Non-Invasive Imaging for the Assessment of Cardiac Dose and Function Following Focused External Beam Irradiation

Technological advances in imaging and radiotherapy have led to significant improvement in the survival rate of breast cancer patients. However, a larger proportion of patients are now exhibiting the less understood, latent effects of incidental cardiac irradiation that occurs during left-sided breast radiotherapy. Here, we examine the utility of four-dimensional computed tomography (4D-CT) for the accurate assessment of cardiac dose; and a hybrid positron emission tomography (PET) magnetic resonance imaging (MRI) system to longitudinally study radiation-induced cardiac effects in a canine model. Using 4D-CT and deformable dose accumulation, we assessed the variation caused by breathing motion in the estimated dose to the heart, left-ventricle, and left anterior descending artery (LAD) of left-sided breast cancer patients. The LAD showed substantial variation in dose due to breathing. In light of this, we suggest the use of 4D-CT and dose accumulation for future clinical studies looking at the relationship between LAD dose and cardiac toxicity. Although symptoms of cardiac dysfunction may not manifest clinically for 10-15 years post radiation, PET-MRI can potentially identify earlier changes in cardiac inflammation and perfusion that are typically asymptomatic. Using PET-MRI, the progression of radiation-induced cardiac toxicity was assessed in a large animal model. Five canines were imaged using 13N-ammonia and 18F-fluorodeoxyglucose (FDG) PET-MRI to assess changes in myocardial perfusion and inflammation, respectively. All subjects were imaged at baseline, 1 week, 4 weeks, 3 months, 6 months, and 12 months after focused cardiac irradiation. To the best of our knowledge PET has not been previously used to assess cardiac perfusion following irradiation. The delivered dose to the heart, left ventricle, LAD, and left circumflex artery were comparable to what has been observed during breast radiotherapy. Relative to baseline, a transient increase in myocardial perfusion was observed followed by a gradual return to baseline. However, a persistent increase in FDG uptake was observed throughout the entire left ventricle, including both irradiated and less-irradiated portions of the heart. In light of these findings, we suggest the use of this imaging approach for future human studies to assess mitigation strategies aimed at minimizing cardiac exposure and long-term toxicity subsequent to left-sided breast irradiation

A Non-Rigid Registration Method for Analyzing Myocardial Wall Motion for Cardiac CT Images

Cardiac resynchronization therapy (CRT) has a high percentage of non-responders. Successfully locating the optimal location for CRT lead placement on a priori images can increase efficiency in procedural preparation and execution and could potentially increase the rate of CRT responders. Registration has been used in the past to assess the motion of medical images. Specifically, one method of non-rigid registration has been utilized to assess the motion of left ventricular MR cardiac images. As CT imaging is often performed as part of resynchronization treatment planning and is a fast and accessible means of imaging, extending this registration method to assessing left ventricular motion of CT images could provide another means of reproducible contractility assessment. This thesis investigates the use of non-rigid registration to evaluate the myocardium motion in multi-phase multi-slice computed tomography (MSCT) cardiac imaging for the evaluation of mechanical contraction of the left ventricle

Automatic Assessment of Cardiac Left Ventricular Function Via Magnetic Resonance Images

Automating global and segmental (regional) assessments of cardiac Left Ventricle (LV) function in Magnetic Resonance Images (MRI) has recently sparked an impressive research effort, which has resulted a number of techniques delivering promising performances. However, despite such an effort, the problem is still acknowledged to be challenging, with substantial room for improvements in regard to accuracy. Furthermore, most of the existing techniques are labour intensive, requiring delineations of the endo- and/or epi-cardial boundaries in all frames of a cardiac sequence. On the one hand, global assessments of LV function focus on estimation of the Ejection Fraction (EF), which quantifies how much blood the heart is pumping within each beat. On the other hand, regional assessments focus on comprehensive analysis of the wall motions within each of the standardized segments of the myocardium, the muscle which contracts and sends the blood out of the LV. In clinical practice, the EF is often estimated via manual segmentations of several images in a cardiac sequence. This is prohibitively time consuming, or via automatic segmentations, which is a challenging and computationally expensive task that may result in high estimation errors. Additionally, the diagnosis of the segmental dysfunction is based on visual LV assessments, which are subject to high inter-observer variability. In this thesis, we propose accurate methods to estimate both global and regional LV function with minimal user inputs in real-time from statistics estimated in MRI. From a simple user input, we build image statistics for all the images in a subject dataset. We demonstrate that these statistics are correlated with regional as well as global LV function. Different machine learning techniques have been employed to find these correlations. The regional dysfunction is investigated in terms of a binary/multi-classification problem. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlated very well with those obtained from independent manual segmentations. Furthermore, comparisons with estimating EF with recent segmentation algorithms show that the proposed method yielded a very competitive performance. For regional binary classification, we report a comprehensive experimental evaluation of the proposed algorithm over 928 cardiac segments obtained from 58 subjects. Compared against ground-truth evaluations by experienced radiologists, the proposed algorithm performed competitively, with an overall classification accuracy of 86.09% and a kappa measure of 0.73. We also report a comprehensive experimental evaluation of the proposed multi-classification algorithm over the same dataset. Compared against ground-truth labels assessed by experienced radiologists, the proposed algorithm yielded an overall 4-class accuracy of 74.14%

Analysis of cardiac motion using MRI and nonrigid image registration

Imperial Users onl

Dynamic Image Processing for Guidance of Off-pump Beating Heart Mitral Valve Repair

Compared to conventional open heart procedures, minimally invasive off-pump beating heart mitral valve repair aims to deliver equivalent treatment for mitral regurgitation with reduced trauma and side effects. However, minimally invasive approaches are often limited by the lack of a direct view to surgical targets and/or tools, a challenge that is compounded by potential movement of the target during the cardiac cycle. For this reason, sophisticated image guidance systems are required in achieving procedural efficiency and therapeutic success. The development of such guidance systems is associated with many challenges. For example, the system should be able to provide high quality visualization of both cardiac anatomy and motion, as well as augmenting it with virtual models of tracked tools and targets. It should have the capability of integrating pre-operative images to the intra-operative scenario through registration techniques. The computation speed must be sufficiently fast to capture the rapid cardiac motion. Meanwhile, the system should be cost effective and easily integrated into standard clinical workflow. This thesis develops image processing techniques to address these challenges, aiming to achieve a safe and efficient guidance system for off-pump beating heart mitral valve repair. These techniques can be divided into two categories, using 3D and 2D image data respectively. When 3D images are accessible, a rapid multi-modal registration approach is proposed to link the pre-operative CT images to the intra-operative ultrasound images. The ultrasound images are used to display the real time cardiac motion, enhanced by CT data serving as high quality 3D context with annotated features. I also developed a method to generate synthetic dynamic CT images, aiming to replace real dynamic CT data in such a guidance system to reduce the radiation dose applied to the patients. When only 2D images are available, an approach is developed to track the feature of interest, i.e. the mitral annulus, based on bi-plane ultrasound images and a magnetic tracking system. The concept of modern GPU-based parallel computing is employed in most of these approaches to accelerate the computation in order to capture the rapid cardiac motion with desired accuracy. Validation experiments were performed on phantom, animal and human data. The overall accuracy of registration and feature tracking with respect to the mitral annulus was about 2-3mm with computation time of 60-400ms per frame, sufficient for one update per cardiac cycle. It was also demonstrated in the results that the synthetic CT images can provide very similar anatomical representations and registration accuracy compared to that of the real dynamic CT images. These results suggest that the approaches developed in the thesis have good potential for a safer and more effective guidance system for off-pump beating heart mitral valve repair

Characterization and Compensation of Hysteretic Cardiac Respiratory Motion in Myocardial Perfusion Studies Through MRI Investigations

Respiratory motion causes artifacts and blurring of cardiac structures in reconstructed images of SPECT and PET cardiac studies. Hysteresis in respiratory motion causes the organs to move in distinct paths during inspiration and expiration. Current respiratory motion correction methods use a signal generated by tracking the motion of the abdomen during respiration to bin list- mode data as a function of the magnitude of this respiratory signal. They thereby fail to account for hysteretic motion. The goal of this research was to demonstrate the effects of hysteretic respiratory motion and the importance of its correction for different medical imaging techniques particularly SPECT and PET. This study describes a novel approach for detecting and correcting hysteresis in clinical SPECT and PET studies. From the combined use of MRI and a synchronized Visual Tracking System (VTS) in volunteers we developed hysteretic modeling using the Bouc-Wen model with inputs from measurements of both chest and abdomen respiratory motion. With the MRI determined heart motion as the truth in the volunteer studies we determined the Bouc Wen model could match the behavior over a range of hysteretic cycles. The proposed approach was validated through phantom simulations and applied to clinical SPECT studies

Cardiac Motion Analysis Based on Optical Flow on Real-Time Three-Dimensional Ultrasound Data

With relatively high frame rates and the ability to acquire volume data sets with a stationary transducer, 3D ultrasound systems, based on matrix phased array transducers, provide valuable three-dimensional information, from which quantitative measures of cardiac function can be extracted. Such analyses require segmentation and visual tracking of the left ventricular endocardial border. Due to the large size of the volumetric data sets, manual tracing of the endocardial border is tedious and impractical for clinical applications. Therefore the development of automatic methods for tracking three-dimensional endocardial motion is essential. In this study, we evaluate a four-dimensional optical flow motion tracking algorithm to determine its capability to follow the endocardial border in three dimensional ultrasound data through time. The four-dimensional optical flow method was implemented using three-dimensional correlation. We tested the algorithm on an experimental open-chest dog data set and a clinical data set acquired with a Philips' iE33 three-dimensional ultrasound machine. Initialized with left ventricular endocardial data points obtained from manual tracing at end-diastole, the algorithm automatically tracked these points frame by frame through the whole cardiac cycle. Finite element surfaces were fitted through the data points obtained by both optical flow tracking and manual tracing by an experienced observer for quantitative comparison of the results. Parameterization of the finite element surfaces was performed and maps displaying relative differences between the manual and semi-automatic methods were compared. The results showed good consistency with less than 10% difference between manual tracing and optical flow estimation on 73% of the entire surface. In addition, the optical flow motion tracking algorithm greatly reduced processing time (about 94% reduction compared to human involvement per cardiac cycle) for analyzing cardiac function in three-dimensional ultrasound data sets. A displacement field was computed from the optical flow output, and a framework for computation of dynamic cardiac information is introduced. The method was applied to a clinical data set from a heart transplant patient and dynamic measurements agreed with known physiology as well as experimental results