Segmentation of Myocardial Boundaries in Tagged Cardiac MRI Using Active Contours: A Gradient-Based Approach Integrating Texture Analysis

The noninvasive assessment of cardiac function is of first importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial and epicardial contours detection is based on the use of texture analysis and active contours models. Texture analysis allows us to define energy maps more efficient than those usually used in active contours methods where attractor is often based on gradient and which were useless in our case of study, for quality of tagged cardiac MRI is very poor

Myocardial deformation assessment using cardiovascular magnetic resonance-feature tracking technique.

BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging is an important modality that allows the assessment of regional myocardial function by measuring myocardial deformation parameters, such as strain and strain rate throughout the cardiac cycle. Feature tracking is a promising quantitative post-processing technique that is increasingly used. It is commonly applied to cine images, in particular steady-state free precession, acquired during routine CMR examinations. OBJECTIVE: To review the studies that have used feature tracking techniques in healthy subjects or patients with cardiovascular diseases. The article emphasizes the advantages and limitations of feature tracking when applied to regional deformation parameters. The challenges of applying the techniques in clinics and potential solutions are also reviewed. RESULTS: Research studies in healthy volunteers and/or patients either applied CMR-feature tracking alone to assess myocardial motion or compared it with either established CMR-tagging techniques or to speckle tracking echocardiography. These studies assessed the feasibility and reliability of calculating or determining global and regional myocardial deformation strain parameters. Regional deformation parameters are reviewed and compared. Better reproducibility for global deformation was observed compared with segmental parameters. Overall, studies demonstrated that circumferential was the most reproducible deformation parameter, usually followed by longitudinal strain; in contrast, radial strain showed high variability. CONCLUSION: Although feature tracking is a promising tool, there are still discrepancies in the results obtained using different software packages. This highlights a clear need for standardization of MRI acquisition parameters and feature tracking analysis methodologies. Validation, including physical and numerical phantoms, is still required to facilitate the use of feature tracking in routine clinical practice

Myocardial tagging by Cardiovascular Magnetic Resonance: evolution of techniques--pulse sequences, analysis algorithms, and applications

Cardiovascular magnetic resonance (CMR) tagging has been established as an essential technique for measuring regional myocardial function. It allows quantification of local intramyocardial motion measures, e.g. strain and strain rate. The invention of CMR tagging came in the late eighties, where the technique allowed for the first time for visualizing transmural myocardial movement without having to implant physical markers. This new idea opened the door for a series of developments and improvements that continue up to the present time. Different tagging techniques are currently available that are more extensive, improved, and sophisticated than they were twenty years ago. Each of these techniques has different versions for improved resolution, signal-to-noise ratio (SNR), scan time, anatomical coverage, three-dimensional capability, and image quality. The tagging techniques covered in this article can be broadly divided into two main categories: 1) Basic techniques, which include magnetization saturation, spatial modulation of magnetization (SPAMM), delay alternating with nutations for tailored excitation (DANTE), and complementary SPAMM (CSPAMM); and 2) Advanced techniques, which include harmonic phase (HARP), displacement encoding with stimulated echoes (DENSE), and strain encoding (SENC). Although most of these techniques were developed by separate groups and evolved from different backgrounds, they are in fact closely related to each other, and they can be interpreted from more than one perspective. Some of these techniques even followed parallel paths of developments, as illustrated in the article. As each technique has its own advantages, some efforts have been made to combine different techniques together for improved image quality or composite information acquisition. In this review, different developments in pulse sequences and related image processing techniques are described along with the necessities that led to their invention, which makes this article easy to read and the covered techniques easy to follow. Major studies that applied CMR tagging for studying myocardial mechanics are also summarized. Finally, the current article includes a plethora of ideas and techniques with over 300 references that motivate the reader to think about the future of CMR tagging

Three Dimensional Tissue Motion Analysis from Tagged Magnetic Resonance Imaging

Motion estimation of soft tissues during organ deformation has been an important topic in medical imaging studies. Its application involves a variety of internal and external organs including the heart, the lung, the brain, and the tongue. Tagged magnetic resonance imaging has been used for decades to observe and quantify motion and strain of deforming tissues. It places temporary noninvasive markers—so called "tags"—in the tissue of interest that deform together with the tissue during motion, producing images that carry motion information in the deformed tagged patterns. These images can later be processed using phase-extraction algorithms to achieve motion estimation and strain computation. In this dissertation, we study three-dimensional (3D) motion estimation and analysis using tagged magnetic resonance images with applications focused on speech studies and traumatic brain injury modeling. Novel algorithms are developed to assist tagged motion analysis. Firstly, a pipeline of methods—TMAP—is proposed to compute 3D motion from tagged and cine images of the tongue during speech. TMAP produces an estimation of motion along with a multi-subject analysis of motion pattern differences between healthy control subjects and post-glossectomy patients. Secondly, an enhanced 3D motion estimation algorithm—E-IDEA—is proposed. E-IDEA tackles the incompressible motion both on the internal tissue region and the tissue boundaries, reducing the boundary errors and yielding a motion estimate that is more accurate overall. Thirdly, a novel 3D motion estimation algorithm—PVIRA—is developed. Based on image registration and tracking, PVIRA is a faster and more robust method that performs phase extraction in a novel way. Lastly, a method to reveal muscles' activity using strain in the line of action of muscle fiber directions is presented. It is a first step toward relating motion production with individual muscles and provides a new tool for future clinical and scientific use

Cardiac image computing for myocardial infarction patients

Cardiovascular diseases (CVDs), which are a prime cause of global mortality, are disorders that affect the heart and blood vessels' functioning. CVDs may cause consequent complications, due to occlusion in a blood vessel and present as impaired cardiac wall functioning (myocardium). Identifying such impairment (infarction) of the myocardium is of great clinical interest, as it can reveal the nature of altered cardiac topography (ventricular remodelling) to aid the associated intervention decisions. With recent advances in cardiac imaging, such as Magnetic Resonance (MR) imaging, the visualisation and identification of infarcted myocardium has been routinely and effectively used in clinical practice. Diagnosing infarcted myocardium is achieved clinically through the late gadolinium enhancement (LGE) test, which acquires MR images after injecting a gadolinium-based contrast agent (GBCA). Due to the increased accuracy and reproducibility, LGE has emerged as the gold-standard MR imaging test in identifying myocardial infarction. However, clinical studies have reported gadolinium deposition concerns in different body organs and adverse outcomes in patients with advanced kidney failure, over time. Such incidents have motivated researchers to look into the development of both accurate as well as safe diagnostic tools. Emerging research on identifying infarcted myocardium utilises myocardial strain to safely identify infarcted myocardium, which has been addressed in the presented study. For example, myocardial strain represents the shortening or lengthening of the myocardium. If the myocardium is infarcted, then the corresponding strain values differ compared to the healthy myocardium. This finding can be identified and utilised for clinical applications. The research presented in this thesis aims to identify infarcted myocardium accurately and safely by using myocardial strain (shortening or lengthening of the myocardium). To achieve the aforementioned aim, the research methodology is divided into six objectives. The initial objectives relate to the development of a novel myocardial tracking method. The middle objectives relate to the development of clinical application methods, and the final objectives concern the validation of the developed methods through clinical studies and associated datasets. The research presented in this thesis has addressed the following research question: Research question 1: How can a 2D myocardial tracking and strain calculation method be developed using the 2D local weighted mean function and structural deformation within the myocardium? Research question 2: How can a 3D myocardial tracking and strain calculation method be developed using the 3D local weighted mean function to calculate 3D myocardial strain? Research question 3: How can 2D circumferential strain of the myocardium be used in identifying infarcted left ventricular segments for the diagnosis of myocardial infarction patients? In literature, myocardial tracking and strain calculation methods have limited extension to 3D and dependency on tissue material properties. Moreover, additional limitations, such as limited inclusion of structural deformation details within the myocardium, are found in the literature. Therefore, methods are likely to become subjective or numerically unstable during computation. Moreover, the inclusion of myocardial details with grid-tagging MRI, for structural deformation within the myocardium, is more realistic compared to cine MRI.   The aforementioned limitations are overcome by proposing a novel Hierarchical Template Matching method, which performs non-rigid image registration among grid-tagging MR images of a cardiac cycle. This is achieved by employing a local weighted mean transformation function. The proposed non-rigid image registration method does not require the use of tissue material properties. Grid-tagging MRI is used to capture wall function within the myocardium, and the local weighted mean function is used for numerical stability. The performance of the developed methods is evaluated with multiple error measures and with a benchmark framework. This benchmark framework has provided an open-access 3D dataset, a set of validation methods, and results of four leading methods for comparison. Validation methods include qualitative and quantitative methods. The qualitative assessment of outcomes and verified ground truth for the quantitative evaluation of results are followed from the benchmark framework paper (Tobon-Gomez, Craene, Mcleod, et al., 2013). 2D HTM method has reported the root mean square error of point tracking in left ventricular slices, which are the basal slice 0.31±0.07 mm, the upper mid-ventricular slice 0.37±0.06 mm, the mid-ventricular slice 0.41±0.05 mm, and the apical slice 0.32±0.08 mm. The mid-ventricular slice has a significantly higher 4% (P=0.05) mean root mean square error compared to the other slices. However, the other slices do not have a significant difference among them. Compared to the benchmark free form deformation method, HTM has a mean error of 0.35±0.05 mm, which is 17% (P=0.07, CI:[-0.01,0.35]) reduced to the free form deformation method. Our technical method has shown the 3D extension of HTM and a method without using material properties, which is advantageous compared to the methods which are limited to 2D or dependent on material properties. Moreover, the 3D HTM has demonstrated the use of 3D local weighted mean function in 3D myocardial tracking. While comparing to the benchmark methods, it was found that the median tracking error of 3D HTM is comparable to benchmark methods and has very few outliers compared to them. The clinical results are validated with LGE imaging. The quantitative error measure is the area under the curve (AUC) of sensitivity vs 1-specificity curve of the receiver operating characteristic (ROC) test. The achieved AUC value in detecting infarcted segments in basal, mid-ventricular, and apical slices are 0.85, 0.82, and 0.87, respectively. Calculating AUC with 95% confidence level, the confidence intervals of lower and upper mean AUC values in basal, mid-ventricular and apical slices are [0.80, 0.89], [0.74, 0.85], and [0.78, 0.91], respectively. Overall, considering the detections of LGE imaging as the base, our method has an accuracy of AUC 0.73 (P=0.05) in identifying infarcted left ventricular segments. The developed methods have shown, systematically, a promising approach in identifying infarcted left ventricular segments by image processing method and without using GBCA-based LGE imaging.

Analysis of strain in the human left ventricle using real-time 3D echocardiography and optical flow

Cardiovascular disease (CVD) consistently ranks among the leading causes of death in the United States. The most common subtype of CVD, ischemic heart disease, is a frequent precursor of myocardial infarction and heart failure, most commonly affecting the left ventricle (LV). Today, echocardiography is regarded as the gold standard in screening, diagnosis, and monitoring of LV dysfunction. But while global assessment of LV function tends to be quantitative, cardiologists with specific expertise still perform many regional evaluations subjectively. However, a more objective and quantitative measure of regional function – myocardial strain – has been developed and widely studied using 2D echocardiography. With recent developments in real-time 3D echocardiography (RT3DE), it has become possible to measure strain in its native 3D orientation as well. Our laboratory’s earlier work introduced the Optical Flow (OF) method of strain analysis, which was validated on simulated echocardiograms as well as through animal studies. The principal goal of this thesis is to translate this OF-based method of strain estimation from the research setting to the patient’s bedside. We have performed a series of studies to evaluate the feasibility, accuracy, and reproducibility of OF-based myocardial strain estimation in a routine clinical setting. The first investigation focused on the optimization of RT3DE acquisition and the OF processing pipeline for use in human subjects. Subsequently, we evaluated the capacity of this technique to distinguish abnormal strain patterns in patients with CVD and varying degrees of LV dysfunction. Our analysis revealed that segmental strain measures obtained by OF may have better sensitivity and specificity than the more commonly used global LV strains. Our third validation study examined the reproducibility of these strain measures in both healthy and diseased populations. We established that OF-based strain measures demonstrate repeatability comparable to that achieved by the latest commercial software commonly used in clinical research to estimate 2D or 3D strain. These studies were driven in large part by the absence of a ground truth or accepted gold standard of 3D strain measurements in the human LV. However, cardiac magnetic resonance imaging has had considerable success in measuring some forms of strain in the human LV. We therefore began to develop an image-processing pipeline to derive strain estimates from a new pulse sequence called 3D-DENSE. We further sought to improve the OF pipeline by automating the process of tracking the LV border. To this end, we developed a level-set based technique which tracks the LV endocardium. Our evaluation of its performance on RT3DE data confirmed that this method performs within the limits of inter-observer variability. Overall, our pilot studies of OF-based strain estimation demonstrate that the technique possesses several promising features for improving cardiologists’ ability to quantify and interpret the complex three-dimensional deformations of the human LV

Flow Imaging Using MRI: Quantification and Analysis

A complex and challenging problem in flow study is to obtain quantitative flow information in opaque systems, for example, blood flow in biological systems and flow channels in chemical reactors. In this regard, MRI is superior to the conventional optical flow imaging or ultrasonic Doppler imaging. However, for high speed flows, complex flow behaviors and turbulences make it difficult to image and analyze the flows. In MR flow imaging, MR tagging technique has demonstrated its ability to simultaneously visualize motion in a sequence of images. Moreover, a quantification method, namely HARmonic Phase (HARP) analysis, can extract a dense velocity field from tagged MR image sequence with minimal manual intervention. In this work, we developed and validated two new MRI methods for quantification of very rapid flows. First, HARP was integrated with a fast MRI imaging method called SEA (Single Echo Acquisition) to image and analyze high velocity flows. Second, an improved HARP method was developed to deal with tag fading and data noise in the raw MRI data. Specifically, a regularization method that incorporates the law of flow dynamics in the HARP analysis was developed. Finally, the methods were validated using results from the computational fluid dynamics (CFD) and the conventional optimal flow imaging based on particle image velocimetry (PIV). The results demonstrated the improvement from the quantification using solely the conventional HARP method