Comparison between conventional and compressed sensing cine cardiovascular magnetic resonance for left atrial volume and strain assessment

Comparison between conventional and compressed sensing cine cardiovascular magnetic resonance for left atrial volume and strain assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparison between conventional and compressed sensing cine cardiovascular magnetic resonance for left atrial volume and strain assessment Yang Chen, Panpan Xu, Wangyan Liu, Yinsu Zhu, Xiaoyue Zhou, Yi Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1850358/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: To investigate the feasibility of compressed sensing (CS) cine in quantifying left atrial (LA) volumes and strain assessments compared with conventional segmented cine. Methods: Segmented and CS cine sequences were acquired in 31 patients with LV diastolic dysfunction defined by echocardiography (21 males, age 51±15 years) and healthy volunteers (22 males, age 39±13 years) using an 3T MR scanner. LA volumes and strains were evaluated in both sequences. Results: There was excellent correlation for normalized LA volumes (ICCs ≥ 0.982), good correlation for LA EFs (ICCs ≥ 0.779) and moderate correlation for LA strains (ICCs ≥ 0.535) for both cines. Compared with segmented cine technique, LA passive EF from CS cine was not statistically different (segmented: 26.2 (10.7, 32.5) vs. CS: 24.9 (11.7, 35.4), p = 0.838), but radial and longitudinal strain derived by CS cine technique were markedly underestimated (all p < 0.001). The LA passive EF (EFpassive), passive radial and longitudinal strain values (Ere and Ele) from both cine techniques showed good diagnostic performance without significant differences in discriminating between patients and healthy controls (EFpassive, p=0.794; Ere, p=0.513; Ele, p=0.346). Conclusion: Compared with segmented cine, LA EFs obtained from CS cine were clinically comparable and LA strain parameters were underestimated. However, the performance of two cine methods to discriminate between patients with and without LV diastolic dysfunction is similar. Magnetic resonance imaging Cine Compressed sensing Atrial function Left Feasibility study Figures Figure 1 Figure 2 Figure 3 Introduction Left atrial (LA) function comprises reservoir, conduit, and booster pump functions that constitute the filling phase during left ventricular (LV) systole, emptying phase during LV early diastole, and the pumping phase during LV late diastole, respectively [1]. LA size and function corresponding to LV diastolic function are generally associated with various cardiovascular diseases with increased LV filling pressures [2]. Existing evidence has shown that LA enlargement is an important factor in predicting and determining the prognosis and risk stratification of adverse cardiovascular outcomes such as stroke, heart failure, atrial fibrillation, and myocardial infarction [3-5]. LA volume quantification and strain analysis both are important in evaluating LA function. Clinically, LA volume can help accurately evaluate LA function and detect the different stages of LA dilatation despite its complex geometry [6]. LA volume mainly reflects the long-term and chronic effects of increased LV filling pressures [7]. LA strain can identify subtle changes on the left atrium and is more sensitive to cardiovascular diseases in the early stages [7, 8]. However, there are intra- and inter-imaging modality inconsistencies that limit more comprehensive clinical applications of the strain [9]. Conventional segmented cine of cardiovascular magnetic resonance (CMR) is regarded as the gold standard for assessing LA structure and function owing to its high resolution, signal-to-noise ratio, accuracy, and reproducibility [10-12]. However, conventional segmented cine could generate poor image quality if the examinee has poor breath-holding and an irregular heart rate (HR). To overcome these problems, researchers have developed a new accelerated CMR technique, compressed sensing (CS). The CS technique comprises data acquisition and non-linear iterative image reconstruction [13]. Several studies have indicated that CS cine could accurately evaluate LV volume and significantly reduce scan time [14, 15]. Because the structure and movement between LA and LV are considerably different, the feasibility of CS to quantitatively evaluate LA function should be verified. The purpose of this study was to investigate the feasibility of CS cine as a reliable method for assessing LA volume and strain compared with the conventional segmented cine. Materials And Methods Study population From April 2018 to June 2020, 30 healthy controls and 33 patients with LV diastolic dysfunction confirmed by echocardiography were retrospectively enrolled[16]. The inclusion criteria for the healthy controls were as follows: normal blood pressure (systolic blood pressure < 140mm Hg, diastolic blood pressure < 90mm Hg); regular sinus rhythm; normal echocardiography findings; and no history of cardiovascular disease. The 33 patients were composed of ischemic cardiomyopathy (n=3), dilated cardiomyopathy (n=15), hypertrophic cardiomyopathy (n=5), ventricular arrhythmia (n=4), myocarditis (n=4), cardiac amyloidosis (n=1), and constrictive pericarditis (n=1). The exclusion criteria were common contraindications to CMR and poor image quality. All participants underwent a CMR study which included conventional segmented cine (the gold standard) and CS cine sequences. All subjects did not take extra medicine for the CMR examination. The protocol and the use of data were approved by the local ethics committee. Written informed consent for CMR examination was obtained by all the study patients. Image acquisition All CMR scans were performed on a 3T system (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Standard long-axis views (2-, 3- and 4-chamber) and a short-axis stack of cine images were acquired using retrospective electrocardiogram (ECG)-gated balanced steady-state free precession conventional cine imaging and an adaptive prospective ECG-triggered CS sequence, both during inspiratory breath-hold. The total scan time for each cine CMR was obtained. CS sequence used incoherent undersampling on the Cartesian k-space by implementing a pseudorandom variable-density readout pattern. Image reconstruction was performed with a nonlinear iterative reconstruction with k-t regularization [17]. Spatial resolution (1.8*1.6 mm) and slice orientations were the same for the two cine imaging methods. Detailed imaging parameters are shown in Table 1. The “real” number of cardiac phases depends on the patient’s HR during CS cine. To facilitate post-processing of the data, all cine images were set to 25 calculated cardiac phases calculated retrospectively using the “real” obtained cardiac phases. Image analysis All image quality assessments and LA parameters were analyzed independently by two radiologists (2 years and 5 years of CMR experience). When it was difficult to reach consistent image quality, it was evaluated by a third radiologists (10 years of CMR experience). LA volume, ejection fraction (EF), and strain were quantified by using the commercial software package (CVI 4.2 v. 5.0, Circle Cardiovascular Imaging, Calgary, Canada) [12, 18]. Pulmonary veins and LA appendages were excluded during the quantification of LA parameters. The image quality of both cine imaging methods was evaluated according to the following image features: the clarity of the endocardial and epicardial border, the contrast of the myocardium and blood pool, the visualization of papillary muscle and valves, and the degree of artifacts [19]. The qualitative analysis of image quality used a 4-point scale: 1 = poor quality (moderate artifacts with unclear image features), 2 = fair quality (mild artifacts with slight blurring of endocardial borders), 3 = good quality (minimal artifacts but not affecting the views of the remaining features), 4 = excellent quality (clear endocardial and epicardial borders, clear contrast of the myocardium and blood pool, clear papillary muscles and valves, and no artifacts). Images with a score ≥ 2 were considered acceptable for further measurements of LA parameters. The LA endocardial border was manually drawn in 2- and 4-chamber views at three phases of the cardiac cycle: LA minimum volume (LAVmin) at the first phase after mitral valve (MV) closing during LV systole, LA maximum volume (LAVmax) at the last phase before MV opening during LV early diastole, and LA pre-contraction volume (LAVpre) at the last phase before the second diastolic opening of the MV during LV late diastole. For the biplane area-length method, LA volumes were calculated using the following equation: LA volume (LAV) = (0.85 * Area 2ch * Area 4ch ) / Lmin [20], and indexed to body surface area (BSA). Lmin is equivalent to the shorter long-axis length of the LA either in 2-chamber or 4-chamber view [20]. LA EFs included LA total EF (EFtotal), passive ejection fraction (EFpassive), and active ejection fraction (EFbooster), and these were calculated using the following equations: EFtotal = (LAVmax - LAVmin) / LAVmax * 100% EFpassive = (LAVmax - LAVpre) / LAVmax * 100% EFbooster = (LAVpre - LAVmin) / LAVpre * 100% LA endocardial and epicardial borders were manually traced in 2- and 4-chamber views at LV end-systole, automatically propagating the borders over the whole cardiac cycle. Automatic tracking was applied to ensure the accuracy of propagation and manually corrected if necessary. LA global radial and longitudinal strain curves were automatically generated by the software (Supplementary Figure). Global radial strain and global longitudinal strain contained total strain (Ers, Els, respectively), passive strain (Ere, Ele, respectively), and active strain (Era, Ela, respectively), respectively, which correspond to the LA reservoir function, conduit function, and booster function. Statistical analysis Continuous data were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR). The Wilcoxon matched-pairs signed-rank test was used to compare image quality and LA parameters between segmented and CS cines. The correlation of all LA parameters between the two cine images was evaluated using intraclass coefficient (ICC) analysis with two-way random effects, and corresponding 95% confident interval (CI). The Bland-Altman analysis was chosen to evaluate the agreement of LA EFpassive, Ere, and Ele between both cine methods. The Mann-Whitney U test was applied to compare LA parameter differences between the two methods in healthy controls and patients with LV diastolic dysfunction. The diagnostic performance of EFpassive, Ere, and Ele in terms of sensitivity, specificity, negative predictive values, and positive predictive values (NPV and PPV, respectively), and accuracy to distinguish between patients and healthy controls were assessed using receiver-operating-characteristic (ROC) curves, and cut-off values were developed. The diagnostic accuracies of LA EFpassive, Ere, and Ele between the segmented and CS cine images were compared with a Chi-squared test. Inter- (two readers) and intra-observer (two readings) reproducibility were also assessed using ICC, corresponding 95% CI and the coefficient of variation (CoV). ICCs greater than 0.90, 0.75, and 0.50 were considered excellent, good, and moderate, respectively [21]. The CoV was calculated as the SD of the differences between repeated measurements normalized to the mean. A value of p < 0.05 was regarded as statistically significant. Statistical analysis was performed using SPSS (IBM SPSS Statistics 23 for Windows). Results Thirty healthy volunteers (22 male, 8 female) were included in the study. After excluding 2 patients with arrhythmia on account of poor image quality on segmented cine CMR, a total of 31 patients with LV diastolic dysfunction (21 male, 10 female) were included. The patients with LV diastolic dysfunction were significantly older than healthy controls (51 ± 15 vs. 39 ± 13 years, p = 0.001). The mean HRs of patients and volunteers were 70 ± 13 beats/min, and 74 ± 10 beats/min (p=0.185), respectively. Fig. 1 shows cine images with excellent image quality from one patient with segmented and CS cine imaging in 2- and 4-chamber views, respectively, at the LAVmax phase. The image quality was not significantly different between segmented and CS cine images (segmented: 4 (4, 4) vs. CS: 4 (3, 4), p = 0.556). Compared to segmented cine, manually correction of LA contour during strain process was needed more commonly in CS cine images. Left atrial volumetric assessment Between the two cine methods, there were excellent correlations for normalized LA volumes (ICCs: from 0.982 to 0.987) and good correlations for LA EFs (ICCs: from 0.779 to 0.897). A significant overestimation of normalized LAVmin was noted on the CS cine images, resulting in the underestimation of EFtotal and EFbooster. Additionally, normalized LAVpre and normalized LAVmax calculated from the CS cine was underestimated. There was no significant difference between the two cine sequences in terms of LA EFpassive (Table 2). The Bland-Altman analysis for EFpassive between the two sequences is shown in Fig. 2. LA strain evaluation The differences and correlations of LA reservoir, conduit, and booster strains in radial and longitudinal directions between segmented and CS cine images are summarized in Table 2. Global radial and longitudinal strains, measured with CS cine imaging, showed moderate correlations between the two sequences (ICCs: from 0.535 to 0.726). CS cine strain parameters were significantly underestimated compared with those of segmented cine. The Bland-Altman plots for Ere and Ele between both cine methods are shown in Fig. 2. Diagnostic performance of left atrial parameters Compared with healthy controls, patients with LV diastolic dysfunction were associated with decreased LA EFpassive, reduced passive radial and longitudinal strains as verified by both cine images, respectively (Supplementary table shows detailed differences of LA parameters from the two cine methods between controls and patients with LV diastolic dysfunction.) The area under the ROC curve (AUC) values for EFpassive (Segmented: 0.863 vs. CS: 0.876), Ere (Segmented: 0.798 vs. CS: 0.812), and Ele (Segmented: 0.901 vs. CS: 0.838) to discriminate between healthy controls and patients with diastolic dysfunction were similar between the two cine sequences (Fig. 3). However, the cut-off values of EFpassive, Ere, and Ele derived from CS cine were considerably lower than those calculated by segmented cine images (EFpassive, 18.2 vs. 21.3 %; Ere, -4.9 vs. -5.3 %; Ele, 8.2 vs. 16.6 %). Table 3 shows detailed data about the AUC values, thresholds, sensitivity, specificity, accuracy, NPV, and PPV of EFpassive, Ere, and Ele from both cine images. Moreover, the diagnostic accuracies for EFpassive, Ere, and Ele derived from segmented cine were slightly higher than those derived from CS cine, but no statistical differences were found in these parameters between the two cine methods (EFpassive, p = 0.794; Ere, p = 0.513; Ele, p = 0.346). Intra-observer and inter-observer reproducibility Table 4 shows the variability analysis of LA volumetric and strain parameters between the segmented and CS cine sequences. The ICCs of all LA parameters derived from the CS sequence resembled those from the segmented sequence. Almost all LA parameters had excellent intra-observer and inter-observer agreements between both cine imaging methods. Discussion In this prospective cohort study on 30 healthy controls and 31 patients with LV diastolic dysfunction, we found excellent correlation for normalized LA volumes, good correlations for LA EFs and moderate correlation for LA strain parameters between CS cine and segmented cine during CMR. Although CS cine underestimated the LA strain, segmented and CS cines showed similarly good diagnostic performance with parameters of LA EFpassive, passive radial and longitudinal strains to discriminate between patients with LV diastolic dysfunction and healthy controls. Our results showed that CS cine-derived EFpassive had a good correlation and accurate values compared with the segmented cine, indicating that the LA EFpassive values were interchangeable between the segmented and CS cine sequences. We found that our LAVs and EFtotal in healthy controls were consistent with those reported by Truong et al [15]. However, EFpassive was lower than EFbooster in our study, which contradicts the data of previous studies [18, 22]. Meanwhile, Li W et al. provided the normal LAVs and EF reference values for a Chinese population, and these findings are consistent with our results [23]. This difference between EFpassive and EFbooster may be owing to different patient populations [12]. In our study, LAVmin/BSA derived from CS cine was statistically higher by 2.4 ml/m 2 compared with segment cine and this overestimation may be partially due to that it was difficult to detect mitral valve on CS cine images owing to reduced ability of tiny structure visualization [24]. The LA myocardial longitudinal strain values, from the segmented cine images in our study, were consistent with the normal reference values in a recent study that reported LA longitudinal strains derived from CMR feature tracking (FT) in 112 volunteers (total longitudinal strain: 39.13±9.27%; passive longitudinal strain: 25.15±8.34 %; active longitudinal strain: 13.99±4.11%, respectively) [18]. However, total and active longitudinal strain in our study were higher than the 29.1% and 7.8% reported by Kowallick et al, which could indicate that LA strain is significantly correlated with volumetric indices [25]. Reservoir and conduit functions decreased in patients with hypertrophic cardiomyopathy or heart failure with preserved EF as compared with healthy volunteers, whereas LA booster pump function could be preserved or impaired [13, 26-28]. Preserved or impaired LA booster pump function depends on the different stages of diseases in select populations, in which impaired LA contractility frequently occurred in patients with severe diastolic dysfunction. In our study, patients showed significantly impaired LA reservoir, conduit, and booster pump functions compared to those of healthy controls detected by both cine methods, indicating the presence of decompensated diastolic dysfunction with complete LA performance impairment. Furthermore, LA EFpassive, passive radial and longitudinal strains obtained from segmented cine and CS cine images, which reflect LA conduit function, were able to accurately distinguish between patients with and without diastolic dysfunction, although the cut-off values of these parameters from two cine techniques were different. FT-CMR provides a robust approach to evaluate LA strain directly from conventional cine images with excellent inter-operator and scan-rescan reproducibility [29]. Experience or dedicated training of CMR FT can improve the reliability and accuracy of the examination [30]. Consistent with previous findings [13, 25], our study found that LA volumetric and strain derived from segmented and CS cines had excellent intra- and inter-observer agreement, which is reliable for repeated examination and follow-up observation. Although our observers were well trained and experienced, LA strain values obtained by CS cine images were significantly underestimated in comparison with those of segmented cine. Lower temporal resolution was an important factor for underestimation of myocardial strain, but the temporal resolutions of 39ms and 46ms had no different influence on the value of strain [31].One possible explanation for this underestimation of CS strain is that the CS cine images with pseudo-random undersampling and iterative reconstruction present reduced signal-to-noise ratio and reduced ability to demonstrate the fine structure; therefore, LA blood pool and pericardial fat might have been included in the LA strain assessment [32]. For this reason, LA strain analysis should be conducted at the same cine sequence to follow-up in the future. Luckily, we found that the diagnostic accuracy of CS cine images to distinguish between patients with and without LV diastolic dysfunction was almost the same as that of conventional segmented cine images, which suggests that CS cine method is feasible for clinical practice for LA strain analysis. Limitations First, we enrolled patients with a wide range of cardiovascular diseases, and approximately half of these patients had an advanced stage of LA dysfunction, which may have had a positive effect on the accuracy of CS cine images to distinguish between patients and healthy controls. Second, despite Simpson’s method is considered the gold standard to calculate LA volume, we used the biplane area-length method owing to faster acquisition and post-processing time [33]. Thirdly, we did not assess the regional myocardial strain abnormalities, which is more sensitive than global strain to detect LA functional abnormalities [13]. But the ability of the CS cine images to evaluate LA regional myocardial strain was limited because of the smaller anatomic details detected [24]. In addition, we did not list the LA scan time on CS cine, including 2- and 4-chamber views, which was not markedly shorter than that of segmented cine, although the total acquisition time of CS cine imaging was obviously reduced, because our clinical patients needed comprehensively evaluate LA and LV functions in short-axis and long-axis views. Conclusion Compared with segmented cine, CS cine is feasible for LA volumetric assessment but LA strain values derived by the two cine sequences should not be interchanged owing to a remarkable underestimation from CS cine imaging. The ability of LA passive EF, passive radial and longitudinal strain derived from CS cine images to accurately discriminate between patients with and without LV diastolic dysfunction is comparable to that of segmented cine images. Declarations Funding This research was partly supported by the National Natural Science Foundation under grants (81701651 and 81601464). Acknowledgements The authors of this manuscript declare no impact on results from funding supporters. Competing interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions Yinsu Zhu, Yi Xu and Xiaomei Zhu contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yang Chen, Panpan Xu, Jun Wang, Wen Qian and Wangyan Liu. The first draft of the manuscript was written by Yang Chen and Panpan Xu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Nanjing Medical University. 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J Cardiovasc Magn Reson. 2021;23:60. http://doi.org/10.1186/s12968-021-00740-5 Feng L, Benkert T, Block KT, Sodickson DK, Otazo R, H C. Compressed sensing for body MRI. J Magn Reson Imaging. 2017;45:966-987. http://doi.org/10.1002/jmri.25547 Nacif MS, Barranhas AD, Turkbey E, et al. Left atrial volume quantification using cardiac MRI in atrial fibrillation: comparison of the Simpson's method with biplane area-length, ellipse, and three-dimensional methods. Diagn Interv Radiol. 2013;19:213-220. http://doi.org/10.5152/dir.2012.002 Tables Table 1. Imaging parameters of the segmented cine and compressed sensing (CS) cine sequences Segmented cine CS cine Echo time, ms 1.4 1.2 Echo spacing time, ms 3.2 2.9 Field of view, mm 380*360 380*360 Image matrix 208*176 208*176 Spatial resolution, mm 1.8*1.6 1.8*1.6 Temporal resolution, ms 39 46 Slice thickness, mm 8 8 Flip angle 45 35 Bandwidth, Hz/pixel 962 962 K-lines/segment 12 16 Cardiac phases 25 Calculated 25 Total scan time, s (depend on heart rate) 120-150 20-30 Table 2. Comparison of LA volumetric and strain parameters measured with conventional segmented cine and CS cine images Segmented cine CS cine Difference P value ICC (95%CI) Normalized Volumetric parameters LAVmin/BSA (ml/m 2 ) 25.4(16.3, 49.5) 27.5(18.4, 52.4) 2.4 0.001 0.987 (0.979-0.992) LAVmax/BSA (ml/m 2 ) 49.9(38.8, 67.2) 47.6(35.7, 64.7) -1.9 0.009 0.982 (0.970-0.989) LAVpre/BSA (ml/m 2 ) 35.2(25.6, 57.4) 33.7(23.2, 56.2) -1.5 0.049 0.987 (0.978-0.992) EFtotal (%) 48.4(21.7, 56.4) 42.2(19.1, 49.9) -5.2 <0.001 0.897 (0.834-0.937) EFpassive (%) 26.2(10.7, 32.5) 24.9(11.7, 35.4) -0.8 0.838 0.859 (0.776-0.913) EFbooster (%) 25.7(11.5, 37.8) 15.2(5.5, 26.4) -6.1 <0.001 0.779 (0.656-0.861) Strain parameters Ers (%) -13.0(-17.7, -7.7) -8.1(-11.4, -5.2) -4.1 <0.001 0.712 (0.562-0.816) Ere (%) -7.3(-9.0, -4.2) -5.6(-7.7, -4.1) -1.2 <0.001 0.726 (0.582-0.826) Era (%) -6.6(-9.1, -3.3) -2.5(-4.2, -0.9) -3.4 <0.001 0.612 (0.427-0.748) Els (%) 24.7(11.6, 36.3) 11.9(6.4, 16.9) -9.9 <0.001 0.582 (0.388-0.726) Ele (%) 14.2(6.6, 22.7) 8.4(5.5, 13.0) -4.1 <0.001 0.561 (0.362-0.711) Ela (%) 9.0(4.3, 13.2) 3.2(1.1, 5.4) -4.9 <0.001 0.535 (0.329-0.692) The data are presented as the median (first quartile, third quartile). CS, compressed sensing; LA, left atrial; LAVmin, left minimum atrial volume; LAVmax, left maximum atrial volume; LAVpre, left atrial pre-contraction volume; BSA, body surface area; EFtotal, total ejection fraction; EFpassive, passive ejection fraction; EFbooster, active ejection fraction; Ers, global total radial strain; Ere, global passive radial strain; Era, global active radial strain; Els, global total longitudinal strain; Ele, global passive longitudinal strain; Ela, global active longitudinal strain; ICC intraclass correlation coefficient. Table 3. Diagnostic performance of LA EFpassive, Ere, and Ele to differentiate between patients with and without diastolic dysfunction using segmented and CS cine methods. AUC Cut-off value Sensitivity Specificity NPV PPV Accuracy EFpassive (%) Segmented cine 0.863 21.3 77.4% 96.7% 80.6% 96.0% 86.9% CS cine 0.876 18.2 74.2% 96.7% 78.4% 95.8% 85.2% Ere (%) Segmented cine 0.798 -5.3 64.5% 96.7% 72.5% 95.2% 80.3% CS cine 0.812 -4.9 64.5% 86.7% 70.3% 83.3% 75.5% Ele (%) Segmented cine 0.901 16.6 90.3% 80.0% 88.9% 82.4% 85.2% CS cine 0.838 8.2 74.2% 83.3% 75.8% 82.1% 78.7% AUC, area under the curve; CS, compressed sensing; LA, left atrial; EFpassive, passive ejection fraction; Ere, global passive radial strain; Ele, global passive longitudinal strain; NPV, negative predictive values; PPV, positive predictive values. Table 4. Intra-observer and inter-observer reproducibility for LA parameters derived from segmented and CS cine sequences Intra-observer Reproducibility Inter-observer Reproducibility Segmented cine CS cine Segmented cine CS cine ICC (95%CI) CoV ICC (95%CI) CoV ICC (95%CI) CoV ICC (95%CI) CoV LAVmin/BSA (ml/m 2 ) 0.979(0.947-0.992) 4.2 0.997(0.991- 0.999) 5.4 0.972(0.930-0.989) 24.7 0.995(0.987-0.998) 27.8 LAVmax/BSA (ml/m 2 ) 0.996(0.991-0.999) 4.3 0.970(0.926-0.988) 21.4 0.997(0.993-0.999) 15.1 0.975(0.937--0.990) 2.0 LAVpre/BSA (ml/m 2 ) 0.991(0.978-0.997) 5.0 0.997(0.991-0.999) 3.7 0.985(0.962-0.994) 21.6 0.993(0.984-0.997) 2.5 EFtotal (%) 0.925(0.822-0.970) 4.7 0.948(0.874-0.979) 7.6 0.887(0.737-0.954) 23.6 0.950(0.878-0.980) 2.6 EFpassive (%) 0.966(0.916-0.986) 5.1 0.943(0.863--0.977) 4.8 0.903(0.771-0.960) 46.9 0.948(0.873-0.979) 1.7 EFbooster (%) 0.976(0.941- 0.991) 9.0 0.941(0.857-0.976) 5.9 0.954(0.888-0.982) 23.8 0.933(0.838-0.973) 25.1 Ers (%) 0.960(0.902-0.984) 8.9 0.968(0.922-0.987) 12.7 0.942(0.860-0.977) 9.6 0.984(0.959-0.993) 4.7 Ere (%) 0.924(0.818-0.969) 12.7 0.942(0.860-0.977) 3.3 0.933(0.839-0.973) 6.6 0.950(0.877-0.980) 2.5 Era (%) 0.973(0.932-0.989) 3.4 0.984(0.959- 0.993) 2.4 0.963(0.910-0.985) 53.5 0.988(0.969-0.995) 2.1 Els (%) 0.970(0.925-0.988) 5.6 0.959(0.900-0.984) 18.0 0.965(0.0.914-0.986) 5.8 0.967(0.920-0.987) 3.9 Ele (%) 0.951(0.880-0.980) 19.4 0.936(0.845-0.974) 4.7 0.947(0.871-0.979) 8.6 0.935(0.843-0.974) 2.8 Ela (%) 0.972(0.931-0.989) 3.1 0.988(0.970-0.995) 1.7 0.963(0.910-0.985) 6.7 0.987(0.967-0.995) 2.4 CS, compressed sensing; LA, left atrial; LAVmin, left minimum atrial volume; LAVmax, left maximum atrial volume; LAVpre, left atrial pre-contraction volume; BSA, body surface area; EFtotal, total ejection fraction; EFpassive, passive ejection fraction; EFbooster, active ejection fraction; Ers, global total radial strain; Ere, global passive radial strain; Era, global active radial strain; Els, global total longitudinal strain; Ele, global passive longitudinal strain; Ela, global active longitudinal strain. Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1850358","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":121147237,"identity":"35fb1afb-e22b-409d-8e29-fd8898b0d13f","order_by":0,"name":"Yang Chen","email":"","orcid":"","institution":"Wuxi people’s hospital, Nanjing medical university","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Chen","suffix":""},{"id":121147238,"identity":"d6ffb650-c2bf-4bf9-a8c1-34564fc631c6","order_by":1,"name":"Panpan Xu","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Panpan","middleName":"","lastName":"Xu","suffix":""},{"id":121147240,"identity":"77421250-8669-4818-8dc4-0fc67eb1deaf","order_by":2,"name":"Wangyan Liu","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wangyan","middleName":"","lastName":"Liu","suffix":""},{"id":121147244,"identity":"98ff5968-eeb5-45c6-8d1c-ddf9fca98199","order_by":3,"name":"Yinsu Zhu","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yinsu","middleName":"","lastName":"Zhu","suffix":""},{"id":121147245,"identity":"e29f16e1-d576-4bd3-97d6-353b256883ac","order_by":4,"name":"Xiaoyue Zhou","email":"","orcid":"","institution":"MR Collaboration, Siemens Healthineers Ltd","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyue","middleName":"","lastName":"Zhou","suffix":""},{"id":121147246,"identity":"662acc22-95f7-4892-90ce-2981c1e21f41","order_by":5,"name":"Yi Xu","email":"","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Xu","suffix":""},{"id":121147247,"identity":"1a559b34-f49b-4547-8b2c-06e66e52f7d1","order_by":6,"name":"Xiaomei Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDACZjApwWDAwHwASIFAAtFa2BKI1AIDBgw8BlAmAS0Gx5kfPrpRYWFvLpHz7YFlzmEGfvYcA4afO3BrkWxmMzbOOSPBbDkjd7uB5LbDDJI9bwwYe8/g1sLPzGAmndsmwWZwI3ebBEiLwY0cA2bGNtxa2JjZv0nn/pPgAap8BtZiT0gLPzMP0JYGCQmgFjaILRIEtEg28xQb5xyTMDA488wMqCWdR+LMs4KDvXi0GJw/vvFxTk2dvcHx5GfSktus5fjbkzc++IlHCwpgBkYlD4hxgEgNDAyMH4hWOgpGwSgYBSMJAACah0a6GunNuAAAAABJRU5ErkJggg==","orcid":"","institution":"the First Affiliated Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaomei","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2022-07-12 11:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1850358/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1850358/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24202945,"identity":"5bdf3c28-8afc-4c85-be62-d9321b79bae8","added_by":"auto","created_at":"2022-07-22 16:27:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366487,"visible":true,"origin":"","legend":"\u003cp\u003eImages of an 83-year-old man with cardiac amyloidosis that were acquired using compressed sensing cine and segmented cine imaging methods. Two- (a) and 4-chamber views (b) at the left atrial maximum phase derived from segmented cine images (c) and compressed sensing cine images (d). Although reduced contrast to noise ratio in compressed sensing images was demonstrated, all images were with clear endocardial and epicardial borders, clear contrast of the myocardium and blood pool, clear papillary muscles and valves, and no artifacts. Both observers rated the image quality as excellent (i.e., score of 4) for all images.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1850358/v1/faa85502978373855be79b23.png"},{"id":24202946,"identity":"bddbffea-5111-485d-879d-8eb02a89ee6b","added_by":"auto","created_at":"2022-07-22 16:27:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":320540,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plots between segmented and compressed sensing cine images for left atrial passive ejection fractions (a), global passive radial strain (b), and global passive longitudinal strain (c).The horizontal red solid line depicts the mean difference; the two red dotted lines depict the upper and lower limits of agreement (+1.96 standard deviation and -1.96 standard deviation, respectively). EFpassive, passive ejection fraction; Ere, global passive radial strain; Ele, global passive longitudinal strain. \u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1850358/v1/3cc0f0122a775cc681e7933c.png"},{"id":24202948,"identity":"f170ce8f-79f9-4974-ab07-4975fb7836ae","added_by":"auto","created_at":"2022-07-22 16:27:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":363771,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver-operating-characteristic curves of the left atrial passive ejection fraction (a), global passive radial strain (b), and global passive longitudinal strain (c) derived from segmented and compressed sensing cine images to discriminate between patients with left ventricular diastolic dysfunction and healthy controls. CS, compressed sensing; EFpassive, passive ejection fraction; Ere, global passive radial strain; Ele, global passive longitudinal strain.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1850358/v1/6e73f7ce61493ed3050790a9.png"},{"id":33764728,"identity":"31a33ad0-ff63-466a-a088-dd3ce25ce6f6","added_by":"auto","created_at":"2023-03-03 19:59:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1251135,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1850358/v1/c27fba52-cf75-42e6-bfd7-b409f34e29f7.pdf"},{"id":24202947,"identity":"92e9d96c-bff3-4862-853d-d2d483a4b8e3","added_by":"auto","created_at":"2022-07-22 16:27:26","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":148044,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-1850358/v1/84bd82f8085718ca8961e00f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison between conventional and compressed sensing cine cardiovascular magnetic resonance for left atrial volume and strain assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeft atrial (LA)\u0026nbsp;function comprises reservoir, conduit, and booster pump functions that constitute the filling phase during left ventricular (LV) systole, emptying phase during LV early diastole, and the pumping phase during LV late diastole, respectively\u0026nbsp;[1]. LA size and function corresponding to LV diastolic function are generally associated with various cardiovascular diseases with increased LV filling pressures\u0026nbsp;[2]. Existing evidence has shown that LA enlargement is an important factor in predicting and determining the prognosis and risk stratification of adverse cardiovascular outcomes such as stroke, heart failure, atrial fibrillation, and myocardial infarction\u0026nbsp;[3-5].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLA volume quantification and strain analysis both are important in evaluating LA function. Clinically, LA volume can help accurately evaluate LA function and detect the different stages of LA dilatation despite its complex geometry\u0026nbsp;[6]. LA volume mainly reflects the long-term and chronic effects of increased LV filling pressures\u0026nbsp;[7]. LA strain can identify subtle changes on the left atrium and is more sensitive to cardiovascular diseases in the early stages\u0026nbsp;[7, 8]. However, there are intra- and inter-imaging modality inconsistencies that limit more comprehensive clinical applications of the strain\u0026nbsp;[9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConventional segmented cine of cardiovascular magnetic resonance (CMR) is regarded as the gold standard for assessing LA structure and function owing to its high resolution, signal-to-noise ratio, accuracy, and reproducibility [10-12]. However, conventional segmented cine could generate poor image quality if the examinee has poor breath-holding and an irregular heart rate (HR). To overcome these problems, researchers have developed a new accelerated CMR technique, compressed sensing (CS). The CS technique comprises data acquisition and non-linear iterative image reconstruction [13]. Several studies have indicated that CS cine could accurately evaluate LV volume and significantly reduce scan time [14, 15]. Because the structure and movement between LA and LV are considerably different, the feasibility of CS to quantitatively evaluate LA function should be verified. The purpose of this study was to investigate the feasibility of CS cine as a reliable method for assessing LA volume and strain compared with the conventional segmented cine.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFrom April 2018 to June 2020, 30 healthy controls and 33 patients with LV diastolic dysfunction confirmed by echocardiography were retrospectively enrolled[16]. The\u0026nbsp;inclusion criteria for the healthy controls were as follows: normal blood pressure (systolic blood pressure \u0026lt; 140mm Hg, diastolic blood pressure \u0026lt; 90mm Hg); regular sinus rhythm; normal echocardiography findings; and no history of cardiovascular disease. The 33 patients were composed of ischemic cardiomyopathy (n=3), dilated cardiomyopathy (n=15), hypertrophic cardiomyopathy (n=5), ventricular arrhythmia (n=4), myocarditis (n=4), cardiac amyloidosis (n=1), and constrictive pericarditis (n=1). The exclusion criteria were common contraindications to CMR and poor image quality. All participants underwent a CMR study which included conventional segmented cine (the gold standard) and CS cine sequences.\u0026nbsp;All subjects did not take extra medicine for the CMR examination. The protocol and the use of data were approved by the local ethics committee. Written informed consent for CMR examination was obtained by all the study patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImage acquisition\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll CMR scans were performed on a 3T system (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany). Standard long-axis views (2-, 3- and 4-chamber) and a short-axis stack of cine images were acquired using retrospective electrocardiogram (ECG)-gated balanced steady-state free precession conventional cine imaging and an adaptive prospective ECG-triggered CS sequence, both during inspiratory breath-hold. The total scan time for each cine CMR was obtained. CS sequence used incoherent undersampling on the Cartesian k-space by implementing a pseudorandom variable-density readout pattern. Image reconstruction\u0026nbsp;was performed with a nonlinear iterative reconstruction with k-t regularization\u0026nbsp;[17]. Spatial resolution\u0026nbsp;(1.8*1.6 mm) and slice orientations were the same for the two cine imaging methods.\u0026nbsp;Detailed imaging parameters are shown in Table 1.\u0026nbsp;The \u0026ldquo;real\u0026rdquo; number of cardiac phases depends on the patient\u0026rsquo;s HR during CS cine. To facilitate post-processing of the data, all cine images were set to 25 calculated cardiac phases calculated retrospectively using the \u0026ldquo;real\u0026rdquo; obtained cardiac phases.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eImage analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll image quality assessments and LA parameters were analyzed independently by two radiologists (2 years and 5 years of CMR experience). When it was difficult to reach consistent image quality, it was evaluated by a third radiologists (10 years of CMR experience). LA volume, ejection fraction (EF), and strain were quantified by using the commercial software package\u0026nbsp;(CVI 4.2 v. 5.0, Circle Cardiovascular Imaging, Calgary, Canada)\u0026nbsp;[12, 18]. Pulmonary veins and LA appendages were excluded during the quantification of LA parameters.\u003c/p\u003e\n\u003cp\u003eThe image quality of both cine imaging methods was evaluated according to the following image features: the clarity of the endocardial and epicardial border, the contrast of the myocardium and blood pool, the visualization of papillary muscle and valves, and the degree of artifacts\u0026nbsp;[19]. The qualitative analysis of image quality used a 4-point scale: 1 = poor quality (moderate artifacts with unclear image features), 2 = fair quality (mild artifacts with slight blurring of endocardial borders), 3 = good quality (minimal artifacts but not affecting the views of the remaining features), 4 = excellent quality (clear endocardial and epicardial borders, clear contrast of the myocardium and blood pool, clear papillary muscles and valves, and no artifacts). Images with a score \u0026ge; 2 were considered acceptable for further measurements of LA parameters.\u003c/p\u003e\n\u003cp\u003eThe LA endocardial border was manually drawn in 2- and 4-chamber views at three phases of the cardiac cycle: LA minimum volume (LAVmin) at the first phase after mitral valve (MV) closing during LV systole, LA maximum volume (LAVmax) at the last phase before MV opening during LV early diastole, and LA pre-contraction volume (LAVpre) at the last phase before the second diastolic opening of the MV during LV late diastole.\u0026nbsp;For the biplane area-length method, LA volumes were calculated using the following equation: LA volume (LAV) = (0.85 * Area\u003csub\u003e2ch\u003c/sub\u003e * Area\u003csub\u003e4ch\u003c/sub\u003e) / Lmin\u0026nbsp;[20], and indexed to body surface area (BSA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLmin is equivalent to the shorter long-axis length of the LA either in 2-chamber or 4-chamber view\u0026nbsp;[20]. LA EFs included LA total EF (EFtotal), passive ejection fraction (EFpassive), and active ejection fraction (EFbooster), and these were calculated using the following equations:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEFtotal = (LAVmax - LAVmin) / LAVmax * 100%\u003c/p\u003e\n\u003cp\u003eEFpassive = (LAVmax - LAVpre) / LAVmax * 100%\u003c/p\u003e\n\u003cp\u003eEFbooster = (LAVpre - LAVmin) / LAVpre * 100%\u003c/p\u003e\n\u003cp\u003eLA endocardial and epicardial borders were manually traced in 2- and 4-chamber views at LV end-systole, automatically propagating the borders over the whole cardiac cycle.\u0026nbsp;Automatic tracking was applied to ensure the accuracy of propagation and manually corrected if necessary. LA global radial and longitudinal strain curves were automatically generated by the software (Supplementary Figure). Global radial strain and global longitudinal strain contained total strain (Ers, Els, respectively), passive strain (Ere, Ele, respectively), and active strain (Era, Ela, respectively), respectively, which correspond to the LA reservoir function, conduit function, and booster function.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eContinuous data were expressed as mean \u0026plusmn; standard deviation (SD) or median and interquartile range (IQR). The Wilcoxon matched-pairs signed-rank test was used to compare image quality and LA parameters between segmented and CS cines. The correlation of all LA parameters between the two cine images was evaluated using intraclass coefficient (ICC) analysis with two-way random effects, and corresponding 95% confident interval (CI). The Bland-Altman analysis was chosen to evaluate the agreement of LA EFpassive, Ere, and Ele between both cine methods. The Mann-Whitney U test was applied to compare LA parameter differences between the two methods in healthy controls and patients with LV diastolic dysfunction. The diagnostic performance of EFpassive, Ere, and Ele in terms of sensitivity, specificity, negative predictive values, and positive predictive values (NPV and PPV, respectively), and accuracy to distinguish between patients and healthy controls were assessed using receiver-operating-characteristic (ROC) curves, and cut-off values were developed. The diagnostic accuracies of LA EFpassive, Ere, and Ele between the segmented and CS cine images were compared with a Chi-squared test. Inter- (two readers) and intra-observer (two readings) reproducibility were also assessed using ICC, corresponding 95% CI and the coefficient of variation (CoV). ICCs greater than 0.90, 0.75, and 0.50 were considered excellent, good, and moderate, respectively [21]. The CoV was calculated as the SD of the differences between repeated measurements normalized to the mean. A value of p \u0026lt; 0.05 was regarded as statistically significant. Statistical analysis was performed using SPSS (IBM SPSS Statistics 23 for Windows).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThirty healthy volunteers (22 male, 8 female) were included in the study. After excluding 2 patients with arrhythmia on account of poor image quality on segmented cine CMR, a total of 31 patients with LV diastolic dysfunction (21 male, 10 female) were included. The patients with LV diastolic dysfunction were significantly older than healthy controls (51 \u0026plusmn; 15 vs. 39 \u0026plusmn; 13 years, p = 0.001). The mean HRs of patients and volunteers were 70 \u0026plusmn; 13 beats/min, and 74 \u0026plusmn; 10 beats/min (p=0.185), respectively. Fig. 1 shows cine images with excellent image quality from one patient with segmented and CS cine imaging in 2- and 4-chamber views, respectively, at the LAVmax phase. The image quality was not significantly different between segmented and CS cine images (segmented: 4 (4, 4) vs. CS: 4 (3, 4), p = 0.556). Compared to segmented cine, manually correction of LA contour during strain process was needed more commonly in CS cine images.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLeft atrial volumetric assessment\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBetween the two cine methods, there were excellent correlations for normalized LA volumes (ICCs: from 0.982 to 0.987) and good correlations for LA EFs (ICCs: from 0.779 to 0.897). A significant overestimation of normalized LAVmin was noted on the CS cine images, resulting in the underestimation of EFtotal and EFbooster. Additionally, normalized LAVpre and normalized LAVmax calculated from the CS cine was underestimated. There was no significant difference between the two cine sequences in terms of LA EFpassive (Table 2). The Bland-Altman analysis for EFpassive between the two sequences is shown in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLA strain evaluation\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe differences and correlations of LA reservoir, conduit, and booster strains in radial and longitudinal directions between segmented and CS cine images are summarized in Table 2. Global radial and longitudinal strains, measured with CS cine imaging, showed moderate correlations between the two sequences (ICCs: from 0.535 to 0.726). CS cine strain parameters were significantly underestimated compared with those of segmented cine. The Bland-Altman plots for Ere and Ele between both cine methods are shown in Fig. 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eDiagnostic performance of left atrial parameters\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCompared with healthy controls, patients with LV diastolic dysfunction were associated with decreased LA EFpassive, reduced passive radial and longitudinal strains as verified by both cine images, respectively (Supplementary table shows detailed differences of LA parameters from the two cine methods between controls and patients with LV diastolic dysfunction.) The area under the ROC curve (AUC) values for EFpassive (Segmented: 0.863 vs. CS: 0.876), Ere (Segmented: 0.798 vs. CS: 0.812), and Ele (Segmented: 0.901 vs. CS: 0.838) to discriminate between healthy controls and patients with diastolic dysfunction were similar between the two cine sequences (Fig. 3). However, the cut-off values of EFpassive, Ere, and Ele derived from CS cine were considerably lower than those calculated by segmented cine images (EFpassive, 18.2 vs. 21.3 %; Ere, -4.9 vs. -5.3 %; Ele, 8.2 vs. 16.6 %). Table 3 shows detailed data about the AUC values, thresholds, sensitivity, specificity, accuracy, NPV, and PPV of EFpassive, Ere, and Ele from both cine images. Moreover, the diagnostic accuracies for EFpassive, Ere, and Ele derived from segmented cine were slightly higher than those derived from CS cine, but no statistical differences were found in these parameters between the two cine methods (EFpassive, p = 0.794; Ere, p = 0.513; Ele, p = 0.346).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIntra-observer and inter-observer reproducibility\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 shows the variability analysis of LA volumetric and strain parameters between the segmented and CS cine sequences. The ICCs of all LA parameters derived from the CS sequence resembled those from the segmented sequence. Almost all LA parameters had excellent intra-observer and inter-observer agreements between both cine imaging methods.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this prospective cohort study on 30 healthy controls and 31 patients with LV diastolic dysfunction, we found excellent correlation for normalized LA volumes, good correlations for LA EFs and moderate correlation for LA strain parameters between CS cine and segmented cine during CMR. Although CS cine underestimated the LA strain, segmented and CS cines showed similarly good diagnostic performance with parameters of LA EFpassive, passive radial and longitudinal strains to discriminate between patients with LV diastolic dysfunction and healthy controls.\u003c/p\u003e\n\u003cp\u003eOur results showed that CS cine-derived EFpassive had a good correlation and accurate values compared with the segmented cine, indicating that the LA EFpassive values were interchangeable between the segmented and CS\u0026nbsp;cine sequences. We found that our LAVs and EFtotal in healthy controls were consistent with those reported by Truong et al\u0026nbsp;[15]. However, EFpassive was lower than EFbooster in our study, which contradicts the data of previous studies\u0026nbsp;[18, 22]. Meanwhile, Li W et al. provided the normal LAVs and EF reference values for a Chinese population, and these findings are consistent with our results\u0026nbsp;[23]. This difference between EFpassive and EFbooster may be owing to different patient populations\u0026nbsp;[12]. In our study, LAVmin/BSA derived from CS cine was statistically higher by 2.4 ml/m\u003csup\u003e2\u003c/sup\u003e compared with segment cine and this overestimation may be partially due to that it was difficult to detect mitral valve on CS cine images owing to reduced ability of tiny structure visualization\u0026nbsp;[24].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe LA myocardial longitudinal strain values, from the segmented cine images in our study, were consistent with the normal reference values in a recent study that reported LA longitudinal strains derived from CMR feature tracking (FT) in 112 volunteers (total longitudinal strain: 39.13\u0026plusmn;9.27%; passive longitudinal strain: 25.15\u0026plusmn;8.34 %; active longitudinal strain: 13.99\u0026plusmn;4.11%, respectively)\u0026nbsp;[18]. However, total and active longitudinal strain in our study were higher than the 29.1% and 7.8% reported by Kowallick et al, which could indicate that LA strain is significantly correlated with volumetric indices\u0026nbsp;[25].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReservoir and conduit functions decreased\u0026nbsp;in patients with hypertrophic cardiomyopathy or heart failure with preserved EF as compared with healthy volunteers, whereas LA booster pump function could be preserved or impaired\u0026nbsp;[13, 26-28]. Preserved or impaired LA booster pump function depends on the different stages of diseases in select populations, in which impaired LA contractility frequently occurred in patients with severe diastolic dysfunction. In our study, patients showed significantly impaired LA reservoir, conduit, and booster pump functions compared to those of healthy controls detected by both cine methods, indicating the presence of decompensated diastolic dysfunction with complete LA performance impairment. Furthermore, LA EFpassive, passive radial and longitudinal strains obtained from segmented cine and CS cine images, which reflect LA conduit function, were able to accurately distinguish between patients with and without diastolic dysfunction, although the cut-off values of these parameters from two cine techniques were different.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFT-CMR provides a robust approach to evaluate LA strain directly from conventional cine images with excellent inter-operator and scan-rescan reproducibility\u0026nbsp;[29]. Experience or dedicated training of CMR FT can improve the reliability and accuracy of the examination\u0026nbsp;[30]. Consistent with previous findings\u0026nbsp;[13, 25], our study found that LA volumetric and strain derived from segmented and CS cines had excellent intra- and inter-observer agreement, which is reliable for repeated examination and follow-up observation. Although our observers were well trained and experienced, LA strain values obtained by CS cine images were significantly underestimated in comparison with those of segmented cine. Lower temporal resolution was an important factor for underestimation of myocardial strain, but the temporal resolutions of 39ms and 46ms had no different influence on the value of strain\u0026nbsp;[31].One possible explanation for this underestimation of CS strain is that the CS cine images with pseudo-random undersampling and iterative reconstruction present reduced signal-to-noise ratio and reduced ability to demonstrate the fine structure; therefore, LA blood pool and pericardial fat might have been included in the LA strain assessment\u0026nbsp;[32]. For this reason, LA strain analysis should be conducted at the same cine sequence to follow-up in the future.\u0026nbsp;Luckily, we found that the diagnostic accuracy of CS cine images to distinguish between patients with and without LV diastolic dysfunction was almost the same as that of conventional segmented cine images, which suggests that CS cine method is feasible for clinical practice for LA strain analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFirst, we enrolled patients with a wide range of cardiovascular diseases, and approximately half of these patients had an advanced stage of LA dysfunction, which may have had a positive effect on the accuracy of CS cine images to distinguish between patients and healthy controls. Second, despite Simpson\u0026rsquo;s method is considered the gold standard to calculate LA volume, we used the biplane area-length method owing to faster acquisition and post-processing time [33]. Thirdly, we did not assess the regional myocardial strain abnormalities, which is more sensitive than global strain to detect LA functional abnormalities [13]. But the ability of the CS cine images to evaluate LA regional myocardial strain was limited because of the smaller anatomic details detected [24]. In addition, we did not list the LA scan time on CS cine, including 2- and 4-chamber views, which was not markedly shorter than that of segmented cine, although the total acquisition time of CS cine imaging was obviously reduced, because our clinical patients needed comprehensively evaluate LA and LV functions in short-axis and long-axis views.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCompared with segmented cine, CS cine is feasible for LA volumetric assessment but LA strain values derived by the two cine sequences should not be interchanged owing to a remarkable underestimation from CS cine imaging. The ability of LA passive EF, passive radial and longitudinal strain derived from CS cine images to accurately discriminate between patients with and without LV diastolic dysfunction is comparable to that of segmented cine images.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partly supported by the National Natural Science Foundation under grants (81701651 and 81601464).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors of this manuscript declare no impact on results from funding supporters.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYinsu Zhu, Yi Xu and Xiaomei Zhu\u0026nbsp;contributed to the study conception and design. Material preparation, data collection and analysis were performed by\u0026nbsp;Yang Chen, Panpan Xu, Jun Wang, Wen Qian and Wangyan Liu. The first draft of the manuscript was written by\u0026nbsp;Yang Chen and Panpan Xu\u0026nbsp;and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Nanjing Medical University.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm that human research participants provided informed consent for publication of the images in Figure2.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFarzaneh-Far A, Ariyarajah V, Shenoy C, et al. Left atrial passive emptying function during dobutamine stress MR imaging is a predictor of cardiac events in patients with suspected myocardial ischemia. \u003cem\u003eJACC Cardiovasc Imaging.\u003c/em\u003e 2011;4:378-388. http://doi.org/10.1016/j.jcmg.2011.01.009\u003c/li\u003e\n\u003cli\u003eTullio MRD, Homma S. 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Scan-rescan reproducibility of ventricular and atrial MRI feature tracking strain. \u003cem\u003eComput Biol Med.\u003c/em\u003e 2018;92:197-203. http://doi.org/10.1016/j.compbiomed.2017.11.015\u003c/li\u003e\n\u003cli\u003eFeisst A, Kuetting DLR, Dabir D, et al. Influence of observer experience on cardiac magnetic resonance strain measurements using feature tracking and conventional tagging. \u003cem\u003eInt J Cardiol Heart Vasc.\u003c/em\u003e 2018;18:46-51. http://doi.org/10.1016/j.ijcha.2018.02.007\u003c/li\u003e\n\u003cli\u003eBackhaus SJ, Metschies G, Billing M, et al. Defining the optimal temporal and spatial resolution for cardiovascular magnetic resonance imaging feature tracking. \u003cem\u003eJ Cardiovasc Magn Reson.\u003c/em\u003e 2021;23:60. http://doi.org/10.1186/s12968-021-00740-5\u003c/li\u003e\n\u003cli\u003eFeng L, Benkert T, Block KT, Sodickson DK, Otazo R, H C. Compressed sensing for body MRI. \u003cem\u003eJ Magn Reson Imaging.\u003c/em\u003e 2017;45:966-987. http://doi.org/10.1002/jmri.25547\u003c/li\u003e\n\u003cli\u003eNacif MS, Barranhas AD, Turkbey E, et al. Left atrial volume quantification using cardiac MRI in atrial fibrillation: comparison of the Simpson\u0026apos;s method with biplane area-length, ellipse, and three-dimensional methods. \u003cem\u003eDiagn Interv Radiol.\u003c/em\u003e 2013;19:213-220. http://doi.org/10.5152/dir.2012.002\u003cstrong\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Imaging parameters of the segmented cine and compressed sensing (CS) cine sequences\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eSegmented cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eCS cine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eEcho time, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eEcho spacing time, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eField of view, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e380*360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e380*360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eImage matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e208*176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e208*176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eSpatial resolution, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.8*1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1.8*1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eTemporal resolution, ms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eSlice thickness, mm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eFlip angle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eBandwidth, Hz/pixel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eK-lines/segment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eCardiac phases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eCalculated 25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003eTotal scan time, s\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(depend on heart rate)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e120-150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\"\u003e\n \u003cp\u003e20-30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003cem\u003e\u0026nbsp;\u003c/em\u003eComparison of LA volumetric and strain parameters measured with conventional segmented cine and CS cine images\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003eSegmented cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003eCS cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"35.74007220216607%\"\u003e\n \u003cp\u003eNormalized Volumetric parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.967509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.99638989169675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.386281588447654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.90974729241877%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eLAVmin/BSA (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e25.4(16.3, 49.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e27.5(18.4, 52.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.987 (0.979-0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eLAVmax/BSA (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e49.9(38.8, 67.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e47.6(35.7, 64.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.982 (0.970-0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eLAVpre/BSA (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e35.2(25.6, 57.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e33.7(23.2, 56.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.987 (0.978-0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEFtotal (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e48.4(21.7, 56.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e42.2(19.1, 49.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.897 (0.834-0.937)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEFpassive (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e26.2(10.7, 32.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e24.9(11.7, 35.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.859 (0.776-0.913)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEFbooster (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e25.7(11.5, 37.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e15.2(5.5, 26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.779 (0.656-0.861)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"35.74007220216607%\"\u003e\n \u003cp\u003eStrain parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.967509025270758%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.99638989169675%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.386281588447654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.90974729241877%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eErs (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e-13.0(-17.7, -7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e-8.1(-11.4, -5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.712 (0.562-0.816)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e-7.3(-9.0, -4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e-5.6(-7.7, -4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.726 (0.582-0.826)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEra (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e-6.6(-9.1, -3.3)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e-2.5(-4.2, -0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.612 (0.427-0.748)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEls (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e24.7(11.6, 36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e11.9(6.4, 16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.582 (0.388-0.726)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEle (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e14.2(6.6, 22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e8.4(5.5, 13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-4.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.561 (0.362-0.711)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.37837837837838%\"\u003e\n \u003cp\u003eEla (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.47747747747748%\"\u003e\n \u003cp\u003e9.0(4.3, 13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.936936936936938%\"\u003e\n \u003cp\u003e3.2(1.1, 5.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.972972972972974%\"\u003e\n \u003cp\u003e-4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.36936936936937%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.864864864864863%\"\u003e\n \u003cp\u003e0.535 (0.329-0.692)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe data are presented as the median (first quartile, third quartile).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCS, compressed sensing; LA, left atrial; LAVmin, left minimum atrial volume; LAVmax, left maximum atrial volume; LAVpre, left atrial pre-contraction volume; BSA, body surface area; EFtotal, total ejection fraction; EFpassive, passive ejection fraction; EFbooster, active ejection fraction; Ers, global total radial strain; Ere, global passive radial strain; Era, global active radial strain; Els, global total longitudinal strain; Ele, global passive longitudinal strain; Ela, global active longitudinal strain; ICC intraclass correlation coefficient.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Diagnostic performance of LA EFpassive, Ere, and Ele to differentiate between patients with and without diastolic dysfunction using segmented and CS cine methods.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003eCut-off value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003eNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003ePPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" valign=\"top\" width=\"100%\"\u003e\n \u003cp\u003eEFpassive (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eSegmented cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e21.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e77.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e96.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e80.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e96.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e86.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eCS cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e96.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e78.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e95.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e85.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"25.85278276481149%\"\u003e\n \u003cp\u003eEre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.874326750448834%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.901256732495511%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.080789946140037%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.053859964093357%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.59245960502693%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.644524236983843%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eSegmented cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e0.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e-5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e64.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e96.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e72.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e95.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e80.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eCS cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e-4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e64.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e86.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e70.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e83.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e75.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eEle (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eSegmented cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e90.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e80.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e88.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e82.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e85.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.487455197132615%\"\u003e\n \u003cp\u003eCS cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.498207885304659%\"\u003e\n \u003cp\u003e0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.85663082437276%\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.874551971326165%\"\u003e\n \u003cp\u003e74.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.053763440860216%\"\u003e\n \u003cp\u003e83.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.03584229390681%\"\u003e\n \u003cp\u003e75.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.57347670250896%\"\u003e\n \u003cp\u003e82.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.620071684587813%\"\u003e\n \u003cp\u003e78.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC, area under the curve; CS, compressed sensing; LA, left atrial; EFpassive, passive ejection fraction; Ere, global passive radial strain; Ele, global passive longitudinal strain; NPV, negative predictive values; PPV, positive predictive values.\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Intra-observer and inter-observer reproducibility for LA parameters derived from segmented and CS cine sequences\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"13.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"42.26086956521739%\"\u003e\n \u003cp\u003eIntra-observer Reproducibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\" width=\"44.17391304347826%\"\u003e\n \u003cp\u003eInter-observer Reproducibility\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" width=\"30.58350100603622%\"\u003e\n \u003cp\u003eSegmented\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"18.712273641851105%\"\u003e\n \u003cp\u003eCS\u0026nbsp;cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"24.748490945674043%\"\u003e\n \u003cp\u003eSegmented\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;cine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.955734406438633%\"\u003e\n \u003cp\u003eCS\u0026nbsp;cine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003eCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003eCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003eCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003eICC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003eCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eLAVmin/BSA (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.979(0.947-0.992)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.997(0.991- 0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.972(0.930-0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.995(0.987-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eLAVmax/BSA (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.996(0.991-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.970(0.926-0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e21.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.997(0.993-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.975(0.937--0.990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eLAVpre/BSA (ml/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.991(0.978-0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.997(0.991-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.985(0.962-0.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.993(0.984-0.997)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEFtotal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.925(0.822-0.970)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.948(0.874-0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.887(0.737-0.954)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003e23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.950(0.878-0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEFpassive (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.966(0.916-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.943(0.863--0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.903(0.771-0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003e46.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.948(0.873-0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEFbooster (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.976(0.941- 0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.941(0.857-0.976)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"9.375%\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.954(0.888-0.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.986111111111111%\"\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.933(0.838-0.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.069444444444445%\"\u003e\n \u003cp\u003e25.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eErs (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.960(0.902-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.944444444444445%\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.968(0.922-0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.942(0.860-0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.986111111111111%\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.984(0.959-0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.069444444444445%\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEre (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.924(0.818-0.969)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.944444444444445%\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.942(0.860-0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.933(0.839-0.973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.986111111111111%\"\u003e\n \u003cp\u003e6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.950(0.877-0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEra (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.973(0.932-0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.944444444444445%\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.984(0.959- 0.993)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.963(0.910-0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.986111111111111%\"\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.988(0.969-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEls (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.970(0.925-0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.944444444444445%\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.959(0.900-0.984)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e18.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.965(0.0.914-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.986111111111111%\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.967(0.920-0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.069444444444445%\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEle (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.951(0.880-0.980)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.944444444444445%\"\u003e\n \u003cp\u003e19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.936(0.845-0.974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.947(0.871-0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.986111111111111%\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.935(0.843-0.974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEla (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.88888888888889%\"\u003e\n \u003cp\u003e0.972(0.931-0.989)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.944444444444445%\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.988(0.970-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e0.963(0.910-0.985)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.986111111111111%\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.32638888888889%\"\u003e\n \u003cp\u003e0.987(0.967-0.995)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.069444444444445%\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCS, compressed sensing; LA, left atrial; LAVmin, left minimum atrial volume; LAVmax, left maximum atrial volume; LAVpre, left atrial pre-contraction volume; BSA, body surface area; EFtotal, total ejection fraction; EFpassive, passive ejection fraction; EFbooster, active ejection fraction; Ers, global total radial strain; Ere, global passive radial strain; Era, global active radial strain; Els, global total longitudinal strain; Ele, global passive longitudinal strain; Ela, global active longitudinal strain.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Magnetic resonance imaging, Cine, Compressed sensing, Atrial function, Left, Feasibility study","lastPublishedDoi":"10.21203/rs.3.rs-1850358/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1850358/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose: \u003c/strong\u003eTo investigate the feasibility of compressed sensing (CS) cine in quantifying left atrial (LA) volumes and strain assessments compared with conventional segmented cine.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eSegmented and CS cine sequences were acquired in 31 patients with LV diastolic dysfunction defined by echocardiography (21 males, age 51±15 years) and healthy volunteers (22 males, age 39±13 years) using an 3T MR scanner. LA volumes and strains were evaluated in both sequences. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e There was excellent correlation for normalized LA volumes (ICCs ≥ 0.982), good correlation for LA EFs (ICCs ≥ 0.779) and moderate correlation for LA strains (ICCs ≥ 0.535) for both cines. Compared with segmented cine technique, LA passive EF from CS cine was not statistically different (segmented: 26.2 (10.7, 32.5) vs. CS: 24.9 (11.7, 35.4), p = 0.838), but radial and longitudinal strain derived by CS cine technique were markedly underestimated (all p \u0026lt; 0.001). The LA passive EF (EFpassive), passive radial and longitudinal strain values (Ere and Ele) from both cine techniques showed good diagnostic performance without significant differences in discriminating between patients and healthy controls (EFpassive, p=0.794; Ere, p=0.513; Ele, p=0.346).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Compared with segmented cine, LA EFs obtained from CS cine were clinically comparable and LA strain parameters were underestimated. However, the performance of two cine methods to discriminate between patients with and without LV diastolic dysfunction is similar.\u003c/p\u003e","manuscriptTitle":"Comparison between conventional and compressed sensing cine cardiovascular magnetic resonance for left atrial volume and strain assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-22 16:27:24","doi":"10.21203/rs.3.rs-1850358/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"641f508a-da5e-4064-9ae1-1170ecb19f39","owner":[],"postedDate":"July 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-03T19:59:19+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-22 16:27:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1850358","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1850358","identity":"rs-1850358","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}