Cine Balanced Steady-state Free Precession Research Articles

Late Gadolinium Enhancement CMR with Generative AI

BackgroundLate gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging enables imaging of scar/fibrosis and is a cornerstone of most CMR imaging protocols. CMR imaging can benefit from image acceleration; however, image acceleration in LGE remains challenging due to its limited signal-to-noise ratio. In this study, we sought to evaluate a rapid 2D LGE imaging protocol using a generative artificial intelligence (AI) algorithm with inline reconstruction. MethodsA generative AI-based image enhancement was used to improve the sharpness of 2D LGE images acquired with low spatial resolution in the phase-encode direction. The generative AI model is an image enhancement technique built on the enhanced super-resolution generative adversarial network. The model was trained using balanced steady-state free-precession cine images, readily used for LGE without additional training. The model was implemented inline, allowing the reconstruction of images on the scanner console. We prospectively enrolled 100 patients (55 ± 14 years, 72 males) referred for clinical CMR at 3T. We collected three sets of LGE images in each subject, with in-plane spatial resolutions of 1.5×1.5-3-6 mm2. The generative AI model enhanced in-plane resolution to 1.5×1.5 mm2 from the low-resolution counterparts. Images were compared using a blur metric, quantifying the perceived image sharpness (0 = sharpest, 1=blurriest). LGE image sharpness (using a 5-point scale) was assessed by three independent readers. ResultsThe scan times for the three imaging sets were 15±3, 9±2, and 6±1seconds, with inline generative AI-based images reconstructed time of ~37 ms. The generative-AI-based model improved visual image sharpness, resulting in lower blur metric than low-resolution counterparts (AI-enhanced from 1.5×3 mm2 resolution: 0.3±0.03 vs. 0.35±0.03, P<0.01). Meanwhile, AI-enhanced images from 1.5×3 mm2 resolution and original LGE images showed similar blur metric (0.30±0.03 vs. 0.31±0.03, P=1.0) Additionally, there was an overall 18% improvement in image sharpness between AI-enhanced images from 1.5×3 mm2 resolution and original LGE images in the subjective blurriness score (P<0.01). ConclusionsGenerative AI-based model enhances the image quality of 2D LGE images while reducing the scan time and preserving imaging sharpness. Further evaluation in a large cohort is needed to assess the clinical utility of AI-enhanced LGE images for scar evaluation, as this proof-of-concept study does not provide evidence of an impact on diagnosis.

Deep learning super-resolution reconstruction for fast and high-quality cine cardiovascular magnetic resonance.

To compare standard-resolution balanced steady-state free precession (bSSFP) cine images with cine images acquired at low resolution but reconstructed with a deep learning (DL) super-resolution algorithm. Cine cardiovascular magnetic resonance (CMR) datasets (short-axis and 4-chamber views) were prospectively acquired in healthy volunteers and patients at normal (cineNR: 1.89 × 1.96 mm2, reconstructed at 1.04 × 1.04 mm2) and at a low-resolution (2.98 × 3.00 mm2, reconstructed at 1.04 × 1.04 mm2). Low-resolution images were reconstructed using compressed sensing DL denoising and resolution upscaling (cineDL). Left ventricular ejection fraction (LVEF), end-diastolic volume index (LVEDVi), and strain were assessed. Apparent signal-to-noise (aSNR) and contrast-to-noise ratios (aCNR) were calculated. Subjective image quality was assessed on a 5-point Likert scale. Student's paired t-test, Wilcoxon matched-pairs signed-rank-test, and intraclass correlation coefficient (ICC) were used for statistical analysis. Thirty participants were analyzed (37 ± 16 years; 20 healthy volunteers and 10 patients). Short-axis views whole-stack acquisition duration of cineDL was shorter than cineNR (57.5 ± 8.7 vs 98.7 ± 12.4 s; p < 0.0001). No differences were noted for: LVEF (59 ± 7 vs 59 ± 7%; ICC: 0.95 [95% confidence interval: 0.94, 0.99]; p = 0.17), LVEDVi (85.0 ± 13.5 vs 84.4 ± 13.7 mL/m2; ICC: 0.99 [0.98, 0.99]; p = 0.12), longitudinal strain (-19.5 ± 4.3 vs -19.8 ± 3.9%; ICC: 0.94 [0.88, 0.97]; p = 0.52), short-axis aSNR (81 ± 49 vs 69 ± 38; p = 0.32), aCNR (53 ± 31 vs 45 ± 27; p = 0.33), or subjective image quality (5.0 [IQR 4.9, 5.0] vs 5.0 [IQR 4.7, 5.0]; p = 0.99). Deep-learning reconstruction of cine images acquired at a lower spatial resolution led to a decrease in acquisition times of 42% with shorter breath-holds without affecting volumetric results or image quality. Question Cine CMR acquisitions are time-intensive and vulnerable to artifacts. Findings Low-resolution upscaled reconstructions using DL super-resolution decreased acquisition times by 35-42% without a significant difference in volumetric results or subjective image quality. Clinical relevance DL super-resolution reconstructions of bSSFP cine images acquired at a lower spatial resolution reduce acquisition times while preserving diagnostic accuracy, improving the clinical feasibility of cine imaging by decreasing breath hold duration.

Deep-Learning-Based Disease Classification in Patients Undergoing Cine Cardiac MRI.

Automated approaches may allow for fast, reproducible clinical assessment of cardiovascular diseases from MRI. To develop an MRI-based deep learning (DL) disease classification algorithm to distinguish among normal subjects (NORM), patients with dilated cardiomyopathy (DCM), hypertrophic cardiomyopathy (HCM), and ischemic heart disease (IHD). Retrospective. A total of 1337 subjects (55% female), comprising normal subjects (N = 568), and patients with DCM (N = 151), HCM (N = 177), and IHD (N = 441). Balanced steady-state free precession cine sequence at 1.5/3.0 T. Bi-ventricular morphological and functional features and global and segmental left ventricular strain features were automatically extracted from short- and long-axis cine images. Variational autoencoder models were trained on the extracted features and compared against consensus disease label provided by two expert readers (13 and 14 years of experience). Adding unlabeled, normal data to the training was explored to increase specificity of NORM class. Tenfold cross-validation for model development; mean, standard deviation (SD) for measurements; classification metrics: area under the curve (AUC), confusion matrix, accuracy, specificity, precision, recall; 95% confidence intervals; Mann-Whitney U test for significance. AUCs of 0.952 for NORM, 0.881 for DCM, 0.908 for HCM, and 0.856 for IHD and overall accuracy of 0.778 were obtained, with specificity of 0.908 for the NORM class using both SAX and LAX features. Longitudinal strain features slightly improved classification metrics by 0.001 to 0.03 points, except for HCM-AUC. Differences in accuracy, metrics for NORM class and HCM-AUC were statistically significant. Cotraining using unlabeled data increased the specificity for the NORM class to 0.961. Cardiac function features automatically extracted from cine MRI have potential to be used for disease classification, especially for normal-abnormal classification. Feature analyses showed that strain features were important for disease labeling. Cotraining using unlabeled data may help to increase specificity for normal-abnormal classification. 3 TECHNICAL EFFICACY: Stage 1.

Accelerated cardiac magnetic resonance imaging using deep learning for volumetric assessment in children

BackgroundVentricular volumetry using a short-axis stack of two-dimensional (D) cine balanced steady-state free precession (bSSFP) sequences is crucial in any cardiac magnetic resonance imaging (MRI) examination. This task becomes particularly challenging in children due to multiple breath-holds.ObjectiveTo assess the diagnostic performance of accelerated 3-RR cine MRI sequences using deep learning reconstruction compared with standard 2-D cine bSSFP sequences.Material and methodsTwenty-nine consecutive patients (mean age 11 ± 5, median 12, range 1–17 years) undergoing cardiac MRI were scanned with a conventional segmented 2-D cine and a deep learning accelerated cine (three heartbeats) acquisition on a 1.5-tesla scanner. Short-axis volumetrics were performed (semi-)automatically in both datasets retrospectively by two experienced readers who visually assessed image quality employing a 4-point grading scale. Scan times and image quality were compared using the Wilcoxon rank-sum test. Volumetrics were assessed with linear regression and Bland–Altman analyses, and measurement agreement with intraclass correlation coefficient (ICC).ResultsMean acquisition time was significantly reduced with the 3-RR deep learning cine compared to the standard cine sequence (45.5 ± 13.8 s vs. 218.3 ± 44.8 s; P < 0.001). No significant differences in biventricular volumetrics were found. Left ventricular (LV) mass was increased in the deep learning cine compared with the standard cine sequence (71.4 ± 33.1 g vs. 69.9 ± 32.5 g; P < 0.05). All volumetric measurements had an excellent agreement with ICC > 0.9 except for ejection fraction (EF) (LVEF 0.81, RVEF 0.73). The image quality of deep learning cine images was decreased for end-diastolic and end-systolic contours, papillary muscles, and valve depiction (2.9 ± 0.5 vs. 3.5 ± 0.4; P < 0.05).ConclusionDeep learning cine volumetrics did not differ significantly from standard cine results except for LV mass, which was slightly overestimated with deep learning cine. Deep learning cine sequences result in a significant reduction in scan time with only slightly lower image quality.Graphical

The Association of Epicardial Adipose Tissue Volume and Atrial Fibrillation in Patients With Hypertrophic Cardiomyopathy: As Assessed by Cardiac MR.

Epicardial adipose tissue (EAT) is a metabolically active visceral fat linked to cardiovascular disease. Prior studies demonstrated the predictive value of EAT volume (EATV) in atrial fibrillation (AF) among hypertrophic obstructive cardiomyopathy patients. To investigate the association between EATV and AF in hypertrophic cardiomyopathy (HCM). Retrospective. Two hundred and twenty-four HCM patients (including 79 patients with AF and 145 patients without AF, 154 men) and 80 healthy controls (54 men). 3.0 T scanner; balanced steady-state free precession (SSFP) cine sequence, gradient echo. EAT thickness was assessed in the 4-chamber and basal short-axis planes. EAT volume was calculated by outlining the epicardial border and visceral pericardium layer on short-axis cine images. Shapiro-Wilk test, Student's t test or the Mann-Whitney U test, chi-square test or Fisher's exact test, Multivariate linear regression analyses, Multivariable binary logistic regression analysis. Intraclass correlation coefficient. Significance was determined at P < 0.05. EATV and EAT volume index (EATVI) were significantly greater in HCM patients with AF than those without AF (126.6 ± 25.9 mL vs. 90.5 ± 24.5 mL, and 73.0 ± 15.9 mL/m2 vs. 51.3 ± 13.4 mL/m2). EATVI was associated with AF in multivariable linear regression analysis among HCM patients (β = 0.62). Multivariable logistic regression analysis revealed that compared to other indicators, the area under curve (AUC) of EATVI was 0.86 (cut-off, 53.9 mL/m2, 95% CI, 0.80-0.89), provided a better performance, with the sensitivity of 96.2% and specificity of 58.6%. The combined model exhibited superior association with AF presence compared to the clinical model (AUC 0.96 vs. 0.76) and the imaging model (AUC 0.96 vs. 0.93). EATVI was associated with AF. EATVI was significantly correlated with incident AF, and provided a better performance in HCM patients compared to other indicators. 3 TECHNICAL EFFICACY: Stage 2.

Impact of Type 2 Diabetes Mellitus on Left Atrioventricular Coupling and Left Atrial Deformation in Patients with Essential Hypertension: An MRI Feature Tracking Study.

Hypertension (HTN) and type 2 diabetes mellitus (T2DM) are both associated with left ventricular (LV) and left atrial (LA) structural and functional abnormalities; however, the relationship between the left atrium and ventricle in this population is unclear. To identify differences between hypertensive patients with and without T2DM as the basis for further investigation the atrioventricular coupling relationship. Cross-sectional, retrospective study. 89 hypertensive patients without T2DM [HTN (T2DM-)] (age: 58.4 +/- 11.9 years, 48 male), 62 hypertensive patients with T2DM [HTN (T2DM+)] (age: 58.5 +/- 9.1 years, 32 male) and 70 matched controls (age: 55.0 +/- 9.6 years, 37 male). 2D balanced steady-state free precession cine sequence at 3.0 T. LA reservoir, conduit, and booster strain (εs, εe, and εa) and strain rate (SRs, SRe, and SRa), LV radial, circumferential and longitudinal peak strain (PS) and peak systolic strain rate and peak diastolic strain rate (PSSR and PDSR) were derived from LA and LV cine images and compared between groups. Chi-square or Fisher's exact test, one-way analysis of variance, analysis of covariance, Pearson's correlation, multivariable linear regression analysis, and intraclass correlation coefficient. A P value <0.05 was considered significant. Compared with controls, εs, εe, SRe and PS-longitudinal, PDSR-radial, and PDSR-longitudinal were significantly lower in HTN (T2DM-) group, and they were even lower in HTN (T2DM+) group than in both controls and HTN (T2DM-) group. SRs, εa, SRa, as well as PS-radial, PS-circumferential, PSSR-radial, and PSSR-circumferential were significantly lower in HTN (T2DM+) compared with controls. Multivariable regression analyses demonstrated that: T2DM and PS-circumferential and PS-longitudinal (β = -4.026, -0.486, and -0.670, respectively) were significantly associated with εs; T2DM and PDSR-radial and PDSR-circumferential were significantly associated with εe (β = -3.406, -3.352, and -6.290, respectively); T2DM and PDSR-radial were significantly associated with SRe (β = 0.371 and 0.270, respectively); T2DM and PDSR-longitudinal were significantly associated with εa (β = -1.831 and 5.215, respectively); and PDSR-longitudinal was significantly associated with SRa (β = 1.07). In hypertensive patients, there was severer LA dysfunction in those with coexisting T2DM, which may be associated with more severe LV dysfunction and suggests adverse atrioventricular coupling. 3. Stage 3.

Assessing right ventricular peak strain in myocardial infarction patients with mitral regurgitation by cardiac magnetic resonance feature tracking.

Although it is known that mitral regurgitation (MR) in patients with myocardial infarction (MI) may increase the right ventricular (RV) afterload, leading to RV dysfunction, the exact detrimental effects on RV function and myocardial peak strain remain unresolved. In this study, we assessed the impact of MR on the impairment of RV myocardial deformation in patients with MI and explored the independent influential factors of RV peak strain. A total of 199 MI participants without or with MR were retrospectively assessed in this study. The cardiovascular magnetic resonance examination protocol included a late gadolinium-enhanced (LGE) imaging technique and a cine-balanced steady-state free precession sequence. Statistical tests, including two independent sample t-test or Mann-Whitney U-test, analysis of variance, Kruskal-Wallis test, and multiple linear regression analysis models were performed. The MI (MR+) group exhibited significantly lower RV strain parameters in the radial, circumferential and longitudinal directions when compared to the control and the MI (MR-) groups (both P<0.05). The RV global longitudinal peak strain (GLPS) in the MI group significantly decreased when compared with that in the control group (P<0.05). As moderate-severe MR worsened in patients with MI, RV myocardial global peak strain and the peak systolic strain rate (PSSR) gradually decreased. Multiple linear regression analysis revealed that left ventricular (LV) GLPS, triglycerides, and age were independently correlated with RV GLPS (all P<0.05). RV end-systolic volume (RVESV) acted as an independent association factor for RV global peak strain. MR may exacerbate the impairment of RV peak strain and functions in patients with MI. LV GLPS was positively correlated with RV GLPS. However, RVESV, triglycerides, and age acted as independent risk factors associated with worsening RV GLPS.

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT). This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT. Fourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI-Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave-One-Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole-heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), and multiscale structural similarity (MS-SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD. For the whole-heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877mm; and 3) Mean 95% HD dropped from 10.236 to 7.700mm. For the whole-body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS-SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart. CycleGAN-generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.

Utilizing Artificial Intelligence-Based Deformable Registration for Global and Layer-Specific Cardiac MRI Strain Analysis in Healthy Children and Young Adults

The absence of published reference values for multilayer-specific strain measurement using cardiac magnetic resonance (CMR) in young healthy individuals limits its use. This study aimed to establish normal global and layer-specific strain values in healthy children and young adults using a deformable registration algorithm (DRA). A retrospective study included 131 healthy children and young adults (62 males and 69 females) with a mean age of 16.6±3.9 years. CMR examinations were conducted using 1.5T scanners, and strain analysis was performed using TrufiStrain research prototype software (Siemens Healthineers, Erlangen, Germany). Global and layer-specific strain parameters were extracted from balanced Steady-state free precession cine images. Statistical analyses were conducted to evaluate the impact of demographic variables on strain measurements. The peak global longitudinal strain (LS) was -16.0±3.0%, peak global radial strain (RS) was 29.9±6.3%, and peak global circumferential strain (CS) was -17.0±1.8%. Global LS differed significantly between males and females. Transmural strain analysis showed a consistent pattern of decreasing LS and CS from endocardium to epicardium, while radial strain increased. Basal-to-apical strain distribution exhibited decreasing LS and increasing CS in both global and layer-specific analysis. This study uses DRA to provide reference values for global and layer-specific strain in healthy children and young adults. The study highlights the impact of sex and age on LS and body mass index on RS. These insights are vital for future cardiac assessments in children, particularly for early detection of heart diseases.

Prognostic Significance of Biventricular and Biatrial Strain in Dilated Cardiomyopathy: Strain Analysis Derived from Cardiovascular Magnetic Resonance.

Dilated cardiomyopathy (DCM) has a poor prognosis and high mortality. The relationship between the deformation capacity of the biatrial and biventricular regions in patients with DCM remains unclear. This retrospective study used cardiovascular magnetic resonance (CMR) to assess patient enrollment between September 2020 to May 2022. Feature tracking (FT) was used to evaluate biventricular global radial strain (GRS), global circumferential strain (GCS) and global longitudinal strain (GLS). Fast long-axis method was used to evaluate biatrial GLS by analyzing balanced steady-state free precession cine images. The median follow-up period was 362 days (interquartile range: 234 to 500 days). DCM patients were divided into two groups based on the occurrence or non-occurrence of major adverse cardiac event (MACE). The primary endpoint was defined as all-cause death, heart transplantation, and adverse ventricular arrhythmia. The secondary end point included hospitalizations due to heart failure. Cox regression analysis was utilized for variables and Kaplan-Meier survival was utilized for clinical outcomes. There were 124 DCM patients (52.82 12.59 years, 67.74% male) and 53 healthy volunteers (53.17 14.67 years, 52.83% male) recruited in this study. Biventricular GRS, GCS, GLS, and biatrial GLS were significantly impaired in the DCM group compared with the healthy group. In receiver-operating characteristic curve, biatrial GLS and biventricular GRS, GCS, and GLS showed significant prognostic value in predicting MACEs (all p 0.05). In multivariate Cox regression analysis, left ventricular (LV) GLS offered a significant and independent prognostic value surpassing other CMR parameters in predicting MACE. In Kaplan-Meier analysis, patients with a LV GLS -4.81% had a significantly higher rate of MACE (Log-rank p 0.001). LV GLS was independently associated with MACEs in DCM patients by using FT and fast long-axis method derived from CMR. Comprehensive CMR examination including biatrial and biventricular functions should be systematically performed, to understand disease characteristics, as well as improve the risk stratification and therapeutic management for patients with DCM.

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation.

Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.

Territorial Post-Revascularization Remodeling of Chronic Coronary Syndromes: Cardiac Magnetic Resonance Feature Tracking Study.

MRI feature-tracking (MRI-FT) can accurately assess ventricular myocardial deformation and regional function and may be a better predictor of mortality than ejection fraction and infarct extension. However, role of MRI-FT in assessing coronary revascularization is unclear. To assess coronary revascularization effect on territorial left ventricle (LV) function of chronic coronary syndrome (CCS) patients by MRI-FT. Prospective. 50 CCS patients (age: 62.22 ± 8.70 years) scheduled for elective percutaneous coronary intervention (PCI), and 30 healthy controls (age: 35.33 ± 11.57 years). 1.5T with balanced steady-state free precession cine sequence. Global and segmental peak systolic longitudinal, circumferential, and radial myocardial strains were quantified in both patient and healthy control groups by an experienced operator using dedicated software. Patients were studied both pre-PCI and 6-month post-PCI and LV territorial myocardial strain values were calculated by averaging the segmental values of each revascularized territory. Student's t-test, paired t-test, Mann Whitney test, and Wilcoxon signed ranks test. Significance was judged at the 5% level. Territorial longitudinal strain showed significant 6-month post-PCI improvement in the left anterior descending (LAD) and right coronary artery (RCA) territories, but there was not in the left circumflex (LCX) territory (LAD: mean - 11.41% ± 3.45% pre, -13.01% ± 3.53% post; RCA: mean - 11.11% ± 2.65% pre, -13.25% ± 2.81% post; and LCX: mean - 15.43% ± 3.97% pre, -16.17% ± 4.38% post, P = 0.215). Territorial circumferential strain showed significant post-PCI improvement in all revascularized territories (LAD: mean - 13.73% ± 6.56% pre, -16.98% ± 6.01% post; LCX: mean - 13.23% ± 4.23% pre, -16.34% ± 3.45% post; and RCA: mean - 11.24% ± 3.36% pre, -13.80% ± 3.51% post). Territorial radial strain showed no significant post-PCI improvement (LAD: mean 22.73% ± 12.38% pre, 21.79% ± 11.55% post, P = 0.541; LCX: mean 27.73% ± 7.95% pre, 29.0% ± 7.25% post, P = 0.264; and RCA: mean 36.68% ± 11.10% pre, 31.75% ± 10.95% post, P = 0.208). Territorial LV systolic function was significantly improved by coronary revascularization in CCS patients. 1 TECHNICAL EFFICACY: Stage 4.

Assessment of the Prognostic Value of MRI Left Ventricular Global Function Index (LVGFI) in Patients With End-Stage Renal Disease Under Maintenance Dialysis.

Left ventricular global function index (LVGFI) integrates LV volumetric and functional parameters. In patients with end-stage renal disease (ESRD), cardiac injury manifests as LV hypertrophy and dysfunction. However, the prognostic value of LVGFI in this population remains unclear. To investigate the association of LVGFI with major adverse cardiac events (MACE) in patients with ESRD. Prospective. One hundred fifty-eight ESRD patients (mean age: 54.1 ± 14.4 years; 105 male) on maintenance dialysis. 3.0 T, balanced steady-state free precession (bSSFP) cine and modified Look-Locker inversion recovery (MOLLI) sequences. LV volumetric and functional parameters were determined from bSSFP images. LVGFI was calculated as the ratio of stroke volume to global volume and native T1 was determined from MOLLI T1 maps. MACE was recorded on follow up. Models were developed to predict MACE from conventional risk factors combined with LVGFI, GLS, native T1, and LV mass index (LVMI), respectively. Subgroup analyses were further performed in participants with LVEF above median. Cox proportional hazard regression and log-rank test were used to investigate the association between LVGFI and MACE. The predictive models were evaluated and compared using Harrell's C-statistics and DeLong tests. A P value <0.05 was considered statistically significant. Thirty-four MACE occurred during the median follow-up period of 26 months. The hazard of MACE increased by 114% for each 10% decrease in LVGFI in univariable analysis. The predictive model consisting of LVGFI (C-statistic: 0.724) had significantly better predictive performance than the others (all P < 0.001). These results were consistent in patients (N = 79) with LVEF > median (63.54%). LVGFI is a novel marker for MACE risk stratification in patients with ESRD and was better able to predict MACE than native T1 mapping and GLS. 2 TECHNICAL EFFICACY: Stage 3.

Glycemic Status and Myocardial Strain by Cardiac MRI in Patients With ST-Segment Elevation Myocardial Infarction.

It is uncertain how various degree of glycemic status affect left ventricular (LV) myocardial strain in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (PPCI). To investigate the relationship of glycemic status and myocardial strain in STEMI patients. Prospective cohort study. 282 STEMI patients with cardiac magnetic resonance imaging 5 ± 2 days post-PPCI. Patients were divided into three groups based on the level of glycated hemoglobin A1c (HbA1c) (group 1: HbA1c < 5.7%; group 2: 5.7% ≤ HbA1c < 6.5%; group 3: HbA1c ≥ 6.5%). 3.0-T; late gadolinium enhancement, balanced steady-state free precession cine sequence, black blood fat-suppressed T2-weighted. LV function, myocardial strain, and infarct characteristics (infarct size, microvascular obstruction, and intramyocardial hemorrhage) were compared among the three groups by one-way analysis of variance (ANOVA) or Wilcoxon rank sum test. Intraobserver and interobserver reproducibility of LV myocardial strain was evaluated. ANOVA or Wilcoxon rank sum test, Pearson chi-square or Fisher's exact test, Spearman's correlation analyses and multivariable linear regression analysis. A two-tailed P value <0.05 was considered statistically significant. Infarct characteristics were similar among the three groups (P = 0.934, P = 0.097, P = 0.533, respectively). Patients with HbA1c ≥ 6.5% had decreased LV myocardial strain compared with HbA1c 5.7%-6.4%, as evidenced by global radial (GRS), global circumferential (GCS), and global longitudinal (GLS) strain. However, no significant differences in myocardial strain were observed between patients with HbA1c 5.7%-6.4% and HbA1c < 5.7% (P = 0.716; P = 0.294; P = 0.883, respectively). After adjustment for confounders, HbA1c as a continuous variable (beta coefficient [β] = -0.676; β = 0.172; β = 0.205, respectively) and HbA1c ≥ 6.5% (β = -3.682; β = 0.552; β = 0.681, respectively) were both independently associated with decreased GRS, GCS, and GLS. Patients with uncontrolled blood glucose (categorized in group HbA1c ≥ 6.5%) had worse myocardial strain. The level of HbA1c appeared to be independently associated with decreased myocardial strain in STEMI patients. 2 TECHNICAL EFFICACY STAGE: 2.

Assessment of Myocardial Viability and Risk Stratification in Coronary Chronic Total Occlusion: A Qualitative and Quantitative Stress Cardiac MRI Study.

Indicators for assessing myocardial viability and risk stratification in patients with coronary chronic total occlusion (CTO) are still in the research stage. To use stress-MRI to assess myocardial function, blood perfusion, and viability and to explore their relationship with collateral circulation. Prospective. Fifty-one patients with CTO in at least one major artery confirmed by X-ray coronary angiography (male: 46; age 55.2 ± 10.8 years). 3.0T; TurboFlash, balanced steady-state free precession cine, and phase-sensitive inversion recovery sequences. Stress-MRI was used to obtain qualitative and quantitative parameters of segmental myocardium. Myocardial segments supplied by CTO target vessels were grouped according to the degree of collateral circulation assessed by radiographic coronary angiography (no/mild, moderate, or good). Depending on qualitative stress perfusion assessment and late gadolinium enhancement (LGE) extent, segments were also categorized as negative, viable, or trans-infarcted. Independent sample Student's t-test, one-way analysis of variance (ANOVA) test, Mann-Whitney U test, Kruskal-Wallis test, Spearman correlation coefficient (r). P < 0.05 was considered statistically significant. A total of 334 segments were supplied by CTO target vessels. The radial strain (RS), circumferential strain (CS), longitudinal strain (LS) of the negative, viable, and trans-infarcted regions showed a significant and stepwise impairment. Myocardial blood flow at rest was positively correlated with RS, CS, and LS (r = 0.42, 0.43, 0.38, respectively). Among the different collateral circulation, there were no significant differences in RS, CS, LS, and LGE volume (P = 0.788, 0.562, 0.122, 0.170, respectively), and there were also no statistically significant differences in the proportions of negative, viable, and trans-infarcted regions (P = 0.372). Myocardial perfusion obtained by stress-MRI combined with strain and LGE may comprehensively evaluate myocardial function and viability, and has potential to facilitate risk stratification of CTO. 2 TECHNICAL EFFICACY: Stage 1.

Serial Cardiac MRI for Quantification of the Dynamics of Anthracycline-Induced Subclinical Myocardial Injury.

Anthracyclines are known to be associated with chemotherapy-induced cardiotoxicity. Limited data focus on dynamic myocardial injury during the course of chemotherapy in patients with breast cancer. To investigate the variation of tissue characterization and myocardial deformation derived by cardiac MRI during anthracycline chemotherapy. Prospective. Fifty-eight female breast cancer patients (mean age: 52.82 ± 2.61 years) were enrolled. A 3.0-T, cardiac MRI including cine balanced steady-state free precession, a modified Looker-Locker inversion recovery (MOLLI), and a fast spin echo (FSE) T2-weighted sequences were performed. Cardiac MRI was performed baseline and after two, four, and six cycles of chemotherapy. Assessment of global longitudinal strain (GLS), global circumstance strain (GCS), global radial strain (GRS), and strain rate (GLS-s, GCS-s, GRS-s) and T1, T2 and T2* were accomplished by CVI42. The anthracycline dose and risk factors were also collected before each cardiac MRI. Analysis of variance (ANOVA) for repeated measures was used to compare the changes in LVEF cardiac function, strain and T1/T2/T2* parameters over time. Pearson correlation analyses were performed to estimate the potential associations between differences in myocardial characteristics (∆) and the chemotherapy cycle. A P value <0.05 was considered statistically significant. LVEF was not significantly different from pretreatment MRI regarding each cycle of chemotherapy (P=0.54). Compared with baseline, patients had significantly lower GLS (-15.85% ± 0.83%, -14.50% ± 0.88%, -12.34% ± 1.01% vs. -18.82% ± 0.92%) and GLS-s (-0.71% ± 0.07%, -0.65% ± 0.05%, -0.64% ± 0.04% vs. -0.95 ± 0.06%) and increased T2 values (57.21 ± 4.27 msec, 58.60 ± 3.93 msec, 58.10 ± 3.17 msec vs. 43.88 ± 3.28 msec) at two, four and six cycles of chemotherapy treatment. ∆GLS and ∆GLS-s were significantly associated with the chemotherapy cycle (correlation coefficients for GLS=0.75, GLS-s=0.75). Cardiac MRI can precisely detect the dynamic changes of anthracycline-induced subclinical myocardial injury that is represented as a gradually decrease in GLS and GLS-s. These parameters may provide new insight for monitoring risk and therapy in patients with breast cancer. 2. Stage 1.

Rapid MRI Assessment of Long-Axis Strain to Indicate Systolic Dysfunction in Patients With Sickle Cell Disease.

Patients with sickle cell disease (SCD) have a unique form of cardiomyopathy. However, left ventricular ejection fraction (LVEF) is often preserved. Monoplanar long-axis strain (LAS) can be assessed from MRI four-chamber views and may be better at detecting mild systolic dysfunction in these patients. To compare LAS (monoplanar and biplanar) with LVEF as a marker of systolic dysfunction in SCD patients. Retrospective. A total of 20 patients with genetically proven SCD (35 MRI examinations), 39 healthy controls, and 124 patients with systemic iron overload (for validation purposes). 1.5 T/3T. Cine balanced steady-state free-precession. Rapidly assessed biplanar LAS from four- and two-chamber views was correlated with age and compared to LVEF by two operators. For validation, biplanar LAS was compared to global longitudinal strain (GLS) using MRI feature-tracking in 124 patients with systemic iron overload. Bland-Altman analysis. Wilcoxon-Mann-Whitney test and Spearman-rank correlation (correlation coefficient, rS ). Receiver-operating-characteristic (ROC) curve analysis (area under the curve, AUC). Bivariate discriminant analysis. Significance level: P < 0.01. There was strong correlation between biplanar LAS and GLS using feature tracking (rS =0.73). Interoperator agreement showed nonsignificant bias for biplanar LAS (-0.02%; ±95%-agreement interval -2.2%/2.2%, P= 0.9). Biplanar LAS increased significantly with age in controls (rS =0.70). In SCD patients, biplanar LAS was better correlated with age than monoplanar LAS (r2 =0.53, standard error of estimate, SEE=1.4% vs. r2 =0.37;SEE=2.0%). ROC analysis of LVEF, biplanar LAS, and age-adjusted Z-scores Z (LAS(age)) showed AUCs of 0.69, 0.75, and 0.86 for differentiation between SCD patients and controls. Bivariate discriminant analysis of biplanar Z (LAS(age)) and LVEF revealed a sensitivity of 63% and a specificity of 95%. Rapidly assessed biplanar LAS demonstrated high diagnostic accuracy and was an indicator of mild systolic dysfunction in patients with SCD. Biplanar LAS provided more precise measurements than monoplanar, and normalization to age increased diagnostic accuracy. 3. Stage 2.

MRI Investigation of the Differential Impact of Left Ventricular Ejection Fraction After Myocardial Infarction in Elderly vs. Nonelderly Patients to Predict Readmission for Heart Failure.

Patients with ST-segment elevation myocardial infarction (STEMI), especially elderly individuals, have an increased risk of readmission for acute heart failure (AHF). To study the impact of left ventricular ejection fraction (LVEF) by MRI to predict AHF in elderly (>70 years) and nonelderly patients after STEMI. Prospective. Multicenter registry of 759 reperfused STEMI patients (23.3% elderly). 1.5-T. Balanced steady-state free precession (cine imaging) and segmented inversion recovery steady-state free precession (late gadolinium enhancement) sequences. One-week MRI-derived LVEF (%) was quantified. Sequential MRI data were recorded in 579 patients. Patients were categorized according to their MRI-derived LVEF as preserved (p-LVEF, ≥50%), mildly reduced (mr-LVEF, 41%-49%), or reduced (r-LVEF, ≤40%). Median follow-up was 5 [2.33-7.54] years. Univariable (Student's t, Mann-Whitney U, chi-square, and Fisher's exact tests) and multivariable (Cox proportional hazard regression) comparisons and continuous-time multistate Markov model to analyze transitions between LVEF categories and to AHF. Hazard ratios (HR) with 95% confidence intervals (CIs) were computed. P < 0.05 was considered statistically significant. Over the follow-up period, 79 (10.4%) patients presented AHF. MRI-LVEF was the most robust predictor in nonelderly (HR 0.94 [0.91-0.98]) and elderly patients (HR 0.94 [0.91-0.97]). Elderly patients had an increased AHF risk across the LVEF spectrum. An excess of risk (compared to p-LVEF) was noted in patients with r-LVEF both in nonelderly (HR 11.25 [5.67-22.32]) and elderly patients (HR 7.55 [3.29-17.34]). However, the mr-LVEF category was associated with increased AHF risk only in elderly patients (HR 3.66 [1.54-8.68]). Less transitions to higher LVEF states (n=19, 30.2% vs. n=98, 53%) and more transitions to AHF state (n=34, 53.9% vs. n=45, 24.3%) were observed in elderly than nonelderly patients. MRI-derived p-LVEF confers a favorable prognosis and r-LVEF identifies individuals at the highest risk of AHF in both elderly and nonelderly patients. Nevertheless, an excess of risk was also found in the mr-LVEF category in the elderly group. 2. Stage 2.

Fetal Cardiac Cine MRI with Doppler US Gating in Complex Congenital Heart Disease.

To apply Doppler US (DUS)-gated fetal cardiac cine MRI in clinical routine and investigate diagnostic performance in complex congenital heart disease (CHD) compared with that of fetal echocardiography. In this prospective study (May 2021 to March 2022), women with fetuses with CHD underwent fetal echocardiography and DUS-gated fetal cardiac MRI on the same day. For MRI, balanced steady-state free precession cine images were acquired in the axial and optional sagittal and/or coronal orientations. Overall image quality was assessed on a four-point Likert scale (from 1 = nondiagnostic to 4 = good image quality). The presence of abnormalities in 20 fetal cardiovascular features was independently assessed by using both modalities. The reference standard was postnatal examination results. Differences in sensitivities and specificities were determined by using a random-effects model. The study included 23 participants (mean age, 32 years ± 5 [SD]; mean gestational age, 36 weeks ± 1). Fetal cardiac MRI was completed in all participants. The median overall image quality of DUS-gated cine images was 3 (IQR, 2.5-4). In 21 of 23 participants (91%), underlying CHD was correctly assessed by using fetal cardiac MRI. In one case, the correct diagnosis was made by using MRI only (situs inversus and congenitally corrected transposition of the great arteries). Sensitivities (91.8% [95% CI: 85.7, 95.1] vs 93.6% [95% CI: 88.8, 96.2]; P = .53) and specificities (99.9% [95% CI: 99.2, 100] vs 99.9% [95% CI: 99.5, 100]; P > .99) for the detection of abnormal cardiovascular features were comparable between MRI and echocardiography, respectively. Using DUS-gated fetal cine cardiac MRI resulted in performance comparable with that of using fetal echocardiography for diagnosing complex fetal CHD.Keywords: Pediatrics, MR-Fetal (Fetal MRI), Cardiac, Heart, Congenital, Fetal Imaging, Cardiac MRI, Prenatal, Congenital Heart DiseaseClinical trial registration no. NCT05066399 Supplemental material is available for this article. © RSNA, 2023See also the commentary by Biko and Fogel in this issue.

An Integrated Algorithm for Differentiating Hypertrophic Cardiomyopathy From Hypertensive Heart Disease.

Differentiating hypertrophic cardiomyopathy (HCM) from hypertensive heart disease (HHD) is challenging. To identify differences between HCM and HHD on a patient basis using MRI. Retrospective. A total of 219 subjects, 148 in phase I (baseline data and algorithm development: 75 HCM, 33 HHD, and 40 controls) and 71 in phase II (algorithm validation: 56 HCM and 15 HHD). Contrast-enhanced inversion-prepared gradient echo and cine-balanced steady-state free precession sequences at 3.0 T. MRI parameters assessed included left ventricular (LV) ejection fraction (LVEF), LV end systolic and end diastolic volumes (LVESV and LVEDV), mean maximum LV wall thickness (MLVWT), LV global longitudinal and circumferential strain (GRS, GLS, and GCS), and native T1. Parameters, which were significantly different between HCM and HHD in univariable analysis, were entered into a principal component analysis (PCA). The selected components were then introduced into a multivariable regression analysis to model an integrated algorithm (IntA) for screening the two disorders. IntA performance was assessed for patients with and without LGE in phase I (development) and phase II (validation). Univariable regression, PCA, receiver operating curve (ROC) analysis. A P value <0.05 was considered statistically significant. Derived IntA formulation included LVEF, LVESV, LVEDV, MLVWT, and GCS. In LGE-positive subjects in phase l, the cutoff point of IntA ≥81 indicated HCM (83% sensitivity and 91% specificity), with the area under the ROC curve (AUC) of 0.900. In LGE-negative subjects, a higher possibility of HCM was indicated by a cutoff point of IntA ≥84 (100% sensitivity and 82% specificity), with an AUC of 0.947. Validation of IntA in phase II resulted in an AUC of 0.846 in LGE-negative subjects and 0.857 in LGE-positive subjects. A per-patient-based IntA algorithm for differentiating HCM and HHD was generated from MRI data and incorporated FT, LGE and morphologic parameters. 3. Stage 2.