DRIMET: Deep Registration for 3D Incompressible Motion Estimation in Tagged-MRI with Application to the Tongue

Tagged magnetic resonance imaging (MRI) has been used for decades to observe and quantify the detailed motion of deforming tissue. However, this technique faces several challenges such as tag fading, large motion, long computation times, and difficulties in obtaining diffeomorphic incompressible flow fields. To address these issues, this paper presents a novel unsupervised phase-based 3D motion estimation technique for tagged MRI. We introduce two key innovations. First, we apply a sinusoidal transformation to the harmonic phase input, which enables end-to-end training and avoids the need for phase interpolation. Second, we propose a Jacobian determinant-based learning objective to encourage incompressible flow fields for deforming biological tissues. Our method efficiently estimates 3D motion fields that are accurate, dense, and approximately diffeomorphic and incompressible. The efficacy of the method is assessed using human tongue motion during speech, and includes both healthy controls and patients that have undergone glossectomy. We show that the method outperforms existing approaches, and also exhibits improvements in speed, robustness to tag fading, and large tongue motion.Comment: Accepted to MIDL 2023 (full paper

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

Filter Design and Consistency Evaluation for 3D Tongue Motion Estimation using Harmonic Phase Analysis Method

Understanding patterns of tongue motion in speech using 3D motion estimation is challenging. Harmonic phase analysis has been used to perform noninvasive tongue motion and strain estimation using tagged magnetic resonance imaging (MRI). Two main contributions have been made in this thesis. First, the filtering process, which is used to produce harmonic phase images used for tissue tracking, influences the estimation accuracy. For this work, we evaluated different filtering approaches, and propose a novel high-pass filter for volumes tagged in individual directions. Testing was done using an open benchmarking dataset and synthetic images obtained using a mechanical model. Second, the datasets with inconsistent motion need to be excluded to yield meaningful motion estimation. For this work, we used a tracking-based method to evaluate the motion consistency between datasets and gave a strategy to identify the inconsistent dataset. Experiments including 2 normal subjects were done to validate our method. In all, the first work about 3D filter design improves the motion estimation accuracy and the second work about motion consistency test ensures the meaningfulness of the estimation results

MulViMotion: shape-aware 3D myocardial motion tracking from multi-view cardiac MRI

Recovering the 3D motion of the heart from cine cardiac magnetic resonance (CMR) imaging enables the assessment of regional myocardial function and is important for understanding and analyzing cardiovascular disease. However, 3D cardiac motion estimation is challenging because the acquired cine CMR images are usually 2D slices which limit the accurate estimation of through-plane motion. To address this problem, we propose a novel multi-view motion estimation network (MulViMotion), which integrates 2D cine CMR images acquired in short-axis and long-axis planes to learn a consistent 3D motion field of the heart. In the proposed method, a hybrid 2D/3D network is built to generate dense 3D motion fields by learning fused representations from multi-view images. To ensure that the motion estimation is consistent in 3D, a shape regularization module is introduced during training, where shape information from multi-view images is exploited to provide weak supervision to 3D motion estimation. We extensively evaluate the proposed method on 2D cine CMR images from 580 subjects of the UK Biobank study for 3D motion tracking of the left ventricular myocardium. Experimental results show that the proposed method quantitatively and qualitatively outperforms competing methods

Motion tracking tMRI datasets to quantify abnormal left ventricle motion using finite element modelling

According to `The Atlas of Heart Disease and Stroke'[MMMG04] published by the World Health Organization, heart disease accounts for nearly half the deaths in both the developed and developing countries and is the world's single biggest killer. However, early detection of a diseased heart condition can prevent many of these fatalities. Regional wall motion abnormalities of the heart precede both ECG abnormalities and chest pain as an indicator of myocardial ischaemia and are an excellent indicator of coronary stenosis [GZM97]. These motion abnormalities of the heart muscle are difficult to observe and track, because the heart is a relatively smooth organ with few landmarks and non-rigid motion with a twisting motion or tangential component. The MRI tissue-tagging technique gives researchers the first glimpse into how the heart actually beats. This research uses the tagged MRI images of the heart to create a three dimensional model of a beating heart indicating the stress of a region. Tagged MRI techniques are still developing and vary vastly, meaning that there needs to be a methodology that can adapt to these changes rapidly and effectively, to meet the needs of the evolving technology. The focus of this research is to develop and test such a methodology by the means of a Strain Estimation Pipeline along with an effective way of validating any changes made to the individual processes that it comprises of

Dynamic Deformation and Mechanical Properties of Brain Tissue

Traumatic brain injury is an important medical problem affecting millions of people. Mathematical models of brain biomechanics are being developed to simulate the mechanics of brain injury and to design protective devices. However, because of a lack of quantitative data on brain-skull boundary conditions and deformations, the predictions of mathematical models remain uncertain. The objectives of this dissertation are to develop methods and obtain experimental data that will be used to parameterize and validate models of traumatic brain injury. To that end, this dissertation first addresses the brain-skull boundary conditions by measuring human brain motion using tagged magnetic resonance imaging. Magnetic resonance elastography was performed in the ferret brain to measure its mechanical properties in vivo. Brain tissue is not only heterogeneous, but may also be anisotropic. To characterize tissue anisotropy, an experimental procedure combining both shear testing and indentation was developed and applied to white matter and gray matter. These measurements of brain-skull interactions and mechanical properties of the brain will be valuable in the development and validation of finite element simulations of brain biomechanics

An image segmentation and registration approach to cardiac function analysis using MRI

Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent years, significant progress has been made in the care and treatment of patients with such diseases. A crucial factor for this progress has been the development of magnetic resonance (MR) imaging which makes it possible to diagnose and assess the cardiovascular function of the patient. The ability to obtain high-resolution, cine volume images easily and safely has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during the image acquisition process. With the development of advanced MR imaging acquisition technologies, 3D MR imaging is more and more clinically feasible. This recent development has allowed new potentially 3D image analysis technologies to be deployed. However, quantitative analysis of cardiovascular system from the images remains a challenging topic. The work presented in this thesis describes the development of segmentation and motion analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic resonance (CMR) images. The first main contribution of the thesis is the development of a fully automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art techniques. The proposed segmentation technique is capable of generating an accurate 3D segmentation from multiple image sequences. The proposed segmentation technique is robust even in the presence of pathological changes, large anatomical shape variations and locally varying contrast in the images. Another main contribution of this thesis is the development of motion tracking techniques that can integrate motion information from different sources. For example, the radial motion of the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of both longitudinal and circumferential motion. We propose a novel technique based on non-rigid image registration for the myocardial motion estimation using both untagged and 3D tagged MR images. The novel aspect of our technique is its simultaneous use of complementary information from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted to maximise the utility of information from both images. The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard FFD approach, a significant increase in registration accuracy can be observed in datasets with discontinuous motion patterns. Both the segmentation and motion tracking techniques presented in this thesis have been applied to clinical studies. We focus on two important clinical applications that can be addressed by the techniques proposed in this thesis. The first clinical application aims at measuring longitudinal changes in cardiac morphology and function during the cardiac remodelling process. The second clinical application aims at selecting patients that positively respond to cardiac resynchronization therapy (CRT). The final chapter of this thesis summarises the main conclusions that can be drawn from the work presented here and also discusses possible avenues for future research