary and secondary endpoints, respectively. Age, heart rate, New York Heart Association functional class >II, N-terminal pro-B-type natriuretic peptide, LV ejection fraction, LV end-diastolic volume index, LV end-systolic volume index, LV mass index, presence of late gadolinium enhancement, global FD, LV mean apical FD, and LV maximal apical FD were univariably associated with the outcomes (all P < 0.05). After multivariate adjustment, LV maximal apical FD remained a significant independent predictor of outcome [hazard ratio = 1.179 (1.116, 1.246), P < 0.001]. The addition of LV maximal apical FD in the nested models added incremental prognostic value to other common clinical and imaging risk factors (all <0.001; C-statistic: 0.84-0.88, P < 0.001).
Conclusion: LV maximal apical FD was an independent predictor of the adverse clinical outcomes in patients with DCM and provided incremental prognostic value over conventional clinical and imaging risk factors.
{"title":"Prognostic value of left ventricular trabeculae fractal analysis in patients with dilated cardiomyopathy.","authors":"Wei-Hui Xie, Bing-Hua Chen, Dong-Aolei An, Rui Wu, Ruo-Yang Shi, Yan Zhou, Heng-Fei Cui, Lei Zhao, Lian-Ming Wu","doi":"10.1016/j.jocmr.2024.101005","DOIUrl":"10.1016/j.jocmr.2024.101005","url":null,"abstract":"<p><strong>Background: </strong>The prognostic value of left ventricular (LV) myocardial trabecular complexity on cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) remains unknown. This study aimed to evaluate the prognostic value of LV myocardial trabecular complexity using fractal analysis in patients with DCM.</p><p><strong>Methods: </strong>Consecutive patients with DCM who underwent CMR between March 2017 and November 2021 at two hospitals were prospectively enrolled. The primary endpoints were defined as the combination of all-cause death and heart failure hospitalization. The events of cardiac death alone were defined as the secondary endpoints.LV trabeculae complexity was quantified by measuring the fractal dimension (FD) of the endocardial border based on fractal geometry on CMR. Cox proportional hazards regression and Kaplan-Meier survival analysis were used to examine the association between variables and outcomes. The incremental prognostic value of FD was assessed in nested models.</p><p><strong>Results: </strong>A total of 403 patients with DCM (49.31 ± 14.68 years, 69% male) were recruited. After a median follow-up of 43 months (interquartile range, 28-55 months), 87 and 24 patients reached the primary and secondary endpoints, respectively. Age, heart rate, New York Heart Association functional class >II, N-terminal pro-B-type natriuretic peptide, LV ejection fraction, LV end-diastolic volume index, LV end-systolic volume index, LV mass index, presence of late gadolinium enhancement, global FD, LV mean apical FD, and LV maximal apical FD were univariably associated with the outcomes (all P < 0.05). After multivariate adjustment, LV maximal apical FD remained a significant independent predictor of outcome [hazard ratio = 1.179 (1.116, 1.246), P < 0.001]. The addition of LV maximal apical FD in the nested models added incremental prognostic value to other common clinical and imaging risk factors (all <0.001; C-statistic: 0.84-0.88, P < 0.001).</p><p><strong>Conclusion: </strong>LV maximal apical FD was an independent predictor of the adverse clinical outcomes in patients with DCM and provided incremental prognostic value over conventional clinical and imaging risk factors.</p>","PeriodicalId":15221,"journal":{"name":"Journal of Cardiovascular Magnetic Resonance","volume":null,"pages":null},"PeriodicalIF":4.2,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11211225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139671884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}