Innovative techniques to devise 3D-printed anatomical brain phantoms for morpho-functional medical imaging

Introduction. The Ph.D. thesis addresses the development of innovative techniques to create 3D-printed anatomical brain phantoms, which can be used for quantitative technical assessments on morpho-functional imaging devices, providing simulation accuracy not obtainable with currently available phantoms. 3D printing (3DP) technology is paving the way for advanced anatomical modelling in biomedical applications. Despite the potential already expressed by 3DP in this field, it is still little used for the realization of anthropomorphic phantoms of human organs with complex internal structures. Making an anthropomorphic phantom is very different from making a simple anatomical model and 3DP is still far from being plug-and-print. Hence, the need to develop ad-hoc techniques providing innovative solutions for the realization of anatomical phantoms with unique characteristics, and greater ease-of-use. Aim. The thesis explores the entire workflow (brain MRI images segmentation, 3D modelling and materialization) developed to prototype a new complex anthropomorphic brain phantom, which can simulate three brain compartments simultaneously: grey matter (GM), white matter (WM) and striatum (caudate nucleus and putamen, known to show a high uptake in nuclear medicine studies). The three separate chambers of the phantom will be filled with tissue-appropriate solutions characterized by different concentrations of radioisotope for PET/SPECT, para-/ferro-magnetic metals for MRI, and iodine for CT imaging. Methods. First, to design a 3D model of the brain phantom, it is necessary to segment MRI images and to extract an error-less STL (Standard Tessellation Language) description. Then, it is possible to materialize the prototype and test its functionality. - Image segmentation. Segmentation is one of the most critical steps in modelling. To this end, after demonstrating the proof-of-concept, a multi-parametric segmentation approach based on brain relaxometry was proposed. It includes a pre-processing step to estimate relaxation parameter maps (R1 = longitudinal relaxation rate, R2 = transverse relaxation rate, PD = proton density) from the signal intensities provided by MRI sequences of routine clinical protocols (3D-GrE T1-weighted, FLAIR and fast-T2-weighted sequences with ≤ 3 mm slice thickness). In the past, maps of R1, R2, and PD were obtained from Conventional Spin Echo (CSE) sequences, which are no longer suitable for clinical practice due to long acquisition times. Rehabilitating the multi-parametric segmentation based on relaxometry, the estimation of pseudo-relaxation maps allowed developing an innovative method for the simultaneous automatic segmentation of most of the brain structures (GM, WM, cerebrospinal fluid, thalamus, caudate nucleus, putamen, pallidus, nigra, red nucleus and dentate). This method allows the segmentation of higher resolution brain images for future brain phantom enhancements. - STL extraction. After segmentation, the 3D model of phantom is described in STL format, which represents the shapes through the approximation in manifold mesh (i.e., collection of triangles, which is continuous, without holes and with a positive – not zero – volume). For this purpose, we developed an automatic procedure to extract a single voxelized surface, tracing the anatomical interface between the phantom's compartments directly on the segmented images. Two tubes were designed for each compartment (one for filling and the other to facilitate the escape of air). The procedure automatically checks the continuity of the surface, ensuring that the 3D model could be exported in STL format, without errors, using a common image-to-STL conversion software. Threaded junctions were added to the phantom (for the hermetic closure) using a mesh processing software. The phantom's 3D model resulted correct and ready for 3DP. Prototyping. Finally, the most suitable 3DP technology is identified for the materialization. We investigated the material extrusion technology, named Fused Deposition Modeling (FDM), and the material jetting technology, named PolyJet. FDM resulted the best candidate for our purposes. It allowed materializing the phantom's hollow compartments in a single print, without having to print them in several parts to be reassembled later. FDM soluble internal support structures were completely removable after the materialization, unlike PolyJet supports. A critical aspect, which required a considerable effort to optimize the printing parameters, was the submillimetre thickness of the phantom walls, necessary to avoid distorting the imaging simulation. However, 3D printer manufacturers recommend maintaining a uniform wall thickness of at least 1 mm. The optimization of printing path made it possible to obtain strong, but not completely waterproof walls, approximately 0.5 mm thick. A sophisticated technique, based on the use of a polyvinyl-acetate solution, was developed to waterproof the internal and external phantom walls (necessary requirement for filling). A filling system was also designed to minimize the residual air bubbles, which could result in unwanted hypo-intensity (dark) areas in phantom-based imaging simulation. Discussions and conclusions. The phantom prototype was scanned trough CT and PET/CT to evaluate the realism of the brain simulation. None of the state-of-the-art brain phantoms allow such anatomical rendering of three brain compartments. Some represent only GM and WM, others only the striatum. Moreover, they typically have a poor anatomical yield, showing a reduced depth of the sulci and a not very faithful reproduction of the cerebral convolutions. The ability to simulate the three brain compartments simultaneously with greater accuracy, as well as the possibility of carrying out multimodality studies (PET/CT, PET/MRI), which represent the frontier of diagnostic imaging, give this device cutting-edge prospective characteristics. The effort to further customize 3DP technology for these applications is expected to increase significantly in the coming years

Automatic segmentation of Nucleus Accumbens

Segmentation of subcortical structures in the brain has become an increasingly important topic in contemporary medicine. The ability to effi ciently isolate different regions of the human brain has allowed doctors and technicians to become more e fficient in the diagnosis of mental disorders and the evaluation of the patient conditions. An area of the brain whose possible segmentation has received particular attention is the Nucleus Accumbens, which is believed to play a central role in the reward circuit. In fact, studies of volumetric brain magnetic resonance imaging (MRI) have shown neuroanatomical abnormalities of this structure in adult attention defficit/hyperactivity disorder (ADHD), and speci cally a smaller average volume of the region. The use of a reliable automated segmentation method would therefore represent an extremely helpful and e fficient tool for identifying this disorder, especially when compared to manual volume labeling methods, which often turn out to be tedious and extremely time-consuming. However, automatic segmentation of the Accumbens is extremely di fficult to obtain, due to the lack of contrast with the surrounding structures. This means that most conventional segmentation methods are useless for this purpose, and makes the segmentation method selection a very delicate procedure. Consequently, the main objective of the thesis is the implementation of a robust algorithm for segmenting the Nucleus Accumbens structure. The research project aims to apply pre-existing segmentation methods to the Nucleus Accumbens, moving then to an evaluation of such methods and an estimation of how e ffective they are. Diff erent segmentation methods were used for this purpose; firstly, the standard Atlas Segmentation Approach was used, showing generally poor results paired with long computational times and high complexity. Moreover, this method has shown potential problems in the individuation of the correct region, leading, in some cases, to completely wrong segmentations. In addition to the fi rst method, Multi Atlas Segmentation and Adaptive Multi Atlas Segmentation methods have been implemented. The results have shown improved accuracy and better performance than the original method. Judging by the results, the segmentation of the Nucleus Accumbens has proven to be an extremely complicated task, both for the dimension of the structure itself and for the lack of contrast with the surrounding structures. In order to improve detection accuracy, combination of multiple methods is necessary, as using a single method for the segmentation process can lead to an incorrect labeling

Visual attention models and arse representations for morphometrical image analysis

Abstract. Medical diagnosis, treatment, follow-up and research activities are nowadays strongly supported on different types of diagnostic images, whose main goal is to provide an useful exchange of medical knowledge. This multi-modal information needs to be processed in order to extract information exploitable within the context of a particular medical task. In despite of the relevance of these complementary sources of medical knowledge, medical images are rarely further processed in actual clinical practice, so the specialists take decisions only based in the raw data. A new trend in the development of medical image processing and analysis tools follows the idea of biologically-inspired methods, which resemble the performance of the human vision system. Visual attention models and sparse representations are examples of this tendency. Based on this, the aim of this thesis was the development of a set of computational methods for automatic morph metrical analysis, combining the relevant region extraction power of visual attention models with the incorporation of a priori information capabilities of sparse representations. The combination of these biologically inspired tools with common machine learning techniques allowed the identification of visual patterns relevant for pathology discrimination, improving the accuracy and interpretability of morph metric measures and comparisons. After extensive validations with different image data sets, the computational methods proposed in this thesis seems to be promising tools for the definition of anatomical biomarkers, based on visual pattern analysis, and suitable for patient's diagnosis, prognosis and follow-up.Las actividades de diagnóstico, tratamiento, seguimiento e investigación en medicina están actualmente soportadas en diferentes clases de imágenes diagnósticas, cuyo objetivo principal es el de proveer un intercambio efectivo de conocimiento médico. Esta información multimodal necesita ser procesada con el objetivo de extraer información aprovechable en el contexto de una tarea médica particular. A pesar de la relevancia de estas fuentes complementarias de información clínica, las imágenes médicas son raramente procesadas en la práctica clínica actual, de forma que los especialistas sólo toman decisiones basados en los datos crudos. Una nueva tendencia en el desarrollo de herramientas de análisis y procesamiento de imágenes médicas persigue la idea de métodos biológicamente inspirados, que se asemejan al sistema de visión humana. Son ejemplos de esta tendencia los modelos de atención visual y las representaciones escasas (sparse representations). Con base en esto, el objetivo de esta tesis fue el desarrollo de un conjunto de métodos computacionales para soportar automáticamente los análisis morfo métricos, combinando el poder de extracción de regiones relevantes de los modelos de atención visual junto con la capacidad de incorporación de información a priori de las representaciones escasas. La combinación de estos métodos biológicamente inspirados con técnicas de aprendizaje de maquina facilito la identificación de patrones visuales relevantes para discriminar patologías cerebrales, mejorando la precisión e interpretabilidad de las medidas y comparaciones morfo métricas. Después de extensivas validaciones con diferentes conjuntos de imágenes, los métodos computacionales propuestos en esta tesis se perfilan como herramientas prometedoras para la definición de biomarcadores anatómicos, basados en el análisis visual de patrones, y convenientes para el diagnóstico, pronóstico y seguimiento del paciente.Doctorad

Prior information for brain parcellation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.Includes bibliographical references (p. 171-184).To better understand brain disease, many neuroscientists study anatomical differences between normal and diseased subjects. Frequently, they analyze medical images to locate brain structures influenced by disease. Many of these structures have weakly visible boundaries so that standard image analysis algorithms perform poorly. Instead, neuroscientists rely on manual procedures, which are time consuming and increase risks related to inter- and intra-observer reliability [53]. In order to automate this task, we develop an algorithm that robustly segments brain structures. We model the segmentation problem in a Bayesian framework, which is applicable to a variety of problems. This framework employs anatomical prior information in order to simplify the detection process. In this thesis, we experiment with different types of prior information such as spatial priors, shape models, and trees describing hierarchical anatomical relationships. We pose a maximum a posteriori probability estimation problem to find the optimal solution within our framework. From the estimation problem we derive an instance of the Expectation Maximization algorithm, which uses an initial imperfect estimate to converge to a good approximation.(cont.) The resulting implementation is tested on a variety of studies, ranging from the segmentation of the brain into the three major brain tissue classes, to the parcellation of anatomical structures with weakly visible boundaries such as the thalamus or superior temporal gyrus. In general, our new method performs significantly better than other :standard automatic segmentation techniques. The improvement is due primarily to the seamless integration of medical image artifact correction, alignment of the prior information to the subject, detection of the shape of anatomical structures, and representation of the anatomical relationships in a hierarchical tree.by Kilian Maria Pohl.Ph.D

Adaptive processing of thin structures to augment segmentation of dual-channel structural MRI of the human brain

This thesis presents a method for the segmentation of dual-channel structural magnetic resonance imaging (MRI) volumes of the human brain into four tissue classes. The state-of-the-art FSL FAST segmentation software (Zhang et al., 2001) is in widespread clinical use, and so it is considered a benchmark. A significant proportion of FAST’s errors has been shown to be localised to cortical sulci and blood vessels; this issue has driven the developments in this thesis, rather than any particular clinical demand. The original theme lies in preserving and even restoring these thin structures, poorly resolved in typical clinical MRI. Bright plate-shaped sulci and dark tubular vessels are best contrasted from the other tissues using the T2- and PD-weighted data, respectively. A contrasting tube detector algorithm (based on Frangi et al., 1998) was adapted to detect both structures, with smoothing (based on Westin and Knutsson, 2006) of an intermediate tensor representation to ensure smoothness and fuller coverage of the maps. The segmentation strategy required the MRI volumes to be upscaled to an artificial high resolution where a small partial volume label set would be valid and the segmentation process would be simplified. A resolution enhancement process (based on Salvado et al., 2006) was significantly modified to smooth homogeneous regions and sharpen their boundaries in dual-channel data. In addition, it was able to preserve the mapped thin structures’ intensities or restore them to pure tissue values. Finally, the segmentation phase employed a relaxation-based labelling optimisation process (based on Li et al., 1997) to improve accuracy, rather than more efficient greedy methods which are typically used. The thin structure location prior maps and the resolution-enhanced data also helped improve the labelling accuracy, particularly around sulci and vessels. Testing was performed on the aged LBC1936 clinical dataset and on younger brain volumes acquired at the SHEFC Brain Imaging Centre (Western General Hospital, Edinburgh, UK), as well as the BrainWeb phantom. Overall, the proposed methods rivalled and often improved segmentation accuracy compared to FAST, where the ground truth was produced by a radiologist using software designed for this project. The performance in pathological and atrophied brain volumes, and the differences with the original segmentation algorithm on which it was based (van Leemput et al., 2003), were also examined. Among the suggestions for future development include a soft labelling consensus formation framework to mitigate rater bias in the ground truth, and contour-based models of the brain parenchyma to provide additional structural constraints

Towards Precision Psychiatry: gray Matter Development And Cognition In Adolescence

Precision Psychiatry promises a new era of optimized psychiatric diagnosis and treatment through comprehensive, data-driven patient stratification. Among the core requirements towards that goal are: 1) neurobiology-guided preprocessing and analysis of brain imaging data for noninvasive characterization of brain structure and function, and 2) integration of imaging, genomic, cognitive, and clinical data in accurate and interpretable predictive models for diagnosis, and treatment choice and monitoring. In this thesis, we shall touch on specific aspects that fit under these two broad points. First, we investigate normal gray matter development around adolescence, a critical period for the development of psychopathology. For years, the common narrative in human developmental neuroimaging has been that gray matter declines in adolescence. We demonstrate that different MRI-derived gray matter measures exhibit distinct age and sex effects and should not be considered equivalent, as has often been done in the past, but complementary. We show for the first time that gray matter density increases from childhood to young adulthood, in contrast with gray matter volume and cortical thickness, and that females, who are known to have lower gray matter volume than males, have higher density throughout the brain. A custom preprocessing pipeline and a novel high-resolution gray matter parcellation were created to analyze brain scans of 1189 youths collected as part of the Philadelphia Neurodevelopmental Cohort. This work emphasizes the need for future studies combining quantitative histology and neuroimaging to fully understand the biological basis of MRI contrasts and their derived measures. Second, we use the same gray matter measures to assess how well they can predict cognitive performance. We train mass-univariate and multivariate models to show that gray matter volume and density are complementary in their ability to predict performance. We suggest that parcellation resolution plays a big role in prediction accuracy and that it should be tuned separately for each modality for a fair comparison among modalities and for an optimal prediction when combining all modalities. Lastly, we introduce rtemis, an R package for machine learning and visualization, aimed at making advanced data analytics more accessible. Adoption of accurate and interpretable machine learning methods in basic research and medical practice will help advance biomedical science and make precision medicine a reality

Probabilistic partial volume modelling of biomedical tomographic image data

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI