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.

Hierarchical template matching for 3D myocardial tracking and cardiac strain estimation

Myocardial tracking and strain estimation can non-invasively assess cardiac functioning using subject-specific MRI. As the left-ventricle does not have a uniform shape and functioning from base to apex, the development of 3D MRI has provided opportunities for simultaneous 3D tracking, and 3D strain estimation. We have extended a Local Weighted Mean (LWM) transformation function for 3D, and incorporated in a Hierarchical Template Matching model to solve 3D myocardial tracking and strain estimation problem. The LWM does not need to solve a large system of equations, provides smooth displacement of myocardial points, and adapt local geometric differences in images. Hence, 3D myocardial tracking can be performed with 1.49 mm median error, and without large error outliers. The maximum error of tracking is up to 24% reduced compared to benchmark methods. Moreover, the estimated strain can be insightful to improve 3D imaging protocols, and the computer code of LWM could also be useful for geo-spatial and manufacturing image analysis researchers

Identifying myocardial infarction using hierarchical template matching–based myocardial strain : algorithm development and usability study

Background: Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. Objective: This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. Methods: HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. Results: The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement–based detections. Conclusions: The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA