On the Real-Time Performance, Robustness and Accuracy of Medical Image Non-Rigid Registration

Three critical issues about medical image non-rigid registration are performance, robustness and accuracy. A registration method, which is capable of responding timely with an accurate alignment, robust against the variation of the image intensity and the missing data, is desirable for its clinical use. This work addresses all three of these issues. Unacceptable execution time of Non-rigid registration (NRR) often presents a major obstacle to its routine clinical use. We present a hybrid data partitioning method to parallelize a NRR method on a cooperative architecture, which enables us to get closer to the goal: accelerating using architecture rather than designing a parallel algorithm from scratch. to further accelerate the performance for the GPU part, a GPU optimization tool is provided to automatically optimize GPU execution configuration.;Missing data and variation of the intensity are two severe challenges for the robustness of the registration method. A novel point-based NRR method is presented to resolve mapping function (deformation field) with the point correspondence missing. The novelty of this method lies in incorporating a finite element biomechanical model into an Expectation and Maximization (EM) framework to resolve the correspondence and mapping function simultaneously. This method is extended to deal with the deformation induced by tumor resection, which imposes another challenge, i.e. incomplete intra-operative MRI. The registration is formulated as a three variable (Correspondence, Deformation Field, and Resection Region) functional minimization problem and resolved by a Nested Expectation and Maximization framework. The experimental results show the effectiveness of this method in correcting the deformation in the vicinity of the tumor. to deal with the variation of the intensity, two different methods are developed depending on the specific application. For the mono-modality registration on delayed enhanced cardiac MRI and cine MRI, a hybrid registration method is designed by unifying both intensity- and feature point-based metrics into one cost function. The experiment on the moving propagation of suspicious myocardial infarction shows effectiveness of this hybrid method. For the multi-modality registration on MRI and CT, a Mutual Information (MI)-based NRR is developed by modeling the underlying deformation as a Free-Form Deformation (FFD). MI is sensitive to the variation of the intensity due to equidistant bins. We overcome this disadvantage by designing a Top-to-Down K-means clustering method to naturally group similar intensities into one bin. The experiment shows this method can increase the accuracy of the MI-based registration.;In image registration, a finite element biomechanical model is usually employed to simulate the underlying movement of the soft tissue. We develop a multi-tissue mesh generation method to build a heterogeneous biomechanical model to realistically simulate the underlying movement of the brain. We focus on the following four critical mesh properties: tissue-dependent resolution, fidelity to tissue boundaries, smoothness of mesh surfaces, and element quality. Each mesh property can be controlled on a tissue level. The experiments on comparing the homogeneous model with the heterogeneous model demonstrate the effectiveness of the heterogeneous model in improving the registration accuracy

A Heterogeneous and Multi-Range Soft-Tissue Deformation Model for Applications in Adaptive Radiotherapy

During fractionated radiotherapy, anatomical changes result in uncertainties in the applied dose distribution. With increasing steepness of applied dose gradients, the relevance of patient deformations increases. Especially in proton therapy, small anatomical changes in the order of millimeters can result in large range uncertainties and therefore in substantial deviations from the planned dose. To quantify the anatomical changes, deformation models are required. With upcoming MR-guidance, the soft-tissue deformations gain visibility, but so far only few soft-tissue models meeting the requirements of high-precision radiotherapy exist. Most state-of-the-art models either lack anatomical detail or exhibit long computation times. In this work, a fast soft-tissue deformation model is developed which is capable of considering tissue properties of heterogeneous tissue. The model is based on the chainmail (CM)-concept, which is improved by three basic features. For the first time, rotational degrees of freedom are introduced into the CM-concept to improve the characteristic deformation behavior. A novel concept for handling multiple deformation initiators is developed to cope with global deformation input. And finally, a concept for handling various shapes of deformation input is proposed to provide a high flexibility concerning the design of deformation input. To demonstrate the model flexibility, it was coupled to a kinematic skeleton model for the head and neck region, which provides anatomically correct deformation input for the bones. For exemplary patient CTs, the combined model was shown to be capable of generating artificially deformed CT images with realistic appearance. This was achieved for small-range deformations in the order of interfractional deformations, as well as for large-range deformations like an arms-up to arms-down deformation, as can occur between images of different modalities. The deformation results showed a strong improvement in biofidelity, compared to the original chainmail-concept, as well as compared to clinically used image-based deformation methods. The computation times for the model are in the order of 30 min for single-threaded calculations, by simple code parallelization times in the order of 1 min can be achieved. Applications that require realistic forward deformations of CT images will benefit from the improved biofidelity of the developed model. Envisioned applications are the generation of plan libraries and virtual phantoms, as well as data augmentation for deep learning approaches. Due to the low computation times, the model is also well suited for image registration applications. In this context, it will contribute to an improved calculation of accumulated dose, as is required in high-precision adaptive radiotherapy

Imaging Meningiomas : The Road to the First Medical Application of the Novel Magnetic Resonance Rheology

In this thesis the first application of Magnetic Resonance Rheology (MRR) to patients with known brain lesions was presented. MRR is a novel approach to image the mechanical properties of brain tissue in vivo. It utilizes a short fall of the head of approximately 1 mm to create a global excitation of the brain tissue. The resulting deformations of the tissue are measured by means of Magnetic Resonance Imaging (MRI) and motion sensitive phase imaging techniques. Numerical simulations were used to predict the signals MRR yields for volume elements following specific trajectories during the experiment. A post-procession pipeline was developed and implemented to analyze the measured phase data and to reconstruct the induced falling motion based on the optically measured position of the lifting device. The influence of mechanical properties of the investigated material (stiffness and density) was presented on the basis of exemplary measurements on inhomogeneous phantoms. These consisted of two layers of different agar hydrogel manufactured with either different stiffness but similar density, or of similar stiffness but different density. The application of the method in vivo was shown by means of exemplary measurements on a healthy volunteer. The measured data was analyzed by visual inspection of the resulting phase images depicting deflection of the material and of computed phase strain images depicting the principle strain in the direction of the falling motion. The dynamics of the material was further investigated using the temporal evolution of the phase over the progression of the falling motion. The results confirmed a global oscillation of the material in response to the excitation. Particularly useful for visual inspection proved the temporal integration of the phase strain. Here, local differences in the mechanical properties of the phantom material respectively the brain tissue were depicted best. The presented methods have been used in a study on four patients diagnosed with meningiomas, which are benign tumors originating from the meninges surrounding the brain. Manual palpation by the neurosurgeons that resected the tumors after the MRR measurements showed an increased stiffness of the tumor tissue compared to healthy tissue. The tumor regions showed well defined signatures in the obtained images. Comparing the strain in the tumor regions with the one in healthy parts of the corresponding brains showed a trend of lower strains with increased tumor stiffness. These results can be considered a proof of concept of the feasibility of MRR to depict local alterations of the mechanical properties of brain tissue in vivo

3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.

Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control

Use of Multicomponent Non-Rigid Registration to Improve Alignment of Serial Oncological PET/CT Studies

Non-rigid registration of serial head and neck FDG PET/CT images from a combined scanner can be problematic. Registration techniques typically rely on similarity measures calculated from voxel intensity values; CT-CT registration is superior to PET-PET registration due to the higher quality of anatomical information present in this modality. However, when metal artefacts from dental fillings are present in a pair of CT images, a nonrigid registration will incorrectly attempt to register the two artefacts together since they are strong features compared to the features that represent the actual anatomy. This leads to localised registration errors in the deformation field in the vicinity of the artefacts. Our objective was to develop a registration technique which overcomes these limitations by using combined information from both modalities. To study the effect of artefacts on registration, metal artefacts were simulated with one CT image rotated by a small angle in the sagittal plane. Image pairs containing these simulated artifacts were then registered to evaluate the resulting errors. To improve the registration in the vicinity where there were artefacts, intensity information from the PET images was incorporated using several techniques. A well-established B-splines based non-rigid registration code was reworked to allow multicomponent registration. A similarity measure with four possible weighted components relating to the ways in which the CT and PET information can be combined to drive the registration of a pair of these dual-valued images was employed. Several registration methods based on using this multicomponent similarity measure were implemented with the goal of effectively registering the images containing the simulated artifacts. A method was also developed to swap control point displacements from the PET-derived transformation in the vicinity of the artefact. This method yielded the best result on the simulated images and was evaluated on images where actual dental artifacts were present

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

Brain and Human Body Modeling

This open access book describes modern applications of computational human modeling with specific emphasis in the areas of neurology and neuroelectromagnetics, depression and cancer treatments, radio-frequency studies and wireless communications. Special consideration is also given to the use of human modeling to the computational assessment of relevant regulatory and safety requirements. Readers working on applications that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest developments in computational modelling and human phantom development to assess a given technology’s safety and efficacy in a timely manner. Describes construction and application of computational human models including anatomically detailed and subject specific models; Explains new practices in computational human modeling for neuroelectromagnetics, electromagnetic safety, and exposure evaluations; Includes a survey of modern applications for which computational human models are critical; Describes cellular-level interactions between the human body and electromagnetic fields

Modeling, Simulation, And Visualization Of 3d Lung Dynamics

Medical simulation has facilitated the understanding of complex biological phenomenon through its inherent explanatory power. It is a critical component for planning clinical interventions and analyzing its effect on a human subject. The success of medical simulation is evidenced by the fact that over one third of all medical schools in the United States augment their teaching curricula using patient simulators. Medical simulators present combat medics and emergency providers with video-based descriptions of patient symptoms along with step-by-step instructions on clinical procedures that alleviate the patient\u27s condition. Recent advances in clinical imaging technology have led to an effective medical visualization by coupling medical simulations with patient-specific anatomical models and their physically and physiologically realistic organ deformation. 3D physically-based deformable lung models obtained from a human subject are tools for representing regional lung structure and function analysis. Static imaging techniques such as Magnetic Resonance Imaging (MRI), Chest x-rays, and Computed Tomography (CT) are conventionally used to estimate the extent of pulmonary disease and to establish available courses for clinical intervention. The predictive accuracy and evaluative strength of the static imaging techniques may be augmented by improved computer technologies and graphical rendering techniques that can transform these static images into dynamic representations of subject specific organ deformations. By creating physically based 3D simulation and visualization, 3D deformable models obtained from subject-specific lung images will better represent lung structure and function. Variations in overall lung deformations may indicate tissue pathologies, thus 3D visualization of functioning lungs may also provide a visual tool to current diagnostic methods. The feasibility of medical visualization using static 3D lungs as an effective tool for endotracheal intubation was previously shown using Augmented Reality (AR) based techniques in one of the several research efforts at the Optical Diagnostics and Applications Laboratory (ODALAB). This research effort also shed light on the potential usage of coupling such medical visualization with dynamic 3D lungs. The purpose of this dissertation is to develop 3D deformable lung models, which are developed from subject-specific high resolution CT data and can be visualized using the AR based environment. A review of the literature illustrates that the techniques for modeling real-time 3D lung dynamics can be roughly grouped into two categories: Geometrically-based and Physically-based. Additional classifications would include considering a 3D lung model as either a volumetric or surface model, modeling the lungs as either a single-compartment or a multi-compartment, modeling either the air-blood interaction or the air-blood-tissue interaction, and considering either a normal or pathophysical behavior of lungs. Validating the simulated lung dynamics is a complex problem and has been previously approached by tracking a set of landmarks on the CT images. An area that needs to be explored is the relationship between the choice of the deformation method for the 3D lung dynamics and its visualization framework. Constraints on the choice of the deformation method and the 3D model resolution arise from the visualization framework. Such constraints of our interest are the real-time requirement and the level of interaction required with the 3D lung models. The work presented here discusses a framework that facilitates a physics-based and physiology-based deformation of a single-compartment surface lung model that maintains the frame-rate requirements of the visualization system. The framework presented here is part of several research efforts at ODALab for developing an AR based medical visualization framework. The framework consists of 3 components, (i) modeling the Pressure-Volume (PV) relation, (ii) modeling the lung deformation using a Green\u27s function based deformation operator, and (iii) optimizing the deformation using state-of-art Graphics Processing Units (GPU). The validation of the results obtained in the first two modeling steps is also discussed for normal human subjects. Disease states such as Pneumothorax and lung tumors are modeled using the proposed deformation method. Additionally, a method to synchronize the instantiations of the deformation across a network is also discussed

Construction of Physics-based brain atlas and its application

Ph.DDOCTOR OF PHILOSOPH