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.

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.

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 (D_C ) and ground truth diameter (D_gt ), (D_C  = 0.951 × D_gt  + 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.

Purpose

To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RS_CT) 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 RS_CT across one heartbeat in each segment. The data set was split evenly into training and validation groups. During training, interchangeability of RS_CT thresholding with experts to detect WMA was assessed using the individual equivalence index (γ), and an optimal threshold of the peak RS_CT (RS_CT*) that achieved maximum agreement was identified. RS_CT* 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, RS_CT* 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 RS_CT* 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

RS_CT 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.

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.

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 (RS_CT) values obtained with CT SQUEEZ in the normal human left ventricle (LV) from standard clinical 4D coronary CTA exams.

Methods

RS_CT was measured over the heart cycle in 25 humans with normal LV function using cine CT from three vendors. Mean and standard deviation of RS_CT values were computed in 16 AHA LV segments to estimate the range of values expected in the normal LV.

Results

Curves describing RS_CT vs. time were consistent between subjects. There was a slight gradient of decreasing minimum RS_CT value (increased shortening) from the base to the apex of the heart. Mean RS_CT values at end-systole were: base = -32% ± 1%, mid = -33% ± 1%, apex = -36% ± 1%. The standard deviation of the minimum systolic RS_CT in each segment over all subjects was 5%. The average time to reach maximum shortening was 34% of the RR interval.

Conclusions

Regional strain (RS_CT) can be rapidly obtained from standard gated coronary CCTA protocols using 4DCT SQUEEZ processing. We estimate that 95% of normal LV end-systolic RS_CT values will fall between -23% and -43%; therefore, we hypothesize that an RS_CT value higher than -23% will indicate a hypokinetic segment in the human heart.

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.

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.

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.