The Brain Tumor Sequence Registration Challenge: Establishing Correspondence between Pre-Operative and Follow-up MRI scans of diffuse glioma patients

Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes, and still an unsolved problem. This paper describes the first Brain Tumor Sequence Registration (BraTS-Reg) challenge, focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a brain diffuse glioma. The BraTS-Reg challenge intends to establish a public benchmark environment for deformable registration algorithms. The associated dataset comprises de-identified multi-institutional multi-parametric MRI (mpMRI) data, curated for each scan's size and resolution, according to a common anatomical template. Clinical experts have generated extensive annotations of landmarks points within the scans, descriptive of distinct anatomical locations across the temporal domain. The training data along with these ground truth annotations will be released to participants to design and develop their registration algorithms, whereas the annotations for the validation and the testing data will be withheld by the organizers and used to evaluate the containerized algorithms of the participants. Each submitted algorithm will be quantitatively evaluated using several metrics, such as the Median Absolute Error (MAE), Robustness, and the Jacobian determinant

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

Enabling technology for non-rigid registration during image-guided neurosurgery

In the context of image processing, non-rigid registration is an operation that attempts to align two or more images using spatially varying transformations. Non-rigid registration finds application in medical image processing to account for the deformations in the soft tissues of the imaged organs. During image-guided neurosurgery, non-rigid registration has the potential to assist in locating critical brain structures and improve identification of the tumor boundary. Robust non-rigid registration methods combine estimation of tissue displacement based on image intensities with the spatial regularization using biomechanical models of brain deformation. In practice, the use of such registration methods during neurosurgery is complicated by a number of issues: construction of the biomechanical model used in the registration from the image data, high computational demands of the application, and difficulties in assessing the registration results. In this dissertation we develop methods and tools that address some of these challenges, and provide components essential for the intra-operative application of a previously validated physics-based non-rigid registration method.;First, we study the problem of image-to-mesh conversion, which is required for constructing biomechanical model of the brain used during registration. We develop and analyze a number of methods suitable for solving this problem, and evaluate them using application-specific quantitative metrics. Second, we develop a high-performance implementation of the non-rigid registration algorithm and study the use of geographically distributed Grid resources for speculative registration computations. Using the high-performance implementation running on the remote computing resources we are able to deliver the results of registration within the time constraints of the neurosurgery. Finally, we present a method that estimates local alignment error between the two images of the same subject. We assess the utility of this method using multiple sources of ground truth to evaluate its potential to support speculative computations of non-rigid registration

Registration of Images with Pathologies

Registration is one of the fundamental tasks in medical image analysis. It is an essential step for many applications to establish spatial correspondences between two images. However, image registration in the presence of pathologies is challenging due to tissue appearance changes and missing correspondences caused by the pathologies. For example, for patients with brain tumors, the tissue is often displaced by the tumors, creating more significant deformations than what is observed in a healthy brain. Moreover, a fast and accurate image registration in the presence of pathologies is especially desired for immediate assessment of the registration results. This dissertation addresses the following problems concerning the registration of images with pathologies: (1) efficient registration between an image with pathologies and a common control atlas; (2) patient-specific longitudinal registration between pre-operative and post recurrence images for patients with glioblastoma; (3) automatic brain extraction for images with pathologies; and (4) fast predictive registration of images with pathologies to an atlas. Contributions presented in this dissertation are as follows: (1) I develop a joint PCA/image-reconstruction approach for images with pathologies. The model estimates quasi-normal image appearance from the image with pathologies and uses the reconstructed quasi normal image for registration. It improves the registration accuracy compared to directly using the images with pathologies, while not requiring the segmentation of the pathological region. (2) I propose a patient-specific registration framework for the longitudinal study of tumor recurrence of patients diagnosed with glioblastoma. It models the healthy tissue appearance for each patient in the individual space, thereby improving the registration accuracy. (3) I develop a brain extraction method for images with pathologies by jointly modeling healthy brain tissue, pathologies, and non-brain volume. (4) I design a joint registration and reconstruction deep learning model which learns an appearance mapping from the image with pathologies to atlas appearance while simultaneously predicting the transformation to atlas space. The network disentangles the spatial variation from the appearance changes caused by the pathology.Doctor of Philosoph

Memory recovery in relation to default mode network impairment and neurite density during brain tumor treatment.

OBJECTIVE: The aim of this study was to test brain tumor interactions with brain networks, thereby identifying protective features and risk factors for memory recovery after resection. METHODS: Seventeen patients with diffuse nonenhancing glioma (ages 22-56 years) underwent longitudinal MRI before and after surgery, and during a 12-month recovery period (47 MRI scans in total after exclusion). After each scanning session, a battery of memory tests was performed using a tablet-based screening tool, including free verbal memory, overall verbal memory, episodic memory, orientation, forward digit span, and backward digit span. Using structural MRI and neurite orientation dispersion and density imaging (NODDI) derived from diffusion-weighted images, the authors estimated lesion overlap and neurite density, respectively, with brain networks derived from normative data in healthy participants (somatomotor, dorsal attention, ventral attention, frontoparietal, and default mode network [DMN]). Linear mixed-effect models (LMMs) that regressed out the effect of age, gender, tumor grade, type of treatment, total lesion volume, and total neurite density were used to test the potential longitudinal associations between imaging markers and memory recovery. RESULTS: Memory recovery was not significantly associated with either the tumor location based on traditional lobe classification or the type of treatment received by patients (i.e., surgery alone or surgery with adjuvant chemoradiotherapy). Nonlocal effects of tumors were evident on neurite density, which was reduced not only within the tumor but also beyond the tumor boundary. In contrast, high preoperative neurite density outside the tumor but within the DMN was associated with better memory recovery (LMM, p value after false discovery rate correction [Pfdr] < 10-3). Furthermore, postoperative and follow-up neurite density within the DMN and frontoparietal network were also associated with memory recovery (LMM, Pfdr = 0.014 and Pfdr = 0.001, respectively). Preoperative tumor and postoperative lesion overlap with the DMN showed a significant negative association with memory recovery (LMM, Pfdr = 0.002 and Pfdr < 10-4, respectively). CONCLUSIONS: Imaging biomarkers of cognitive recovery and decline can be identified using NODDI and resting-state networks. Brain tumors and their corresponding treatment affecting brain networks that are fundamental for memory functioning such as the DMN can have a major impact on patients' memory recovery.We thank all patients for generous involvement in the study. We also thank to Luca Villa, Jessica Ingham, Alexa Mcdonald for their contribution to the study. This research was supported by The Brain Tumour Charity and the Guarantors of Brai

Signs of progression:MR image analysis for the management of low-grade glioma

Every year approximately one thousand people in the Netherlands are diagnosedwith diffuse glioma, a type of infiltrative brain tumor that originates from theglial cells. There is no curative treatment available for adults diagnosed witha diffuse glioma, although surgical resection, radiotherapy and chemotherapyare used to improve prognosis and decrease symptoms. Low-grade glioma canremain stable for long periods of time before, inevitably, malignant progressionoccurs. The radiological assessment of glioma through magnetic resonanceimaging (MRI) plays an important role in the management of glioma. Inthis thesis I explore the role of quantitative measurements, emerging imagingmarkers and predictive modelling in the management of glioma. These methodscan aid the radiologist to predict the timing, location and severity of tumorprogression, to ultimately improve the quality of life for glioma patients.<br/

Radiological Society of North America (RSNA) 3D printing Special Interest Group (SIG): Guidelines for medical 3D printing and appropriateness for clinical scenarios

Este número da revista Cadernos de Estudos Sociais estava em organização quando fomos colhidos pela morte do sociólogo Ernesto Laclau. Seu falecimento em 13 de abril de 2014 surpreendeu a todos, e particularmente ao editor Joanildo Burity, que foi seu orientando de doutorado na University of Essex, Inglaterra, e que recentemente o trouxe à Fundação Joaquim Nabuco para uma palestra, permitindo que muitos pudessem dialogar com um dos grandes intelectuais latinoamericanos contemporâneos. Assim, buscamos fazer uma homenagem ao sociólogo argentino publicando uma entrevista inédita concedida durante a sua passagem pelo Recife, em 2013, encerrando essa revista com uma sessão especial sobre a sua trajetória

Advanced Application of Diffusion Kurtosis Imaging

Diffusion tensor imaging (DTI) has become a standard procedure in clinical routine as well as research as it enables the reconstruction and visualization of fiber tracts in the human brain. Due to the simplified assumption the tensor model – a Gaussian distribution of the diffusion – it typically fails to provide neither accurate spatial mapping nor quantification of crossing or kissing fibers. A clinically feasible development might be diffusion kurtosis imaging (DKI), an extension of DTI also integrating non-Gaussian distribution diffusion processes and thereby shall overcome some of its limitations. The potential DKI will be evaluated in case of the detection of the interhemispheric asymmetry of the white matter in healthy volunteers (n = 20), as well as the analysis of tumor-related impairments of fiber tracts and their correlation with neurological deficits in patients (n = 13) diagnosed with glioma. In order to analyze interhemispheric asymmetry across the whole brain, especially of nine large fiber tracts, tract-based spatial statistics (TBSS) analysis was performed using DTI- and DKI-based parameters, a laterality index was calculated for asymmetries and DTI- and DKI-based results were compared. With regard to fractional anisotropy as marker of integrity, asymmetry was found for all nine fiber tracts based on DTI and seven tracts based on DKI. For mean diffusivity, asymmetries were found for three (DTI) and two (DKI) fiber tracts. Regarding mean kurtosis, asymmetry was found in one tract. The interhemispheric asymmetry thereby varied in anatomical location as well as in cluster size. Only small parts of the tracts were affected. A comparison of DTI and DKI showed significantly higher fractional anisotropy and mean diffusivity based on DKI compared to DTI. Gender and handedness did not seem to have any influence. For the assessment of tumor-related changes of fiber tracts in patients diagnosed with glioma, especially in relation to pre-existing and postoperative neurological deficits (hemiparesis, aphasia), templates for the corticospinal tract and the arcuate fasciculus were created based on DTI- and DKI-derived parameters, respectively. The corticospinal tract and the arcuate fasciculus were reconstructed for each patient and the associated parametric maps were projected onto the templates. Based on this, alterations along the tracts could be identified and quantified. Alterations were found on fiber tracts regardless of the spatial proximity to the lesion. There was a correlation between alterations based on fractional anisotropy, mean diffusivity and mean kurtosis. Increased mean diffusivity was associated with alteration in mean kurtosis, a decreased fractional anisotropy was found concurrent with a likewise decreased mean kurtosis. In the case of pre-existing neurological deficits (hemiparesis, aphasia) with regard to the changes along the fiber tracts (corticospinal tract, left arcuate fasciculus), most often increased mean diffusivity and altered mean kurtosis was found. Applying this pattern for prediction of corresponding postoperative neurological deficits a sensitivity of 75.0% and a specificity of 87.5% was achieved. DKI seems to more precisely estimated and depict the underlying microstructure in comparison to DTI. Thereby, in pathological cases especially the mean kurtosis seems to be of special interest. A combination of DTI- and DKI based parameters, particularly with regard to its clinical usability and value, offers great potential in clinical routine