Direct three-dimensional myocardial strain tensor quantification and tracking using zHARP

Images of myocardial strain can be used to diagnose heart disease, plan and monitor treatment, and to learn about cardiac structure and function. Three-dimensional (3D) strain is typically quantified using many magnetic resonance (MR) images obtained in two or three orthogonal planes. Problems with this approach include long scan times, image misregistration, and through-plane motion. This article presents a novel method for calculating cardiac 3D strain using a stack of two or more images acquired in only one orientation. The zHARP pulse sequence encodes in-plane motion using MR tagging and out-of-plane motion using phase encoding, and has been previously shown to be capable of computing 3D displacement within a single image plane. Here, data from two adjacent image planes are combined to yield a 3D strain tensor at each pixel; stacks of zHARP images can be used to derive stacked arrays of 3D strain tensors without imaging multiple orientations and without numerical interpolation. The performance and accuracy of the method is demonstrated in vitro on a phantom and in vivo in four healthy adult human subjects.     

Towards robust 3D visual tracking for motion compensation in beating heart surgery

In the context of minimally invasive cardiac surgery, active vision-based motion compensation schemes have been proposed for mitigating problems related to physiological motion. However, robust and accurate visual tracking remains a difficult task. The purpose of this paper is to present a robust visual tracking method that estimates the 3D temporal and spatial deformation of the heart surface using stereo endoscopic images. The novelty is the combination of a visual tracking method based on a Thin-Plate Spline (TPS) model for representing the heart surface deformations with a temporal heart motion model based on a time-varying dual Fourier series for overcoming tracking disturbances or failures. The considerable improvements in tracking robustness facing specular reflections and occlusions are demonstrated through experiments using images of in vivo porcine and human beating hearts.     

Regional infarction identification from cardiac CT images: a computer-aided biomechanical approach

Purpose

Regional infarction identification is important for heart disease diagnosis and management, and myocardial deformation has been shown to be effective for this purpose. Although tagged and strain-encoded MR images can provide such measurements, they are uncommon in clinical routine. On the contrary, cardiac CT images are more available with lower costs, but they only provide motion of cardiac boundaries and additional constraints are required to obtain the myocardial strains. The goal of this study is to verify the potential of contrast-enhanced CT images on computer-aided regional infarction identification.

Methods

We propose a biomechanical approach combined with machine learning algorithms. A hyperelastic biomechanical model is used with deformable image registration to estimate 3D myocardial strains from CT images. The regional strains and CT image intensities are input to a classifier for regional infarction identification. Cross-validations on ten canine image sequences with artificially induced infarctions were used to study the performances of using different feature combinations and machine learning algorithms.

Results

Radial strain, circumferential strain, first principal strain, and image intensity were shown to be discriminative features. The highest identification accuracy (\(85\pm 14\) %) was achieved when combining radial strain with image intensity. Random forests gave better results than support vector machines on less discriminative features. Random forests also performed better when all strains were used together.

Conclusion

Although CT images cannot directly measure myocardial deformation, with the use of a biomechanical model, the estimated strains can provide promising identification results especially when combined with CT image intensity.

     

The role of still-frame parametric imaging in magnetic resonance assessment of left ventricular wall motion by non-cardiologists.

BACKGROUND: Cardiac magnetic resonance (MR) images are often reviewed by non-cardiologists who are not trained in the interpretation of regional left ventricular (LV) function. We hypothesized that the use of still-frame parametric MR images of wall motion could aid in the assessment of regional LV function. METHODS: Dynamic, electrocardiogram-gated, steady-state free precession (FIESTA) short-axis images were obtained in 6 to 10 slices in 18 consecutive patients. Each loop was used to automatically generate a still-frame image, in which each pixel is assigned a value equal to the amplitude of cyclic variation in local intensity, resulting in higher intensity in pixels that change between blood and tissue during the cardiac cycle. The dynamic images were reviewed by an expert cardiologist who provided gold standard grades for regional wall motion and by four radiologists. Then the radiologists reviewed and graded the same MR images in combination with parametric images. Grades assigned to each segment in the two sessions were compared with the gold standard. RESULTS: According to expert interpretation, 6 patients had normal wall motion, and 12 had wall motion abnormalities. Parametric images showed a bright band in the area spanned by endocardial motion, with reduced brightness and thickness in areas of hypokinesis. The agreement between the radiologists' grades and the gold standard significantly improved by adding parametric images (from 77% to 81%), which also resulted in reduced interobserver variability (from 52% to 33%). CONCLUSIONS: Still-frame parametric images aid in the assessment of regional wall motion by non-cardiologists who are required to interpret cardiac images.     

Assessment of Regional Wall Motion Abnormalities with Real-Time 3-Dimensional Echocardiography

Accurate characterization of regional wall motion abnormalities requires a thorough evaluation of the entire left ventricle (LV). Although 2-dimensional echocardiography is frequently used for this purpose, the inability of tomographic techniques to record the complete endocardial surface is a limitation. Three-dimensional echocardiography, with real-time volumetric imaging, has the potential to overcome this limitation by capturing the entire volume of the LV and displaying it in a cineloop mode. The purpose of this study was to assess the feasibility of using real-time 3-dimensional (RT3D) echocardiography to detect regional wall motion abnormalities in patients with abnormal LV function and to develop a scheme for the systematic evaluation of wall motion by using the 3-dimensional data set. Twenty-six patients with high-quality 2-dimensional echo images and at least 1 regional wall motion abnormality were examined with RT3D echocardiography. For 2-dimensional echocardiography, wall motion was analyzed with a 16-segment model and graded on a 4-point scale from normal (1) to dyskinetic (4), from which a wall motion score index was calculated. Individual segments were then grouped into regions (anterior, inferoposterior, lateral, and apical) and the number of regional wall motion abnormalities was determined. The RT3D echocardiogram was recorded as a volumetric, pyramid-shaped data set that contained the entire LV. Digital images, consisting of a single cardiac cycle cineloop, were analyzed off-line with a computerized display of the apical projection. Two intersecting orthogonal apical projections were simultaneously displayed in cineloop mode, each independently tilted to optimize orientation and endocardial definition. The 2 planes were then slowly rotated about the major axis to visualize the entire LV endocardium. Wall motion was then graded in 6 equally spaced views, separated by 30 degrees, yielding 36 segments per patient. A higher percentage of segments were visualized with 2-dimensional versus RT3D echocardiography (97% vs 83%, respectively, P < .001). With the use of the 2-dimensional echocardiographic results as the standard, RT3D echocardiography detected 55 (96%) of 57 regional wall motion abnormalities. Analysis of the RT3D echocardiograms resulted in 3 false-negative and 5 false-positive findings. The total number of regional wall motion abnormalities was correctly classified by RT3D echocardiography in 19 (73%) of 26 patients. RT3D echocardiography detected 11 of 13 anterior, 19 of 20 inferoposterior, 9 of 9 lateral, and 15 of 15 apical wall motion abnormalities. An excellent correlation was found between the 2 techniques for assessment of the regional wall motion score index (r = 0.89, P < .001). This initial clinical study demonstrates the feasibility and potential advantages of RT3D echocardiography for the assessment of regional LV function. Compared with 2-dimensional echocardiography, this new method permits recording of the entire LV in a single beat, allowing the extent and location of the regional wall motion abnormalities to be determined. (J Am Soc Echocardiogr 1999;12:7-14.)     

MR to ultrasound registration for image-guided prostate interventions

A deformable registration method is described that enables automatic alignment of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland. The method employs a novel "model-to-image" registration approach in which a deformable model of the gland surface, derived from an MR image, is registered automatically to a TRUS volume by maximising the likelihood of a particular model shape given a voxel-intensity-based feature that represents an estimate of surface normal vectors at the boundary of the gland. The deformation of the surface model is constrained by a patient-specific statistical model of gland deformation, which is trained using data provided by biomechanical simulations. Each simulation predicts the motion of a volumetric finite element mesh due to the random placement of a TRUS probe in the rectum. The use of biomechanical modelling in this way also allows a dense displacement field to be calculated within the prostate, which is then used to non-rigidly warp the MR image to match the TRUS image. Using data acquired from eight patients, and anatomical landmarks to quantify the registration accuracy, the median final RMS target registration error after performing 100 MR-TRUS registrations for each patient was 2.40 mm.     

Shape analysis of hypertrophic and hypertensive heart disease using MRI-based 3D surface models of left ventricular geometry

The focus of this study was to develop advanced mathematical tools to construct high-resolution 3D models of left-ventricular (LV) geometry to evaluate focal geometric differences between patients with hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD) using cardiac magnetic resonance (MR) cross-sectional images. A limiting factor in 3D analysis of cardiac MR cross-sections is the low out-of-plane resolution of the acquired images. To overcome this problem, we have developed a mathematical framework to construct a population-based high-resolution 3D LV triangulated surface (template) in which an iterative matching algorithm maps a surface mesh of a normal heart to a set of cross-sectional contours that were extracted from short-axis cine cardiac MR images of patients who were diagnosed with either HCM or HHD. A statistical analysis was conducted on deformations that were estimated at each surface node to identify shape differences at end-diastole (ED), end-systole (ES), and motion-related shape variation from ED to ES. Some significant shape difference in radial thickness was detected at ES. Differences of LV 3D surface geometry were identified focally on the basal anterior septum wall. Further research is needed to relate these findings to the HCM morphological substrate and to design a classifier to discriminate among different etiologies of LV hypertrophy.     

Kinematic analysis of left ventricular deformation in myocardial infarction using magnetic resonance cardiac tagging

The Magnetic Resonance (MR) tagging technique provides detailed information about 2D motion in the plane of observation. Interpretation of this information as a reflection of the 3D motion of the entire cardiac wall is a major problem. In finite element models of the mechanics of the infarcted heart, an infarcted region causes motional asymmetry, extending far beyond the infarct boundary. Here we present a method to quantify such asymmetry inamplitude and orientation. For this purpose images of a short-axis cross-section of the ejecting left ventricle were acquired from 9 healthy volunteers and 5 patients with myocardial infarction. MR-tags were applied in a 5 mm grid at end-diastole. The tags were tracked by video-image analysis. Tag motion was fitted to a kinematic model of cardiac motion. For the volunteers and the patients the center of the cavity displaced by about the same amount(p=0.11) during the ejection phase: 3.8 ± 1.4 and 3.0 ± 0.9 mm (mean ± sd), respectively. Cross-sectional rotation and the decrease in cross-sectional area of the cavity were both greater in the volunteers than in the patients: 6.4 ± 1.5 vs. 3.0 ± 0.8 degrees (p<0.001), and 945 ± 71 vs. 700 ± 176 mm 2 (p=0.02), respectively. In the patients, asymmetry of wall motion, as expressed by a sine wave dependency of contraction around the circumference, was significantly enlarged (p=0.02). The proposed method of kinematic analysis can be used to assess cardiac deformation in humans. We expect that by analyzing images of more cross-sections simultaneously, the 3D location and the degree of infarction can be assessed efficiently.     

Effects of respiratory motion on coronary wall MR imaging: a quantitative study of older adults

The aim of the present study is to assess the effects of respiratory motion on the image quality of two-dimensional (2D), free-breathing, black-blood coronary wall magnetic resonance (MR) imaging. This study was compliance with the HIPPA. With the approval of the institution review board, 230 asymptomatic participants, including 164 male patients (72.9 ± 4.4 years) and 66 female patients (72.4 ± 5.1 years), were recruited. Written informed consent was obtained. A 2D navigator (NAV)-gated, black-blood coronary wall MR imaging sequence was run on the left main artery, the left anterior descending artery and the right coronary artery. The drift of the location of the NAV and scan efficiency were compared between good (scored 2 or 3) and poor images (scored 1). Age, body weight, body weight index, heart rate, length of the rest period of cardiac motion, diaphragm excursion and breathing frequency were compared using a t test between the “successful” (having 2 or 3 good images) and “unsuccessful” cases (having 1 or 0 good images). A logistic regression model was applied to identify the contributors to good image quality. The drift of the NAV location and the scan efficiency were higher in the 411 good images compared with the 279 poor images. Minimal drift of the NAV location and low body weight were identified as independent predictors of good images after using a logistic regression model to adjust for multiple physiological and technical factors. The stability of respiratory motion significantly influences the image quality of 2D, free-breathing, black-blood coronary wall MR imaging.     

经食管三维彩色室壁动态技术（CK）评价室壁运动准确性的研究

为了检验和评价彩色室壁动态技术跟踪和显示的左室心内膜位移的准确性，我们在２５例患者中同时进行了多平面经食管左室二维超声和ＣＫ技术检查，并利用自制的计算机软件对上述图像进行左室腔体积和ＣＫ色带体积的三维重建，结果显示：左室三维ＣＫ图像不仅可立体显示左室的整体室壁运动状态，而且左室三维重建后的ＣＫ色带体积与三维重建法测量的心搏量高度相关（ｒ＝０．９２，ｐ＜０．００１），从而表明ＣＫ技术是定量分析左室整体室壁运动的可靠方法     

Accurate two-dimensional cardiac strain calculation using adaptive windowed Fourier transform and Gabor wavelet transform

Purpose

Cardiac strain calculated from tagged magnetic resonance (MR) images provides clinicians information about abnormalities of heart-wall motion in patients. It is important to develop an accurate method to determine the cardiac strain efficiently. An adaptive windowed harmonic phase (AWHARP) method is proposed for cardiac strain calculation.

Materials and methods

AWHARP is based on adaptive windowed Fourier transform (AWFT) and 2D Gabor wavelet transform (2D-GWT). The AWFT provides a spatially varying representation of the signal spectra, which allows the harmonic phase (HARP) image to be extracted with high accuracy. Instantaneous spatial frequencies are calculated using 2D-GWT, and the widths of the adaptive windows are then determined according to the instantaneous spatial frequencies for multi-resolution analysis of phase extraction. The proposed method was studied using simulated images and patients’ MR images. Both single tagged images (SPAMM) and subtracted tagged images (CSPAMM) were generated using our simulation method, and their results calculated using AWHARP and HARP methods were compared. Normal and pathological tagged MR images were also processed to evaluate the performance of our method.

Results

Our experimental results show that the accuracies of phase and strain images calculated using the AWHARP method are higher than that calculated using the HARP method especially for large tag line deformation. The improvement in accuracies can be up to 3.2 strain (E1) and 17.3 calculation from MR images reveals that the cardiac strain in the end-systolic state is significantly reduced for patients with hypertrophic cardiomyopathy (HCM) compared to that of healthy subjects.

Conclusion

The proposed AWHARP is an accurate and efficient method for cardiac strain estimation from MR images. This new algorithm can help clinicians to detect left ventricle dysfunctions and myocardial diseases with accurate cardiac strain analysis.     

Validation of dynamic heart models obtained using non-linear registration for virtual reality training, planning, and guidance of minimally invasive cardiac surgeries

Current minimally invasive techniques for beating heart surgery are associated with three major limitations: the shortage of realistic and safe training methods, the process of selecting port locations for optimal target coverage from X-rays and angiograms, and the sole use of the endoscope for instrument navigation in a dynamic and confined 3D environment. To supplement the current surgery training, planning and guidance methods, we continue to develop our Virtual Cardiac Surgery Planning environment (VCSP) -- a virtual reality, patient-specific, thoracic cavity model derived from 3D pre-procedural images. In this work, we create and validate dynamic models of the heart and its components. A static model is first generated by segmenting one of the image frames in a given 4D data set. The dynamics of this model are then extracted from the remaining image frames using a non-linear, intensity-based registration algorithm with a choice of six different similarity metrics. The algorithm is validated on an artificial CT image set created using an excised porcine heart, on CT images of canine subjects, and on MR images of human volunteers. We found that with the appropriate choice of similarity metric, our algorithm extracts the motion of the epicardial surface in CT images, or of the myocardium, right atrium, right ventricle, aorta, left atrium, pulmonary arteries, vena cava and epicardial surface in MR images, with a root mean square error in the 1 mm range. These results indicate that our method of modeling the motion of the heart is easily adaptable and sufficiently accurate to meet the requirements for reliable cardiac surgery training, planning, and guidance.     

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions

In image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method.     

Oblique 3D MRI tags for the estimation of true 3D cardiac motion parameters

Aim of this study is to demonstrate the advantages of oblique 3D tags in cardiac magnetic resonance imaging (MRI) and the potential to accurately describe the complex motion of the myocardial wall. 3D cardiac Cine data were densely tagged with 3D oblique tags. The latter were tracked using Gabor analysis and active geometries. From the tag intersections, common 2D parameters such as long axis shortening, radial shortening and rotation were evaluated on a global as well as detailed local level. Finally, the same data were used to estimate left ventricular volume change and myocardial stress/strain. We have successfully tracked dense 3D tags and evaluated common parameters on a detailed local level. In addition, inherently 3D parameters could be estimated. Global motion data are in accordance with previously published data. Oblique tags allow for unambiguous localization of the tag plane in all MRI slices and in any time frame. In contrast to HARP, our tag tracking methodology allows for tracking of the tags even when they are dense. Motion parameters can be extracted in greater detail. Moreover, the intersections of dense oblique 3D tags provide a natural basis for a finite element model of the heart. Straight forward access to the 3D characteristics of the cardiac motion is provided.     

Boundary element method-based regularization for recovering of LV deformation

The quantification of left ventricular (LV) deformation from noninvasive image sequences is an important clinical problem. To date, feature information from either magnetic resonance (MR), computed tomographic (CT) or echocardiographic image data have been assembled with the help of different regularization models to estimate LV deformation. The currently available regularization models have tradeoffs related to accuracy, lattice density, physical plausibility and computation time. This paper introduces a new regularization model based on the boundary element method (BEM) which can overcome these tradeoffs. We then employ this new regularization model with the generalized robust point matching (GRPM) strategy to estimate the dense displacement fields and strains from 3D LV image sequences. The approach is evaluated on in vivo cardiac magnetic resonance image sequences. All results are compared to displacements found using implanted markers, taken to be a gold standard. The approach is also evaluated on the 4D real time echocardiographic image sequences and the results demonstrate that the approach is capable of tracking the LV deformation for echocardiography.     

Left ventricle wall motion quantification from echocardiographic images by non-rigid image registration

Purpose

The aim of this study is to evaluate the efficiency of applying a new non-rigid image registration method on two-dimensional echocardiographic images for computing the left ventricle (LV) myocardial motion field over a cardiac cycle.

Methods

The key feature of our method is to register all images in the sequence to a reference image (end-diastole image) using a hierarchical transformation model, which is a combination of an affine transformation for modeling the global LV motion and a free-form deformation (FFD) transformation based on B-splines for modeling the local LV deformation. Registration is done by minimizing a cost function associated with the image similarity based on a global pixel-based matching and the smoothness of transformation. The algorithm uses a fast and robust optimization strategy using a multiresolution approach for the estimation of parameters of the deformation model. The proposed algorithm is evaluated for calculating the displacement curves of two expert-identified anatomical landmarks in apical views of the LV for 10 healthy volunteers and 14 subjects with pathology. The proposed algorithm is also evaluated for classifying the regional LV wall motion abnormality using the calculation of the strain value at the end of systole in 288 segments as scored by two consensual experienced echocardiographers in a three-point scale: 1: normokinesia, 2: hypokinesia, and 3: akinesia. Moreover, we compared the results of the proposed registration algorithm to those previously obtained using the other image registration methods.

Results

Regarding to the reference two experienced echocardiographers, the results demonstrate the proposed algorithm more accurately estimates the displacement curve of the two anatomical landmarks in apical views than the other registration methods in all data set. Moreover, the p values of the t test for the strain value of each segment at the end of systole measured by the proposed algorithm show higher differences than the other registration method. These differences are between each pair of scores in all segments and in three segments of septum independently.

Conclusions

The clinical results show that the proposed algorithm can improve both the calculation of the displacement curve of every point of LV during a cardiac cycle and the classification of regional LV wall motion abnormality. Therefore, this diagnostic system can be used as a useful tool for clinical evaluation of the regional LV function.     

Tracking and motion analysis of the left ventricle with deformable superquadrics

We present a new approach to analyse the deformation of the left ventricle of the heart based on a parametric model that gives a compact representation of a set of points in a 3-D image. We present a strategy for tracking surfaces in a sequence of 3-D cardiac images. Following tracking, we then infer quantitative parameters which characterize: left ventricle motion, volume of left ventricle, ejection fraction, amplitude and twist component of cardiac motion. We explain the computation of these parameters using our model. Experimental results are shown in time sequences of two modalities of medical images, nuclear medicine and X-ray computed tomography (CT). Video sequences presenting these results are on the CD-ROM.     

Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model

The construction of subject-specific dense and realistic 3D meshes of the myocardial fibers is an important pre-requisite for the simulation of cardiac electrophysiology and mechanics. Current diffusion tensor imaging (DTI) techniques, however, provide only a sparse sampling of the 3D cardiac anatomy based on a limited number of 2D image slices. Moreover, heart motion affects the diffusion measurements, thus resulting in a significant amount of noisy fibers. This paper presents a Markov random field (MRF) approach for dense reconstruction of 3D cardiac fiber orientations from sparse DTI 2D slices. In the proposed MRF model, statistical constraints are used to relate the missing and the known fibers, while a consistency term is encoded to ensure that the obtained 3D meshes are locally continuous. The validation of the method using both synthetic and real DTI datasets demonstrates robust fiber reconstruction and denoising, as well as physiologically meaningful estimations of cardiac electrical activation.     

Preliminary validation of angle-independent myocardial elastography using MR tagging in a clinical setting

Myocardial elastography (ME), a radio-frequency (RF) based speckle tracking technique, was employed in order to image the entire two-dimensional (2D) transmural deformation field in full echocardiographic views and was validated against tagged magnetic resonance imaging (tMRI) in normal as well as reperfused (i.e., treated myocardial infarction [MI]) human left ventricles. RF ultrasound and tMRI frames were acquired at the papillary muscle level in 2D short-axis (SA) views at the frame rates of 136 (fps; real-time) and 33 fps (electrocardiogram [ECG]-gated), respectively. In ME, in-plane, 2D (lateral and axial) incremental displacements were iteratively estimated using one-dimensional (1D) cross-correlation and recorrelation techniques in a 2D search with a 1D matching kernel. In tMRI, cardiac motion was estimated by a template-matching algorithm on a 2D grid-shaped mesh. In both ME and tMRI, cumulative 2D displacements were obtained and then used to estimate 2D Lagrangian finite systolic strains, from which polar (i.e., radial and circumferential) strains, namely angle-independent measures, were further obtained through coordinate transformation. Principal strains, which are angle-independent and less centroid-dependent than polar strains, were also computed and imaged based on the 2D finite strains using methodology previously established. Both qualitatively and quantitatively, angle-independent ME is shown to be capable of (1) estimating myocardial deformation in good agreement with tMRI estimates in a clinical setting and of (2) differentiating abnormal from normal myocardium in a full left-ventricular view. The principal strains were concluded to be a potential diagnostic measure for detection of cardiac disease with reduced centroid dependence.