CNN-based Lung CT Registration with Multiple Anatomical Constraints

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below $1.2$ mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/

Computational Techniques to Predict Orthopaedic Implant Alignment and Fit in Bone

Among the broad palette of surgical techniques employed in the current orthopaedic practice, joint replacement represents one of the most difficult and costliest surgical procedures. While numerous recent advances suggest that computer assistance can dramatically improve the precision and long term outcomes of joint arthroplasty even in the hands of experienced surgeons, many of the joint replacement protocols continue to rely almost exclusively on an empirical basis that often entail a succession of trial and error maneuvers that can only be performed intraoperatively. Although the surgeon is generally unable to accurately and reliably predict a priori what the final malalignment will be or even what implant size should be used for a certain patient, the overarching goal of all arthroplastic procedures is to ensure that an appropriate match exists between the native and prosthetic axes of the articulation. To address this relative lack of knowledge, the main objective of this thesis was to develop a comprehensive library of numerical techniques capable to: 1) accurately reconstruct the outer and inner geometry of the bone to be implanted; 2) determine the location of the native articular axis to be replicated by the implant; 3) assess the insertability of a certain implant within the endosteal canal of the bone to be implanted; 4) propose customized implant geometries capable to ensure minimal malalignments between native and prosthetic axes. The accuracy of the developed algorithms was validated through comparisons performed against conventional methods involving either contact-acquired data or navigated implantation approaches, while various customized implant designs proposed were tested with an original numerical implantation method. It is anticipated that the proposed computer-based approaches will eliminate or at least diminish the need for undesirable trial and error implantation procedures in a sense that present error-prone intraoperative implant insertion decisions will be at least augmented if not even replaced by optimal computer-based solutions to offer reliable virtual “previews” of the future surgical procedure. While the entire thesis is focused on the elbow as the most challenging joint replacement surgery, many of the developed approaches are equally applicable to other upper or lower limb articulations

Analysis of the inspection of mechanical parts using dense range data

More than ever, efficiency and quality are key words in modern industry. This situation enhances the importance of quality control and creates a great demand for cheap and reliable automatic inspection systems. Taking into account these facts and the demand for systems able to inspect the final shape of machined parts, we decided to investigate the viability of automatic model-based inspection of mechanical parts using the dense range data produced by laser stripers. Given a part to be inspected and a corresponding model of the part stored in the model data base, the first step of inspecting the part is the acquisition of data corresponding to the part, in our case this means the acquisition of a range image of it. In order to be able to compare the part image and its stored model, it is necessary to align the model with the range image of the part. This process, called registration, corresponds to finding the rigid transformation that superposes model and image. After the image and model are registered, the actual inspection uses the range image to verify if all the features predicted in the model are present and have the right pose and dimensions. Therefore, besides the acquisition of range images, the inspection of machined parts involves three main issues: modelling, registration and inspection diagnosis. The application, for inspection purposes, of the main representational schemes for modelling solid objects is discussed and it is suggested the use of EDT models (see [Zeid 91]). A particular implementation of EDT models is presented. A novel approach for the verification of tolerances during the inspection is proposed. The approach allows not only the inspection of the most common tolerances described in the tolerancing standards, but also the inspection of tolerances defined according to Requicha's theory of tolerancing (see [Requicha 83]). A model of the sensitivity and reliability of the inspection process based on the modelling of the errors during the inspection process is also proposed. The importance of the accuracy of the registration in different inspections tasks is discussed. A modified version of the ICP algorithm (see [Besl &; McKay 92]) for the registration of sculptured surfaces is proposed. The maximum accuracy of the ICP algorithm, as a function of the sensor errors and the number of matched points, is determined. A novel method for the measurement and reconstruction of waviness errors on sculp¬ tured surfaces is proposed. The method makes use of the 2D Discrete Fourier Transform for the detection and reconstruction of the waviness error. A model of the sensitivity and reliability of the method is proposed. The application of the methods proposed is illustrated using synthetic and real range image

A Unified Framework for Parallel Anisotropic Mesh Adaptation

Finite-element methods are a critical component of the design and analysis procedures of many (bio-)engineering applications. Mesh adaptation is one of the most crucial components since it discretizes the physics of the application at a relatively low cost to the solver. Highly scalable parallel mesh adaptation methods for High-Performance Computing (HPC) are essential to meet the ever-growing demand for higher fidelity simulations. Moreover, the continuous growth of the complexity of the HPC systems requires a systematic approach to exploit their full potential. Anisotropic mesh adaptation captures features of the solution at multiple scales while, minimizing the required number of elements. However, it also introduces new challenges on top of mesh generation. Also, the increased complexity of the targeted cases requires departing from traditional surface-constrained approaches to utilizing CAD (Computer-Aided Design) kernels. Alongside the functionality requirements, is the need of taking advantage of the ubiquitous multi-core machines. More importantly, the parallel implementation needs to handle the ever-increasing complexity of the mesh adaptation code. In this work, we develop a parallel mesh adaptation method that utilizes a metric-based approach for generating anisotropic meshes. Moreover, we enhance our method by interfacing with a CAD kernel, thus enabling its use on complex geometries. We evaluate our method both with fixed-resolution benchmarks and within a simulation pipeline, where the resolution of the discretization increases incrementally. With the Telescopic Approach for scalable mesh generation as a guide, we propose a parallel method at the node (multi-core) for mesh adaptation that is expected to scale up efficiently to the upcoming exascale machines. To facilitate an effective implementation, we introduce an abstract layer between the application and the runtime system that enables the use of task-based parallelism for concurrent mesh operations. Our evaluation indicates results comparable to state-of-the-art methods for fixed-resolution meshes both in terms of performance and quality. The integration with an adaptive pipeline offers promising results for the capability of the proposed method to function as part of an adaptive simulation. Moreover, our abstract tasking layer allows the separation of different aspects of the implementation without any impact on the functionality of the method

Toward quantitative limited-angle ultrasound reflection tomography to inform abdominal HIFU treatment planning

High-Intensity Focused Ultrasound (HIFU) is a treatment modality for solid cancers of the liver and pancreas which is non-invasive and free from many of the side-effects of radiotherapy and chemotherapy. The safety and efficacy of abdominal HIFU treatment is dependent on the ability to bring the therapeutic sound waves to a small focal ”lesion” of known and controllable location within the patient anatomy. To achieve this, pre-treatment planning typically includes a numerical simulation of the therapeutic ultrasound beam, in which anatomical compartment locations are derived from computed tomography or magnetic resonance images. In such planning simulations, acoustic properties such as density and speed-of-sound are assumed for the relevant tissues which are rarely, if ever, determined specifically for the patient. These properties are known to vary between patients and disease states of tissues, and to influence the intensity and location of the HIFU lesion. The subject of this thesis is the problem of non-invasive patient-specific measurement of acoustic tissue properties. The appropriate method, also, of establishing spatial correspondence between physical ultrasound transducers and modeled (imaged) anatomy via multimodal image reg-istration is also investigated; this is of relevance both to acoustic tissue property estimation and to the guidance of HIFU delivery itself. First, the principle of a method is demonstrated with which acoustic properties can be recovered for several tissues simultaneously using reflection ultrasound, given accurate knowledge of the physical locations of tissue compartments. Second, the method is developed to allow for some inaccuracy in this knowledge commensurate with the inaccuracy typical in abdominal multimodal image registration. Third, several current multimodal image registration techniques, and two novel modifications, are compared for accuracy and robustness. In conclusion, relevant acoustic tissue properties can, in principle, be estimated using reflected ultrasound data that could be acquired using diagnostic imaging transducers in a clinical setting

Improving the forward model for electrical impedance tomography of brain function through rapid generation of subject specific finite element models

Electrical Impedance Tomography (EIT) is a non-invasive imaging method which allows internal electrical impedance of any conductive object to be imaged by means of current injection and surface voltage measurements through an array of externally applied electrodes. The successful generation of the image requires the simulation of the current injection patterns on either an analytical or a numerical model of the domain under examination, known as the forward model, and using the resulting voltage data in the inverse solution from which images of conductivity changes can be constructed. Recent research strongly indicates that geometric and anatomical conformance of the forward model to the subject under investigation significantly affects the quality of the images. This thesis focuses mainly on EIT of brain function and describes a novel approach for the rapid generation of patient or subject specific finite element models for use as the forward model. After introduction of the topic, methods of generating accurate finite element (FE) models using commercially available Computer-Aided Design (CAD) tools are described and show that such methods, though effective and successful, are inappropriate for time critical clinical use. The feasibility of warping or morphing a finite element mesh as a means of reducing the lead time for model generation is then presented and demonstrated. This leads on to the description of methods of acquiring and utilising known system geometry, namely the positions of electrodes and registration landmarks, to construct an accurate surface of the subject, the results of which are successfully validated. The outcome of this procedure is then used to specify boundary conditions to a mesh warping algorithm based on elastic deformation using well-established continuum mechanics procedures. The algorithm is applied to a range of source models to empirically establish optimum values for the parameters defining the problem which can successfully generate meshes of acceptable quality in terms of discretization errors and which more accurately define the geometry of the target subject. Further validation of the algorithm is performed by comparison of boundary voltages and image reconstructions from simulated and laboratory data to demonstrate that benefits in terms of image artefact reduction and localisation of conductivity changes can be gained. The processes described in the thesis are evaluated and discussed and topics of further work and application are described

Refinement of object-based segmentation

Automated object-based segmentation methods calculate the shape and pose of anatomical structures of interest. These methods require modeling both the geometry and object-relative image intensity patterns of target structures. Many object-based segmentation methods minimize a non-convex function and risk failure due to convergence to a local minimum. This dissertation presents three refinements to existing object-based segmentation methods. The first refinement mitigates the risk of local minima by initializing the segmentation closely to the correct answer. The initialization searches pose- and shape-spaces for the object that best matches user specified points on three designated image slices. Thus-initialized m-rep based segmentations of the bladder from CT are frequently better than segmentations reported elsewhere. The second refinement is a statistical test on object-relative intensity patterns that allows estimation of the local credibility of a segmentation. This test effectively identifies regions with local segmentation errors in m-rep based segmentations of the bladder and prostate from CT. The third refinement is a method for shape interpolation that is based on changes in the position and orientation of samples and that tends to be more shape-preserving than a competing linear method. This interpolation can be used with dynamic structures and to understand changes between segmentations of an object in atlas and target images. Together, these refinements aid in the segmentation of a dense collection of targets via a hybrid of object-based and atlas-based methods. The first refinement increases the probability of successful object-based segmentations of the subset of targets for which such methods are appropriate, the second increases the user's confidence that those object-based segmentations are correct, and the third is used to transfer the object-based segmentations to an atlas-based method that will be used to segment the remainder of the targets

Diffusion MRI of the prostate at 300 mT/m

Diffusion magnetic resonance imaging gained worldwide recognition as the chief non-invasive mean of mapping microstructural properties of biological tissue. By far, the most common clinical application of this method is diagnosing conditions such as stroke and cancer. Prostate cancer is one of the leading cancer-related causes of deaths among men. Recently, MRI became a triage test in the diagnostic pathway for men suspected of having prostate cancer. While providing a very useful contrast, limited gradient power of clinical MR hardware prevents it from capturing the whole range of microstructural changes. For that reason, one research priority is to provide novel MRI-based biomarkers for increased efficacy of its early diagnosis. Assessing early microstructural changes in the prostate is of fundamental importance, as it can increase patients’ chance of survival. Novel technological developments can be used when trying to address the issues with limited spatial resolution, sensitivity and specificity of the clinical MRI scans. The work presented in this thesis focused on the use of one of the most powerful whole-body MRI scanners - in terms of the gradient amplitude - available worldwide to advance diffusion MRI of the prostate. Advanced image reconstruction, accounting for MR field perturbations, was exploited to address concerns regarding low image quality resulting from limited MR gradient fidelity. In addition, diffusion MRI was probed at sub-millimetre spatial resolution and effectively enhanced spatial resolution of the images which is crucial for accurate localisation of cancerous lesions. The work presented in this thesis showcases the ways to obtain higher spatial resolution, and boosted signal-to-noise ratio in diffusion MRI images of the prostate, as well as increased sensitivity to subtle tissue alterations that can be observed early in prostate cancer. The preliminary results are promising, but further development of methods is required, including exploring more advanced microstructural imaging sequences and methods for analysing the data they allow to obtain