Correlation of CT-based regional cardiac function (SQUEEZ) with myocardial strain calculated from tagged MRI: an experimental study

Successful Cardiac Resynchronization Therapy Reduces Negative Septal Work in Patient-Specific Models of Dyssynchronous Heart Failure

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

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.

Gender Medicine in Clinical Radiology Practice

Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

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.

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.

Four-dimensional computed tomography of the left ventricle, Part I: Motion artifact reduction

PURPOSE

Standard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series.

METHODS

We use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images. After identifying the key locations where motion artifacts exist in the uncorrected images, paired subvolumes within the PAR images are analyzed with a modified cross-correlation function in order to estimate 3D velocity and acceleration vectors at these locations. A subsequent motion compensation process (also based on PAR images) includes the creation of a dense motion field, followed by a backproject-and-warp style compensation. The algorithm was tested on a 3D printed phantom, which represents the left ventricle (LV) and on challenging clinical cases corrupted by severe artifacts.

RESULTS

The results from our preliminary phantom test as well as from clinical cardiac scans show crisp endocardial edges and resolved double-wall artifacts. When viewed as a temporal series, the corrected images exhibit a much smoother motion of the LV endocardial boundary as compared to the uncorrected images. In addition, quantitative results from our phantom studies show that ResyncCT processing reduces endocardial surface distance errors from 0.9 ± 0.8 to 0.2 ± 0.1 mm.

CONCLUSIONS

The ResyncCT algorithm was shown to be effective in reducing motion artifacts and restoring accurate wall positions. Some perspectives on the use of conjugate-PAR images and on techniques for CT motion artifact reduction more generally are also given.

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.

Novel 4DCT Method to Measure Regional Left Ventricular Endocardial Shortening Before and After Transcatheter Mitral Valve Implantation

BACKGROUND

Regional left ventricular (LV) mechanics in mitral regurgitation (MR) patients, and local changes in function after transcatheter mitral valve implantation (TMVI) have yet to be evaluated. Herein, we introduce a method for creating high resolution maps of endocardial function from 4DCT images, leading to detailed characterization of changes in local LV function. These changes are particularly interesting when evaluating the effect of the Tendyne™ TMVI device in the region of the epicardial pad.

METHODS

Regional endocardial shortening from CT (RSCT) was evaluated in Tendyne (Abbott Medical) TMVI patients with 4DCT exams pre- and post-implantation. Regional function was evaluated in 90 LV segments (5 longitudinal × 18 circumferential). LV volumes and ejection fraction (EF) were also computed. A reproducibility study was performed in a subset of patients to determine the precision of RSCT measurements in this population.

RESULTS

Baseline and local changes in RSCT post TMVI were highly variable and extremely spatially heterogeneous. Both inter- and intra-observer variability were low and demonstrated the high precision of RSCT for evaluating regional LV function.

CONCLUSION

RSCT is a reproducible metric which can be evaluated in patients with highly abnormal regional LV function and geometry. After TMVI, significant spatially heterogeneous changes in RSCT were observed in all subjects; therefore, it is unlikely that the functional state of TMVI patients can be fully described by changes in LV volume or EF. Measurement of RSCT provides precise characterization of the spatially heterogeneous effects of MR and TMVI on LV function and remodeling.

Optimisation of Left Atrial Feature Tracking Using Retrospective Gated Computed Tomography Images

Retrospective gated cardiac computed tomography (CCT) images can provide high contrast and resolution images of the heart throughout the cardiac cycle. Feature tracking in retrospective CCT images using the temporal sparse free-form deformations (TSFFDs) registration method has previously been optimised for the left ventricle (LV). However, there is limited work on optimising nonrigid registration methods for feature tracking in the left atria (LA). This paper systematically optimises the sparsity weight (SW) and bending energy (BE) as two hyperparameters of the TSFFD method to track the LA endocardium from end-diastole (ED) to end-systole (ES) using 10-frame retrospective gated CCT images. The effect of two different control point (CP) grid resolutions was also investigated. TSFFD optimisation was achieved using the average surface distance (ASD), directed Hausdorff distance (DHD) and Dice score between the registered and ground truth surface meshes and segmentations at ES. For baseline comparison, the configuration optimised for LV feature tracking gave errors across the cohort of 0.826 ± 0.172mm ASD, 5.882 ± 1.524mm DHD, and 0.912 ± 0.033 Dice score. Optimising the SW and BE hyperparameters improved the TSFFD performance in tracking LA features, with case specific optimisations giving errors across the cohort of 0.750 ± 0.144mm ASD, 5.096 ± 1.246mm DHD, and 0.919 ± 0.029 Dice score. Increasing the CP resolution and optimising the SW and BE further improved tracking performance, with case specific optimisation errors of 0.372 ± 0.051mm ASD, 2.739 ± 0.843mm DHD and 0.949 ± 0.018 Dice score across the cohort. We therefore show LA feature tracking using TSFFDs is improved through a chamber-specific optimised configuration.

Hyperparameter optimisation and validation of registration algorithms for measuring regional ventricular deformation using retrospective gated computed tomography images

Recent dose reduction techniques have made retrospective computed tomography (CT) scans more applicable and extracting myocardial function from cardiac computed tomography (CCT) images feasible. However, hyperparameters of generic image intensity-based registration techniques, which are used for tracking motion, have not been systematically optimised for this modality. There is limited work on their validation for measuring regional strains from retrospective gated CCT images and open-source software for motion analysis is not widely available. We calculated strain using our open-source platform by applying an image registration warping field to a triangulated mesh of the left ventricular endocardium. We optimised hyperparameters of two registration methods to track the wall motion. Both methods required a single semi-automated segmentation of the left ventricle cavity at end-diastolic phase. The motion was characterised by the circumferential and longitudinal strains, as well as local area change throughout the cardiac cycle from a dataset of 24 patients. The derived motion was validated against manually annotated anatomical landmarks and the calculation of strains were verified using idealised problems. Optimising hyperparameters of registration methods allowed tracking of anatomical measurements with a mean error of 6.63% across frames, landmarks, and patients, comparable to an intra-observer error of 7.98%. Both registration methods differentiated between normal and dyssynchronous contraction patterns based on circumferential strain (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_1=0.0065$$\end{document}p1=0.0065, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p_2=0.0011$$\end{document}p2=0.0011). To test whether a typical 10 temporal frames sampling of retrospective gated CCT datasets affects measuring cardiac mechanics, we compared motion tracking results from 10 and 20 frames datasets and found a maximum error of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$8.51\pm 0.8\%$$\end{document}8.51±0.8%. Our findings show that intensity-based registration techniques with optimal hyperparameters are able to accurately measure regional strains from CCT in a very short amount of time. Furthermore, sufficient sensitivity can be achieved to identify heart failure patients and left ventricle mechanics can be quantified with 10 reconstructed temporal frames. Our open-source platform will support increased use of CCT for quantifying cardiac mechanics.

Low-Radiation-Dose Stress Myocardial Perfusion Measurement Using First-Pass Analysis Dynamic Computed Tomography: A Preliminary Investigation in a Swine Model

OBJECTIVES

The aim of this study was to assess the feasibility of a prospective first-pass analysis (FPA) dynamic computed tomography (CT) perfusion technique for accurate low-radiation-dose global stress perfusion measurement.

MATERIALS AND METHODS

The prospective FPA technique was evaluated in 10 swine (42 ± 12 kg) by direct comparison to a previously validated retrospective FPA technique. Of the 10 swine, 3 had intermediate stenoses with fractional flow reserve severities of 0.70 to 0.90. In each swine, contrast and saline were injected peripherally followed by dynamic volume scanning with a 320-slice CT scanner. Specifically, for the reference standard retrospective FPA technique, volume scans were acquired continuously at 100 kVp and 200 mA over 15 to 20 seconds, followed by systematic selection of only 2 volume scans for global perfusion measurement. For the prospective FPA technique, only 2 volume scans were acquired at 100 kVp and 50 mA for global perfusion measurement. All prospective global stress perfusion measurements were then compared with the corresponding reference standard retrospective global stress perfusion measurements through regression analysis. The CTDIvol and size-specific dose estimate of the prospective FPA technique were also determined.

RESULTS

All prospective global stress perfusion measurements (PPRO) at 50 mA were in good agreement with the reference standard retrospective global stress perfusion measurements (PREF) at 200 mA (PPRO = 1.07 PREF -0.09, r = 0.94; root-mean-square error = 0.30 mL/min per gram). The CTDIvol and size-specific dose estimate of the prospective FPA technique were 2.3 and 3.7 mGy, respectively.

CONCLUSIONS

Accurate low-radiation-dose global stress perfusion measurement is feasible using a prospective FPA dynamic CT perfusion technique.

Anthropomorphic left ventricular mesh phantom: a framework to investigate the accuracy of SQUEEZ using Coherent Point Drift for the detection of regional wall motion abnormalities

We present an anthropomorphically accurate left ventricular (LV) phantom derived from human computed tomography (CT) data to serve as the ground truth for the optimization and the spatial resolution quantification of a CT-derived regional strain metric (SQUEEZ) for the detection of regional wall motion abnormalities. Displacements were applied to the mesh points of a clinically derived end-diastolic LV mesh to create analytical end-systolic poses with physiologically accurate endocardial strains. Normal function and regional dysfunction of four sizes [1, 2/3, 1/2, and 1/3 American Heart Association (AHA) segments as core diameter], each exhibiting hypokinesia (70% reduction in strain) and subtle hypokinesia (40% reduction in strain), were simulated. Regional shortening (RS CT ) estimates were obtained by registering the end-diastolic mesh to each simulated end-systolic mesh condition using a nonrigid registration algorithm. Ground-truth models of normal function and of hypokinesia were used to identify the optimal parameters in the registration algorithm and to measure the accuracy of detecting regional dysfunction of varying sizes and severities. For normal LV function,RS CT values in all 16 AHA segments were accurate to within ± 5 % . For cases with regional dysfunction, the errors inRS CT around the dysfunctional region increased with decreasing size of dysfunctional tissue.

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 averageF 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 inF 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 inF D . Additionally, a principal component analysis is performed on the temporal profiles ofF 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. TheF D values in the free-wall segments correlated well with radial strain and with endocardial regional shortening measurements.

Automated 4-dimensional regional myocardial strain evaluation using cardiac computed tomography

Evaluation of myocardial regional function is generally performed by visual "eyeballing" which is highly subjective. A robust quantifiable parameter of regional function is required to provide an objective, repeatable and comparable measure of myocardial performance. We aimed to evaluate the clinical utility of novel regional myocardial strain software from cardiac computed tomography (CT) datasets. 93 consecutive patients who had undergone retrospectively gated cardiac CT were evaluated by the software, which utilizes a finite element based tracking algorithm through the cardiac cycle. Circumferential (CS), longitudinal (LS) and radial (RS) strains were calculated for each of 16 myocardial segments and compared to a visual assessment, carried out by an experienced cardiologist on cine movies of standard "echo" views derived from the CT data. A subset of 37 cases was compared to speckle strain by echocardiography. The automated software performed successfully in 93/106 cases, with minimal human interaction. Peak CS, LS and RS all differentiated well between normal, hypokinetic and akinetic segments. Peak strains for akinetic segments were generally post-systolic, peaking at 50 ± 17% of the RR interval compared to 43 ± 9% for normokinetic segments. Using ROC analysis to test the ability to differentiate between normal and abnormal segments, the area under the curve was 0.84 ± 0.01 for CS, 0.80 ± 0.02 for RS and 0.68 ± 0.02 for LS. There was a moderate agreement with speckle strain. Automated 4D regional strain analysis of CT datasets shows a good correspondence to visual analysis and successfully differentiates between normal and abnormal segments, thus providing an objective quantifiable map of myocardial regional function.

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 (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.

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.

Precision of regional wall motion estimates from ultra-low-dose cardiac CT using SQUEEZ

Resting regional wall motion abnormality (RWMA) has significant prognostic value beyond the findings of computed tomography (CT) coronary angiography. Stretch quantification of endocardial engraved zones (SQUEEZ) has been proposed as a measure of regional cardiac function. The purpose of the work reported here was to determine the effect of lowering the radiation dose on the precision of automatic SQUEEZ assessments of RWMA. Chronic myocardial infarction was created by a 2-h occlusion of the left anterior descending coronary artery in 10 swine (heart rates 80-100, ejection fraction 25-57%). CT was performed 5-11 months post infarct using first-pass contrast enhanced segmented cardiac function scans on a 320-detector row scanner at 80 kVp/500 mA. Images were reconstructed at end diastole and end systole with both filtered back projection and using the "standard" adaptive iterative dose reduction (AIDR) algorithm. For each acquisition, 9 lower dose acquisitions were created. End systolic myocardial function maps were calculated using SQUEEZ for all noise levels and contrast-to-noise ratio (CNR) between the left ventricle blood and myocardium was calculated as a measure of image quality. For acquisitions with CNR > 4, SQUEEZ could be estimated with a precision of ± 0.04 (p < 0.001) or 5.7% of its dynamic range. The difference between SQUEEZ values calculated from AIDR and FBP images was not statistically significant. Regional wall motion abnormality can be quantified with good precision from low dose acquisitions, using SQUEEZ, as long as the blood-myocardium CNR stays above 4.

Comparison of Echocardiography, Cardiac Magnetic Resonance, and Computed Tomographic Imaging for the Evaluation of Left Ventricular Myocardial Function: Part 2 (Diastolic and Regional Assessment)

Assessing left ventricular diastolic and regional function is a crucial part of the cardiovascular evaluation. Diastolic function is as important as systolic function for left ventricular performance because it is the determinant of the ability of the left atrium and ventricle to fill at relatively low pressures. Additionally, diastolic function plays an important role in the management and prognosis of patients with symptoms and signs of heart failure. Technical advances in the imaging modalities have allowed a comprehensive noninvasive assessment of global and regional cardiac mechanics and precise estimation of cardiovascular hemodynamics. In this review, we will discuss and compare clinically available techniques and novel approaches using echocardiography, cardiac magnetic resonance, and computed tomography for the assessment of diastolic and regional left ventricular function.

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.

Comprehensive use of cardiac computed tomography to guide left ventricular lead placement in cardiac resynchronization therapy

Background

Optimal lead positioning is an important determinant of cardiac resynchronization therapy (CRT) response.

Objective

The purpose of this study was to evaluate cardiac computed tomography (CT) selection of the optimal epicardial vein for left ventricular (LV) lead placement by targeting regions of late mechanical activation and avoiding myocardial scar.

Methods

Eighteen patients undergoing CRT upgrade with existing pacing systems underwent preimplant electrocardiogram-gated cardiac CT to assess wall thickness, hypoperfusion, late mechanical activation, and regions of myocardial scar by the derivation of the stretch quantifier for endocardial engraved zones (SQUEEZ) algorithm. Cardiac venous anatomy was mapped to individualized American Heart Association (AHA) bull’s-eye plots to identify the optimal venous target and compared with acute hemodynamic response (AHR) in each coronary venous target using an LV pressure wire.

Results

Fifteen data sets were evaluable. CT-SQUEEZ–derived targets produced a similar mean AHR compared with the best achievable AHR (20.4% ± 13.7% vs 24.9% ± 11.1%; P = .36). SQUEEZ-derived guidance produced a positive AHR in 92% of target segments, and pacing in a CT-SQUEEZ target vein produced a greater clinical response rate vs nontarget segments (90% vs 60%).

Conclusion

Preprocedural CT-SQUEEZ–derived target selection may be a valuable tool to predict the optimal venous site for LV lead placement in patients undergoing CRT upgrade.