Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach.

Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the inter-slice misalignment due to cardiac motion and data sparsity from large gaps between SAX slices. Therefore, we aim to propose an end-to-end deep learning (DL) model to address these two challenges simultaneously, employing specific model components for each challenge. The objective is to reconstruct a high-resolution 3D volume of the heart (VHR) from acquired CMR SAX slices (VLR). We define the transformation from VLR to VHR as a sequential process of motion correction and super-resolution. Accordingly, our DL model incorporates two distinct components. The first component conducts motion correction by predicting displacement vectors to re-position each SAX slice accurately. The second component takes the motion-corrected SAX slices from the first component and performs the super-resolution to fill the data gaps. These two components operate in a sequential way, and the entire model is trained end-to-end. Our model significantly reduced inter-slice misalignment from originally 3.33±0.74 mm to 1.36±0.63 mm and generated accurate high resolution 3D volumes with Dice of 0.974±0.010 for left ventricle (LV) and 0.938±0.017 for myocardium in a simulation dataset. When compared to the LAX contours in a real-world dataset, our model achieved Dice of 0.945±0.023 for LV and 0.786±0.060 for myocardium. In both datasets, our model with specific components for motion correction and super-resolution significantly enhance the performance compared to the model without such design considerations. The codes for our model are available at https://github.com/zhennongchen/CMR_MC_SR_End2End.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

BACKGROUND: Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. PURPOSE: To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. METHODS: Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. RESULTS: The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45 mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48 mm or degree). The resulting motion-corrected image, I D L , P A R ${I}_{DL,\ PAR}$ , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 ± $ \pm $ 33HU to 37 ± $ \pm $ 9HU, structural similarity index (SSIM) increases from 0.60 ± $ \pm $ 0.06 to 0.98 ± $ \pm $ 0.01) and the phantom study (image MAE drops from 117 ± $ \pm $ 17HU to 42 ± $ \pm $ 19HU, SSIM increases from 0.83 ± $ \pm $ 0.04 to 0.98 ± $ \pm $ 0.02). CONCLUSIONS: We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

Regional Strain of Right Ventricle From Computed Tomography Improves Risk Stratification of Right Ventricle Failure.

Patients who undergo implantation of a left ventricular assist device (LVAD) are at a high risk for right ventricular failure (RVF), presumably due to poor right ventricular (RV) function before surgery. Cine computerized tomography (cineCT) can be used to evaluate RV size, function, and endocardial strain. However, CT-based strain measures in patients undergoing workup for LVAD implantation have not been evaluated. We quantified RV strain in the free wall (FW) and septal wall (SW) in patients with end-stage heart failure using cineCT. Compared to controls, both FW and SW strains were significantly impaired in heart failure patients. The difference between FW and SW strains predicted RV failure after LVAD implantation (area-under-the curve [AUC] = 0.82). Cine CT strain can be combined with RV volumetry to risk-stratify patients. In our study, patients with preserved RV volumes and poor strain had a higher rate of RV failure (57%), than those with preserved volume and preserved strain (0%). This suggests that CT could improve risk stratification of patients receiving LVADs and that strain metrics were particularly useful in risk-stratifying patients with preserved RV volumes.

Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality.

Purpose: To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RSCT) can be used as a decision classifier to detect the presence of left ventricular (LV) wall motion abnormalities (WMAs). Materials and Methods: One hundred electrocardiographically gated cardiac 4D CT studies (mean age, 59 years ± 14 [SD]; 61 male patients) conducted between April 2018 and December 2020 were retrospectively evaluated. Three experts labeled LV wall motion in each of the 16 American Heart Association (AHA) segments as normal or abnormal; they also measured peak RSCT across one heartbeat in each segment. The data set was split evenly into training and validation groups. During training, interchangeability of RSCT thresholding with experts to detect WMA was assessed using the individual equivalence index (γ), and an optimal threshold of the peak RSCT (RSCT*) that achieved maximum agreement was identified. RSCT* was then validated using the validation group, and the effect of AHA segment-specific thresholds was evaluated. Agreement was assessed using κ statistics. Results: The optimal threshold, RSCT* of -0.19, when applied to all AHA segments, led to high agreement (agreement rate = 92.17%, κ = 0.82) and interchangeability with experts (γ = -2.58%). The same RSCT* also achieved high agreement in the validation group (agreement rate = 90.29%, κ = 0.76, γ = -0.38%). The use of AHA segment-specific thresholds (range: 0.16 to -0.23 across AHA segments) slightly improved agreement (1.79% increase). Conclusion: RSCT thresholding was interchangeable with expert visual analysis in detecting segmental WMA from 4D CT and may be used as an objective decision classifier.Keywords: CT, Left Ventricle, Regional Endocardial Shortening, Wall Motion Abnormality Supplemental material is available for this article. © RSNA, 2023.

DiFiR-CT: Distance field representation to resolve motion artifacts in computed tomography.

BACKGROUND: Motion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields. PURPOSE: To develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of the motion. METHODS: We describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation. RESULTS: We illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html. CONCLUSIONS: Projection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.

Pressure Volume Loop Analysis of the Right Ventricle in Heart Failure With Computed Tomography.

Right ventricular (RV) function is an important marker of mortality in chronic left-sided heart failure. Right ventricular function is particularly important for patients receiving left ventricular assist devices as it is a predictor of postoperative RV failure. RV stroke work index (RVSWI), the area enclosed by a pressure-volume (PV) loop, is prognostic of RV failure. However, clinical RVSWI approximates RVSWI as the product of thermodilution-derived stroke volume and the pulmonary pressure gradient. This ignores the energetic contribution of regurgitant flow and does not allow for advanced energetic measures, such as pressure-volume area and efficiency. Estimating RVSWI from forward flow may underestimate the underlying RV function. We created single-beat PV loops by combining data from cine computed tomography (CT) and right heart catheterization in 44 heart failure patients, tested the approximations made by clinical RVSWI and found it to underestimate PV loop RVSWI, primarily due to regurgitant flow in tricuspid regurgitation. The ability of RVSWI to predict post-operative RV failure improved when the single-beat approach was used. Further, RV pressure-volume area and efficiency measures were obtained and show broad agreement with other functional measures. Future work is needed to investigate the utility of these PV metrics in a clinical setting.

Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning.

Background: The presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework. Methods: Three hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing. Results: Volume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment. Conclusions: Dynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.

Prediction of Cardiac Resynchronization Therapy Response Using a Lead Placement Score Derived From 4-Dimensional Computed Tomography.

BACKGROUND: Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure; however, 30% of patients do not respond to the treatment. We sought to derive patient-specific left ventricle maps of lead placement scores (LPS) that highlight target pacing lead sites for achieving a higher probability of CRT response. METHODS: Eighty-two subjects recruited for the ImagingCRT trial (Empiric Versus Imaging Guided Left Ventricular Lead Placement in Cardiac Resynchronization Therapy) were retrospectively analyzed. All 82 subjects had 2 contrast-enhanced full cardiac cycle 4-dimensional computed tomography scans: a baseline and a 6-month follow-up scan. CRT response was defined as a reduction in computed tomography-derived end-systolic volume ≥15%. Eight left ventricle features derived from the baseline scans were used to train a support vector machine via a bagging approach. An LPS map over the left ventricle was created for each subject as a linear combination of the support vector machine feature weights and the subject's own feature vector. Performance for distinguishing responders was performed on the original 82 subjects. RESULTS: Fifty-two (63%) subjects were responders. Subjects with an LPS≤Q1 (lower-quartile) had a posttest probability of responding of 14% (3/21), while subjects with an LPS≥ Q3 (upper-quartile) had a posttest probability of responding of 90% (19/21). Subjects with Q1

Regional left ventricular endocardial strains estimated from low-dose 4DCT: Comparison with cardiac magnetic resonance feature tracking.

BACKGROUND: Estimates of regional left ventricular (LV) strains provide additional information to global function parameters such as ejection fraction (EF) and global longitudinal strain (GLS) and are more sensitive in detecting abnormal regional cardiac function. The accurate and reproducible assessment of regional cardiac function has implications in the management of various cardiac diseases such as heart failure, myocardial ischemia, and dyssynchrony. PURPOSE: To develop a method that yields highly reproducible, high-resolution estimates of regional endocardial strains from 4DCT images. METHODS: A method for estimating regional LV endocardial circumferential ( ε c c ) $( {{\epsilon }_{cc}} )$ and longitudinal ( ε l l ${\epsilon }_{ll}$ ) strains from 4DCT was developed. Point clouds representing the LV endocardial surface were extracted for each time frame of the cardiac cycle from 4DCT images. 3D deformation fields across the cardiac cycle were obtained by registering the end diastolic point cloud to each subsequent point cloud in time across the cardiac cycle using a 3D point-set registration technique. From these deformation fields, ε c c and ε l l ${\epsilon }_{cc}\ {\rm{and\ }}{\epsilon }_{ll}$ were estimated over the entire LV endocardial surface by fitting an affine transformation with maximum likelihood estimation. The 4DCT-derived strains were compared with strains estimated in the same subjects by cardiac magnetic resonance (CMR); twenty-four subjects had CMR scans followed by 4DCT scans acquired within a few hours. Regional LV circumferential and longitudinal strains were estimated from the CMR images using a commercially available feature tracking software (cvi42). Global circumferential strain (GCS) and global longitudinal strain (GLS) were calculated as the mean of the regional strains across the entire LV for both modalities. Pearson correlation coefficients and Bland-Altman analyses were used for comparisons. Intraclass correlation coefficients (ICC) were used to assess the inter- and intraobserver reproducibility of the 4DCT-derived strains. RESULTS: The 4DCT-derived regional strains correlated well with the CMR-derived regional strains ( ε c c ${\epsilon }_{cc}$ : r = 0.76, p < 0.001; ε l l ${\epsilon }_{ll}$ : r = 0.64, p < 0.001). A very strong correlation was found between 4DCT-derived GCS and 4DCT-derived EF (r = -0.96; p < 0.001). The 4DCT-derived strains were also highly reproducible, with very low inter- and intraobserver variability (intraclass correlation coefficients in the range of [0.92, 0.99]). CONCLUSIONS: We have developed a novel method to estimate high-resolution regional LV endocardial circumferential and longitudinal strains from 4DCT images. Except for the definition of the mitral valve and LV outflow tract planes, the method is completely user independent, thus yielding highly reproducible estimates of endocardial strain. The 4DCT-derived strains correlated well with those estimated using a commercial CMR feature tracking software. The promising results reported in this study highlight the potential utility of 4DCT in the precise assessment of regional cardiac function for the management of cardiac disease.

Deep learning automates detection of wall motion abnormalities via measurement of longitudinal strain from ECG-gated CT images.

Introduction: 4D cardiac CT (cineCT) is increasingly used to evaluate cardiac dynamics. While echocardiography and CMR have demonstrated the utility of longitudinal strain (LS) measures, measuring LS from cineCT currently requires reformatting the 4D dataset into long-axis imaging planes and delineating the endocardial boundary across time. In this work, we demonstrate the ability of a recently published deep learning framework to automatically and accurately measure LS for detection of wall motion abnormalities (WMA). Methods: One hundred clinical cineCT studies were evaluated by three experienced cardiac CT readers to identify whether each AHA segment had a WMA. Fifty cases were used for method development and an independent group of 50 were used for testing. A previously developed convolutional neural network was used to automatically segment the LV bloodpool and to define the 2, 3, and 4 CH long-axis imaging planes. LS was measured as the perimeter of the bloodpool for each long-axis plane. Two smoothing approaches were developed to avoid artifacts due to papillary muscle insertion and texture of the endocardial surface. The impact of the smoothing was evaluated by comparison of LS estimates to LV ejection fraction and the fractional area change of the corresponding view. Results: The automated, DL approach successfully analyzed 48/50 patients in the training cohort and 47/50 in the testing cohort. The optimal LS cutoff for identification of WMA was -21.8, -15.4, and -16.6% for the 2-, 3-, and 4-CH views in the training cohort. This led to correct labeling of 85, 85, and 83% of 2-, 3-, and 4-CH views, respectively, in the testing cohort. Per-study accuracy was 83% (84% sensitivity and 82% specificity). Smoothing significantly improved agreement between LS and fractional area change (R 2: 2 CH = 0.38 vs. 0.89 vs. 0.92). Conclusion: Automated LV blood pool segmentation and long-axis plane delineation via deep learning enables automatic LS assessment. LS values accurately identify regional wall motion abnormalities and may be used to complement standard visual assessments.

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning.

AIMS: To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging. METHODS AND RESULTS: One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert. A DL model was trained to predict bloodpool segmentations and imaging planes. Deep learning bloodpool segmentations showed close agreement with manual LV [median Dice: 0.91, Hausdorff distance (HD): 6.18 mm] and LA (Dice: 0.93, HD: 7.35 mm) segmentations and a strong correlation with manual ejection fraction (Pearson r: 0.95 LV, 0.92 LA). Predicted planes had low median location (6.96 mm) and angular orientation (7.96 ° ) errors which were comparable to inter-reader differences (P > 0.71). 84-97% of DL-prescribed LAX planes correctly intersected American Heart Association segments, which was comparable (P > 0.05) to manual slicing. In a test cohort of 144 patients, we evaluated the ability of the DL approach to provide diagnostic imaging planes. Visual scoring by two blinded experts determined ≥94% of DL-predicted planes to be diagnostically adequate. Further, DL-enabled visualization of LV wall motion abnormalities due to CAD and provided reproducible planes upon repeat imaging. CONCLUSION: A volumetric, DL approach provides multiple chamber segmentations and can re-slice the imaging volume along standardized cardiac imaging planes for reproducible wall motion abnormality and functional assessment.

Precise measurement of coronary stenosis diameter with CCTA using CT number calibration.

PURPOSE: Coronary x-ray computed tomography angiography (CCTA) continues to develop as a noninvasive method for the assessment of coronary vessel geometry and the identification of physiologically significant lesions. The uncertainty of quantitative lesion diameter measurement due to limited spatial resolution and vessel motion reduces the accuracy of CCTA diagnoses. In this paper, we introduce a new technique called computed tomography (CT)-number-Calibrated Diameter to improve the accuracy of the vessel and stenosis diameter measurements with CCTA. METHODS: A calibration phantom containing cylindrical holes (diameters spanning from 0.8 mm through 4.0 mm) capturing the range of diameters found in human coronary vessels was three-dimensional printed. We also printed a human stenosis phantom with 17 tubular channels having the geometry of lesions derived from patient data. We acquired CT scans of the two phantoms with seven different imaging protocols. Calibration curves relating vessel intraluminal maximum voxel value (maximum CT number of a voxel, described in Hounsfield Units, HU) to true diameter, and full-width-at-half maximum (FWHM) to true diameter were constructed for each CCTA protocol. In addition, we acquired scans with a small constant motion (15 mm/s) and used a motion correction reconstruction (Snapshot Freeze) algorithm to correct motion artifacts. We applied our technique to measure the lesion diameter in the 17 lesions in the stenosis phantom and compared the performance of CT-number-Calibrated Diameter to the ground truth diameter and a FWHM estimate. RESULTS: In all cases, vessel intraluminal maximum voxel value vs diameter was found to have a simple functional form based on the two-dimensional point spread function yielding a constant maximum voxel value region above a cutoff diameter, and a decreasing maximum voxel value vs decreasing diameter below a cutoff diameter. After normalization, focal spot size and reconstruction kernel were the principal determinants of cutoff diameter and the rate of maximum voxel value reduction vs decreasing diameter. The small constant motion had a significant effect on the CT number calibration; however, the motion-correction algorithm returned the maximum voxel value vs diameter curve to that of stationary vessels. The CT number Calibration technique showed better performance than FWHM estimation of diameter, yielding a high accuracy in the tested range (0.8 mm through 2.5 mm). We found a strong linear correlation between the smallest diameter in each of 17 lesions measured by CT-number-Calibrated Diameter (DC ) and ground truth diameter (Dgt ), (DC  = 0.951 × Dgt  + 0.023 mm, r = 0.998 with a slope very close to 1.0 and intercept very close to 0 mm. CONCLUSIONS: Computed tomography-number-Calibrated Diameter is an effective method to enhance the accuracy of the estimate of small vessel diameters and degree of coronary stenosis in CCTA.

Regional myocardial strain measurements from 4DCT in patients with normal LV function.

BACKGROUND: CT SQUEEZ is a new automated technique to evaluate regional endocardial strain by tracking features on the endocardium from 4D cine CT data. The objective of this study was to measure the range of endocardial regional strain (RSCT) values obtained with CT SQUEEZ in the normal human left ventricle (LV) from standard clinical 4D coronary CTA exams. METHODS: RSCT was measured over the heart cycle in 25 humans with normal LV function using cine CT from three vendors. Mean and standard deviation of RSCT values were computed in 16 AHA LV segments to estimate the range of values expected in the normal LV. RESULTS: Curves describing RSCT vs. time were consistent between subjects. There was a slight gradient of decreasing minimum RSCT value (increased shortening) from the base to the apex of the heart. Mean RSCT values at end-systole were: base = -32% ± 1%, mid = -33% ± 1%, apex = -36% ± 1%. The standard deviation of the minimum systolic RSCT in each segment over all subjects was 5%. The average time to reach maximum shortening was 34% of the RR interval. CONCLUSIONS: Regional strain (RSCT) can be rapidly obtained from standard gated coronary CCTA protocols using 4DCT SQUEEZ processing. We estimate that 95% of normal LV end-systolic RSCT values will fall between -23% and -43%; therefore, we hypothesize that an RSCT value higher than -23% will indicate a hypokinetic segment in the human heart.

A novel method for evaluating regional RV function in the adult congenital heart with low-dose CT and SQUEEZ processing.

BACKGROUND: Measuring local RV function in adult congenital heart disease (ACHD) with echocardiography or MRI is challenging because of the complex geometry and existing pacing devices. Visual assessment of ventricular function via low-dose cardiac CT has been recently performed. This pilot study assessed whether low-dose 4D cine CT combined with automatic measurement of regional shortening could quantify right-ventricular function in ACHD patients. METHODS: Seven patients with Tetralogy of Fallot either contraindicated for MRI or assessed for coronary artery disease and seven non-congenital patients were imaged with ECG-gated cardiac CT utilizing a 320-detector row scanner. Right ventricular global function and regional shortening were quantified. RESULTS: Non-congenital patients were imaged with 2.9±2.1mSv and 395±359 HU blood-myocardium contrast. The ACHD patients were imaged with 2.1±1.3mSv and 726±296 HU contrast. Right ventricles of the ACHD patients had higher end-diastolic volume (297±107mL vs 123±34mL, p=0.001), lower ejection fraction (32.0±4.9% vs 45.0±6.0%, p=0.001), and higher dyskinetic fraction (10.9±3.7% vs 2.6±2.8%, p<0.001) relative to the non-congenital controls. CONCLUSIONS: In this initial pilot study, right ventricular global and regional systolic function were measured using low-dose cine CT with SQUEEZ quantification in non-congenital patients as well as ACHD patients with Tetralogy of Fallot. Unique regional features of RV dyskinesia were identified in the ACHD patients which could yield a more precise quantification of RV function.