Quantitative metrics of the LV trabeculated layer by cardiac CT and cardiac MRI in patients with suspected noncompaction cardiomyopathy.

OBJECTIVES: To compare cardiac computed tomography (CCT) and cardiac magnetic resonance (CMR) for the quantitative assessment of the left ventricular (LV) trabeculated layer in patients with suspected noncompaction cardiomyopathy (NCCM). MATERIALS AND METHODS: Subjects with LV excessive trabeculation who underwent both CMR and CCT imaging as part of the prospective international multicenter NONCOMPACT clinical study were included. For each subject, short-axis CCT and CMR slices were matched. Four quantitative metrics were estimated: 1D noncompacted-to-compacted ratio (NCC), trabecular-to-myocardial area ratio (TMA), trabecular-to-endocardial cavity area ratio (TCA), and trabecular-to-myocardial volume ratio (TMV). In 20 subjects, end-diastolic and mid-diastolic CCT images were compared for the quantification of the trabeculated layer. Relationships between the metrics were investigated using linear regression models and Bland-Altman analyses. RESULTS: Forty-eight subjects (49.9 ± 12.8 years; 28 female) were included in this study. NCC was moderately correlated (r = 0.62), TMA and TMV were strongly correlated (r = 0.78 and 0.78), and TCA had excellent correlation (r = 0.92) between CMR and CCT, with an underestimation bias from CCT of 0.3 units, and 5.1, 4.8, and 5.4 percent-points for the 4 metrics, respectively. TMA, TCA, and TMV had excellent correlations (r = 0.93, 0.96, 0.94) and low biases (- 3.8, 0.8, - 3.8 percent-points) between the end-diastolic and mid-diastolic CCT images. CONCLUSIONS: TMA, TCA, and TMV metrics of the LV trabeculated layer in patients with suspected NCCM demonstrated high concordance between CCT and CMR images. TMA and TCA were highly reproducible and demonstrated minimal differences between mid-diastolic and end-diastolic CCT images. CLINICAL RELEVANCE STATEMENT: The results indicate similarity of CCT to CMR for quantifying the LV trabeculated layer, and the small differences in quantification between end-diastole and mid-diastole demonstrate the potential for quantifying the LV trabeculated layer from clinically performed coronary CT angiograms. KEY POINTS: • Data on cardiac CT for quantifying the left ventricular trabeculated layer are limited. • Cardiac CT yielded highly reproducible metrics of the left ventricular trabeculated layer that correlated well with metrics defined by cardiac MR. • Cardiac CT appears to be equivalent to cardiac MR for the quantification of the left ventricular trabeculated layer.

Opportunistic Screening for Atrial Fibrillation on Routine Chest Computed Tomography.

PURPOSE: Quantitative biomarkers from chest computed tomography (CT) can facilitate the incidental detection of important diseases. Atrial fibrillation (AFib) substantially increases the risk for comorbid conditions including stroke. This study investigated the relationship between AFib status and left atrial enlargement (LAE) on CT. MATERIALS AND METHODS: A total of 500 consecutive patients who had undergone nongated chest CTs were included, and left atrium maximal axial cross-sectional area (LA-MACSA), left atrium anterior-posterior dimension (LA-AP), and vertebral body cross-sectional area (VB-Area) were measured. Height, weight, age, sex, and diagnosis of AFib were obtained from the medical record. Parametric statistical analyses and receiver operating characteristic curves were performed. Machine learning classifiers were run with clinical risk factors and LA measurements to predict patients with AFib. RESULTS: Eighty-five patients with a diagnosis of AFib were identified. Mean LA-MACSA and LA-AP were significantly larger in patients with AFib than in patients without AFib (28.63 vs. 20.53 cm 2 , P <0.000001; 4.34 vs. 3.5 cm, P <0.000001, respectively), both with area under the curves (AUCs) of 0.73. Multivariable logistic regression analysis including age, sex, and VB-Area with LA-MACSA improved the AUC for predicting AFib (AUC=0.77). An LA-MACSA threshold of 30 cm 2 demonstrated high specificity for AFib diagnosis at 92% and sensitivity of 48%, and LA-AP threshold at 4.5 cm demonstrated 90% specificity and 42% sensitivity. A Bayesian machine learning model using age, sex, height, body surface area, and LA-MACSA predicted AFib with an AUC of 0.743. CONCLUSIONS: LA-MACSA or LA-AP can be rapidly measured from routine chest CT, and when >30 cm 2 and >4.5 cm, respectively, are specific indicators to predict patients at increased risk for AFib.

Octree Representation Improves Data Fidelity of Cardiac CT Images and Convolutional Neural Network Semantic Segmentation of Left Atrial and Ventricular Chambers.

PURPOSE: To assess whether octree representation and octree-based convolutional neural networks (CNNs) improve segmentation accuracy of three-dimensional images. MATERIALS AND METHODS: Cardiac CT angiographic examinations from 100 patients (mean age, 67 years ± 17 [standard deviation]; 60 men) performed between June 2012 and June 2018 with semantic segmentations of the left ventricular (LV) and left atrial (LA) blood pools at the end-diastolic and end-systolic cardiac phases were retrospectively evaluated. Image quality (root mean square error [RMSE]) and segmentation fidelity (global Dice and border Dice coefficients) metrics of the octree representation were compared with spatial downsampling for a range of memory footprints. Fivefold cross-validation was used to train an octree-based CNN and CNNs with spatial downsampling at four levels of image compression or spatial downsampling. The semantic segmentation performance of octree-based CNN (OctNet) was compared with the performance of U-Nets with spatial downsampling. RESULTS: Octrees provided high image and segmentation fidelity (median RMSE, 1.34 HU; LV Dice coefficient, 0.970; LV border Dice coefficient, 0.843) with a reduced memory footprint (87.5% reduction). Spatial downsampling to the same memory footprint had lower data fidelity (median RMSE, 12.96 HU; LV Dice coefficient, 0.852; LV border Dice coefficient, 0.310). OctNet segmentation improved the border segmentation Dice coefficient (LV, 0.612; LA, 0.636) compared with the highest performance among U-Nets with spatial downsampling (Dice coefficients: LV, 0.579; LA, 0.592). CONCLUSION: Octree-based representations can reduce the memory footprint and improve segmentation border accuracy.Keywords CT, Cardiac, Segmentation, Supervised Learning, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.

M-SiSSR: Regional Endocardial Function Using Multilabel Simultaneous Subdivision Surface Registration.

Quantification of regional cardiac function is a central goal of cardiology. Multiple methods, such as Coherent Point Drift (CPD) and Simultaneous Subdivision Surface Registration (SiSSR), have been used to register meshes to the endocardial surface. However, these methods do not distinguish between cardiac chambers during registration, and consequently the mesh may "slip" across the interface between two structures during contraction, resulting in inaccurate regional functional measurements. Here, we present Multilabel-SiSSR (M-SiSSR), a novel method for registering a "labeled" cardiac mesh (with each triangle assigned to a cardiac structure). We compare our results to the original, label-agnostic version of SiSSR and find both a visual and quantitative improvement in tracking of the mitral valve plane.

Vascular Landmark-Based Method for Highly Reproducible Measurement of Left Atrial Appendage Volume in Computed Tomography.

BACKGROUND: Modern computed tomographic scanning can produce 4-dimensional images of the left atrial appendage (LAA). LAA function and morphology can then be measured, to plan interventions such as occlusion and to evaluate LAA flow for thrombogenic risk analysis. A current problem here is defining a reproducible boundary between the LAA and the left atrium. METHODS: This study used retrospectively gated 4-dimensional computed tomographic data from 25 implantation and coronary artery imaging patients. In each patient, the LAA ostium was defined at multiple time points during the RR interval. To examine the reproducibility of the definition of the LAA ostium, 3 observers analyzed all time frames in each patient 3 times. Five nonconsecutive time frames from each patient were then compared using intraclass correlation coefficients to quantify the precision of the method across patients. The correlation of LAA volumes for each time frame of each patient was determined across the different observers (interobserver) and within each observer's own data sets (intraobserver). RESULTS: The method was successful in 92% of patients. Two-way random-effect, absolute-agreement, single-measurement intraclass correlation coefficients for interobserver measurements were 0.984, 0.990, and 0.988, with intraobserver intraclass correlation coefficients of 0.989, 0.989, and 0.995. The intraclass correlation coefficient of all observations was 0.988. CONCLUSIONS: Classification of the LAA ostium using a stepwise procedure identifying the coumadin ridge and 2 vascular landmarks in ECG-gated computed tomography provides a viable method of establishing a highly reproducible boundary between the atrium and LAA needed to obtain LAA metrics useful for procedure planning and measuring LAA function.

Regional dynamics of fractal dimension of the left ventricular endocardium from cine computed tomography images.

We present a method to leverage the high fidelity of computed tomography (CT) to quantify regional left ventricular function using topography variation of the endocardium as a surrogate measure of strain. 4DCT images of 10 normal and 10 abnormal subjects, acquired with standard clinical protocols, are used. The topography of the endocardium is characterized by its regional values of fractal dimension ( F D ), computed using a box-counting algorithm developed in-house. The average F D in each of the 16 American Heart Association segments is calculated for each subject as a function of time over the cardiac cycle. The normal subjects show a peak systolic percentage change in F D of 5.9 % ± 2 % in all free-wall segments, whereas the abnormal cohort experiences a change of 2 % ± 1.2 % ( p < 0.00001 ). Septal segments, being smooth, do not undergo large changes in F D . Additionally, a principal component analysis is performed on the temporal profiles of F D to highlight the possibility for unsupervised classification of normal and abnormal function. The method developed is free from manual contouring and does not require any feature tracking or registration algorithms. The F D values in the free-wall segments correlated well with radial strain and with endocardial regional shortening measurements.

Left Ventricular Strain Is Abnormal in Preclinical and Overt Hypertrophic Cardiomyopathy: Cardiac MR Feature Tracking.

Purpose To evaluate myocardial strain and circumferential transmural strain difference (cTSD; the difference between epicardial and endocardial circumferential strain) in a genotyped cohort with hypertrophic cardiomyopathy (HCM) and to explore correlations between cTSD and other anatomic and functional markers of disease status. Left ventricular (LV) dysfunction may indicate early disease in preclinical HCM (sarcomere mutation carriers without LV hypertrophy). Cardiac MRI feature tracking may be used to evaluate myocardial strain in carriers of HCM sarcomere mutation. Materials and Methods Participants with HCM and their family members participated in a prospective, multicenter, observational study (HCMNet). Genetic testing was performed in all participants. Study participants underwent cardiac MRI with temporal resolution at 40 msec or less. LV myocardial strain was analyzed by using feature-tracking software. Circumferential strain was measured at the epicardial and endocardial surfaces; their difference yielded the circumferential transmural strain difference (cTSD). Multivariable analysis to predict HCM status was performed by using multinomial logistic regression adjusting for age, sex, and LV parameters. Results Ninety-nine participants were evaluated (23 control participants, 34 participants with preclinical HCM [positive for sarcomere mutation and negative for LV hypertrophy], and 42 participants with overt HCM [positive for sarcomere mutation and negative for LV hypertrophy]). The average age was 25 years ± 11 and 44 participants (44%) were women. Maximal LV wall thickness was 9.5 mm ± 1.4, 9.8 mm ± 2.2, and 16.1 mm ± 5.3 in control participants, participants with preclinical HCM (P = .496 vs control participants), and participants with overt HCM (P < .001 vs control participants), respectively. cTSD for control participants, preclinical HCM, and overt HCM was 14% ± 4, 17% ± 4, and 22% ± 7, respectively (P < .01 for all comparisons). In multivariable models (controlling for septal thickness and log-transformed N-terminal brain-type natriuretic peptide), cTSD was predictive of preclinical and overt HCM disease status (P < .01). Conclusion Cardiac MRI feature tracking identifies myocardial dysfunction not only in participants with overt hypertrophic cardiomyopathy, but also in carriers of sarcomere mutation without left ventricular hypertrophy, suggesting that contractile abnormalities are present even when left ventricular wall thickness is normal. © RSNA, 2018 Online supplemental material is available for this article.

Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks.

Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained using three-fold cross-validation on a cohort of patients with hypertrophic cardiomyopathy (HCM, N=42) and healthy control subjects (N=21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch using five-fold cross-validation on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.

SiSSR: Simultaneous subdivision surface registration for the quantification of cardiac function from computed tomography in canines.

Recent improvements in cardiac computed tomography (CCT) allow for whole-heart functional studies to be acquired at low radiation dose (<2mSv) and high-temporal resolution (<100ms) in a single heart beat. Although the extraction of regional functional information from these images is of great clinical interest, there is a paucity of research into the quantification of regional function from CCT, contrasting with the large body of work in echocardiography and cardiac MR. Here we present the Simultaneous Subdivision Surface Registration (SiSSR) method: a fast, semi-automated image analysis pipeline for quantifying regional function from contrast-enhanced CCT. For each of thirteen adult male canines, we construct an anatomical reference mesh representing the left ventricular (LV) endocardium, obviating the need for a template mesh to be manually sculpted and initialized. We treat this generated mesh as a Loop subdivision surface, and adapt a technique previously described in the context of 3-D echocardiography to register these surfaces to the endocardium efficiently across all cardiac frames simultaneously. Although previous work performs the registration at a single resolution, we observe that subdivision surfaces naturally suggest a multiresolution approach, leading to faster convergence and avoiding local minima. We additionally make two notable changes to the cost function of the optimization, explicitly encouraging plausible biological motion and high mesh quality. Finally, we calculate an accepted functional metric for CCT from the registered surfaces, and compare our results to an alternate state-of-the-art CCT method.

Internal tissue references for 18Fluorodeoxyglucose vascular inflammation imaging: Implications for cardiovascular risk stratification and clinical trials.

INTRODUCTION: 18Fluorodeoxyglucose (FDG) positron emission tomography (PET) uptake in the artery wall correlates with active inflammation. However, in part due to the low spatial resolution of PET, variation in the apparent arterial wall signal may be influenced by variation in blood FDG activity that cannot be fully corrected for using typical normalization strategies. The purpose of this study was to evaluate the ability of the current common methods to normalize for blood activity and to investigate alternative methods for more accurate quantification of vascular inflammation. MATERIALS AND METHODS: The relationship between maximum FDG aorta wall activity and mean blood activity was evaluated in 37 prospectively enrolled subjects aged 55 years or more, treated for hyperlipidemia. Target maximum aorta standardized uptake value (SUV) and mean background reference tissue activity (blood, spleen, liver) were recorded. Target-to-background ratios (TBR) and arterial maximum activity minus blood activity were calculated. Multivariable regression was conducted, predicting uptake values based on variation in background reference and target tissue FDG uptake; adjusting for gender, age, lean body mass (LBM), blood glucose, blood pool activity, and glomerular filtration rate (GFR), where appropriate. RESULTS: Blood pool activity was positively associated with maximum artery wall SUV (ß = 5.61, P<0.0001) as well as mean liver (ß = 6.23, P<0.0001) and spleen SUV (ß = 5.20, P<0.0001). Artery wall activity divided by blood activity (TBRBlood) or subtraction of blood activity did not remove the statistically significant relationship to blood activity. Blood pool activity was not related to TBRliver and TBRspleen (ß = -0.36, P = NS and ß = -0.58, P = NS, respectively). CONCLUSIONS: In otherwise healthy individuals treated for hyperlipidemia, blood FDG activity is associated with artery wall activity. However, variation in blood activity may mask artery wall signal reflective of inflammation, which requires normalization. Blood-based TBR and subtraction do not sufficiently adjust for blood activity. Warranting further investigation, background reference tissues with cellular uptake such as the liver and spleen may better adjust for variation in blood activity to improve assessment of vascular activity.

Feature tracking CMR reveals abnormal strain in preclinical arrhythmogenic right ventricular dysplasia/ cardiomyopathy: a multisoftware feasibility and clinical implementation study.

BACKGROUND: Regional right ventricular (RV) dysfunction is the hallmark of Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy (ARVD/C), but is currently only qualitatively evaluated in the clinical setting. Feature Tracking Cardiovascular Magnetic Resonance (FT-CMR) is a novel quantitative method that uses cine CMR to calculate strain values. However, most prior FT-CMR studies in ARVD/C have focused on global RV strain using different software methods, complicating implementation of FT-CMR in clinical practice. We aimed to assess the clinical value of global and regional strain using FT-CMR in ARVD/C and to determine differences between commercially available FT-CMR software packages. METHODS: We analyzed cine CMR images of 110 subjects (39 overt ARVD/C [mutation+/phenotype+], 40 preclinical ARVD/C [mutation+/phenotype-] and 31 control) for global and regional (subtricuspid, anterior, apical) RV strain in the horizontal longitudinal axis using four FT-CMR software methods (Multimodality Tissue Tracking, TomTec, Medis and Circle Cardiovascular Imaging). Intersoftware agreement was assessed using Bland Altman plots. RESULTS: For global strain, all methods showed reduced strain in overt ARVD/C patients compared to control subjects (p < 0.041), whereas none distinguished preclinical from control subjects (p > 0.275). For regional strain, overt ARVD/C patients showed reduced strain compared to control subjects in all segments which reached statistical significance in the subtricuspid region for all software methods (p < 0.037), in the anterior wall for two methods (p < 0.005) and in the apex for one method (p = 0.012). Preclinical subjects showed abnormal subtricuspid strain compared to control subjects using one of the software methods (p = 0.009). Agreement between software methods for absolute strain values was low (Intraclass Correlation Coefficient = 0.373). CONCLUSIONS: Despite large intersoftware variability of FT-CMR derived strain values, all four software methods distinguished overt ARVD/C patients from control subjects by both global and subtricuspid strain values. In the subtricuspid region, one software package distinguished preclinical from control subjects, suggesting the potential to identify early ARVD/C prior to overt disease expression.

Automatic high-resolution infarct detection using volumetric multiphase dual-energy CT.

OBJECTIVES: Late contrast enhancement CT (LCE-CT) visualizes the presence of myocardial infarcts. Differentiation of the contrast-enhanced infarct from blood pool is challenging. We developed a novel method using data from first pass CT angiography (CTA) imaging to enable automatic infarct detection. MATERIALS AND METHODS: A canine model of myocardial infarction was produced in 11 animals. Two months later, first pass CTA (90 kVp) and LCE-CT (dual energy 90 kVp/150 kVp tin filtered) were performed. Late gadolinium enhancement MRI was used as reference standard. The CTA and LCE-CT were co-registered using a fully automatic non-rigid method based on curved B-splines. The method allowed for limited elastic deformation and the considerable differences in attenuation between first-pass and delayed image. The blood pool was easily identified on the CTA image by high attenuation. Because CTA and LCE-CT were registered, the blood pool segmentation can be directly transferred to the LCE-CT - thereby solving the key problem of infarct/blood pool differentiation. The remaining segmentation of infarcted vs. noninfarcted myocardium was performed using a threshold. Automatic and MRI-guided expert segmentations of LCE-CT infarcts were compared to each other on volume and area basis (intraclass correlation coefficient, ICC) and on voxel basis (dice similarity coefficient, DSC between automatic and expert CT segmentation). CT infarct volumes were compared with the reference standard MRI. RESULTS: The infarcts were mainly subendocardial (81%) and relatively small (median MRI infarct mass 7.4 g). The automatic segmentation showed excellent agreement with expert segmentation on volume and area measurements (ICC = 0.96 and 0.87, respectively). DSC showed moderately good agreement (DSC = 0.47). Compared to MRI there was modest agreement (ICC = 0.62) and excellent correlation (R = 0.9). Manual interaction was less than 1 min per exam. CONCLUSION: We propose an automatic method for infarct segmentation on LCE-CT using multiphase CT information, which showed excellent agreement with expert readers and favorable correlation with MRI.

Right ventricular strain by MR quantitatively identifies regional dysfunction in patients with arrhythmogenic right ventricular cardiomyopathy.

BACKGROUND: Analysis of regional wall motion of the right ventricle (RV) is primarily qualitative with large interobserver variation in clinical practice. Thus, the purpose of this study was to use feature tracking to analyze regional wall motion abnormalities in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). METHODS: We enrolled 110 subjects (39 overt ARVC [mutation+/phenotype+] (35.5%), 40 preclinical ARVC [mutation+/phenotype-] (36.3%), and 31 control subjects (28.2%)). Cine steady state free precession cardiac MR was performed with temporal resolution ≤40 ms in the horizontal long axis (HLA), axial, and short axis directions. Regional strain was analyzed using feature tracking software and reproducibility was assessed by means of intraclass correlation coefficient. Dunnett's test was used in univariate analysis for comparisons to control subjects; cumulative odds logistic regression was used for minimally and fully adjusted multivariate models. RESULTS: Strain was significantly impaired in overt ARVC compared with control subjects both globally (P < 0.01) and regionally (all segments of HLA view, P < 0.01). In the HLA view, regional reproducibility was excellent within (intraclass correlation coefficient [ICC] = 0.81) and moderate between (ICC = 0.62) observers. Using a threshold of -31% subtricuspid strain in the HLA view, the sensitivity and specificity for overt ARVC were 75.0% and 78.2%, respectively. In multivariable analysis involving all three groups, subtricuspid strain less than -31% (beta = 1.38; P = 0.014) and RV end diastolic volume index (beta = 0.06; P = 0.001) were significant predictors of disease presence. CONCLUSION: RV strain can be reproducibly assessed with MR feature tracking, and regional strain is abnormal in overt ARVC compared with control subjects.

Regional Strain Analysis with Multidetector CT in a Swine Cardiomyopathy Model: Relationship to Cardiac MR Tagging and Myocardial Fibrosis.

PURPOSE: To investigate the use of cine multidetector computed tomography (CT) to detect changes in myocardial function in a swine cardiomyopathy model. MATERIALS AND METHODS: All animal protocols were in accordance with the Principles for the Utilization and Care of Vertebrate Animals Used in Testing Research and Training and approved by the University of Missouri Animal Care and Use Committee. Strain analysis of cine multidetector CT images of the left ventricle was optimized and analyzed with feature-tracking software. The standard of reference for strain was harmonic phase analysis of tagged cardiac magnetic resonance (MR) images at 3.0 T. An animal model of cardiomyopathy was imaged with both cardiac MR and 320-section multidetector CT at a temporal resolution of less than 50 msec. Three groups were evaluated: control group (n = 5), aortic-banded myocardial hypertrophy group (n = 5), and aortic-banded and cyclosporine A- treated cardiomyopathy group (n = 5). Histologic samples of the myocardium were obtained for comparison with strain results. Dunnett test was used for comparisons of the concentric remodeling group and eccentric remodeling group against the control group. RESULTS: Collagen volume fraction ranged from 10.9% to 14.2%; lower collagen fraction values were seen in the control group than in the cardiomyopathy groups (P < .05). Ejection fraction and conventional metrics showed no significant differences between control and cardiomyopathy groups. Radial strain for both cardiac MR and multidetector CT was abnormal in both concentric (cardiac MR 25.1% ± 4.2; multidetector CT 28.4% ± 2.8) and eccentric (cardiac MR 23.2% ± 2.0; multidetector CT 24.4% ± 2.1) remodeling groups relative to control group (cardiac MR 18.9% ± 1.9, multidetector CT 22.0% ± 1.7, P < .05, all comparisons). Strain values for multidetector CT versus cardiac MR showed better agreement in the radial direction than in the circumferential direction (r = 0.55, P = .03 vs r = 0.40, P = .13, respectively). CONCLUSION: Multidetector CT strain analysis has potential to identify regional wall-motion abnormalities in cardiomyopathy that is not otherwise detected using conventional metrics of myocardial function.