Anatomical curve identification

Methods for capturing images in three dimensions are now widely available, with stereo-photogrammetry and laser scanning being two common approaches. In anatomical studies, a number of landmarks are usually identified manually from each of these images and these form the basis of subsequent statistical analysis. However, landmarks express only a very small proportion of the information available from the images. Anatomically defined curves have the advantage of providing a much richer expression of shape. This is explored in the context of identifying the boundary of breasts from an image of the female torso and the boundary of the lips from a facial image. The curves of interest are characterised by ridges or valleys. Key issues in estimation are the ability to navigate across the anatomical surface in three-dimensions, the ability to recognise the relevant boundary and the need to assess the evidence for the presence of the surface feature of interest. The first issue is addressed by the use of principal curves, as an extension of principal components, the second by suitable assessment of curvature and the third by change-point detection. P-spline smoothing is used as an integral part of the methods but adaptations are made to the specific anatomical features of interest. After estimation of the boundary curves, the intermediate surfaces of the anatomical feature of interest can be characterised by surface interpolation. This allows shape variation to be explored using standard methods such as principal components. These tools are applied to a collection of images of women where one breast has been reconstructed after mastectomy and where interest lies in shape differences between the reconstructed and unreconstructed breasts. They are also applied to a collection of lip images where possible differences in shape between males and females are of interest

Recent advances in 3D printing of biomaterials.

3D Printing promises to produce complex biomedical devices according to computer design using patient-specific anatomical data. Since its initial use as pre-surgical visualization models and tooling molds, 3D Printing has slowly evolved to create one-of-a-kind devices, implants, scaffolds for tissue engineering, diagnostic platforms, and drug delivery systems. Fueled by the recent explosion in public interest and access to affordable printers, there is renewed interest to combine stem cells with custom 3D scaffolds for personalized regenerative medicine. Before 3D Printing can be used routinely for the regeneration of complex tissues (e.g. bone, cartilage, muscles, vessels, nerves in the craniomaxillofacial complex), and complex organs with intricate 3D microarchitecture (e.g. liver, lymphoid organs), several technological limitations must be addressed. In this review, the major materials and technology advances within the last five years for each of the common 3D Printing technologies (Three Dimensional Printing, Fused Deposition Modeling, Selective Laser Sintering, Stereolithography, and 3D Plotting/Direct-Write/Bioprinting) are described. Examples are highlighted to illustrate progress of each technology in tissue engineering, and key limitations are identified to motivate future research and advance this fascinating field of advanced manufacturing

Breast Cancer: Modelling and Detection

This paper reviews a number of the mathematical models used in cancer modelling and then chooses a specific cancer, breast carcinoma, to illustrate how the modelling can be used in aiding detection. We then discuss mathematical models that underpin mammographic image analysis, which complements models of tumour growth and facilitates diagnosis and treatment of cancer. Mammographic images are notoriously difficult to interpret, and we give an overview of the primary image enhancement technologies that have been introduced, before focusing on a more detailed description of some of our own recent work on the use of physics-based modelling in mammography. This theoretical approach to image analysis yields a wealth of information that could be incorporated into the mathematical models, and we conclude by describing how current mathematical models might be enhanced by use of this information, and how these models in turn will help to meet some of the major challenges in cancer detection

Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). 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Validation of the PSIR sequence for the determination of arrhythmogenic channels in ventricular ischemia

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2021-2022. Tutora/Directora: Paz Garré Anguera de Sojo and Sara VázquezIn patients with ventricular tachycardia of ischemic origin, arrhythmogenic channels are the pathway of abnormal tissue activation and their determination in the substrate is a essential factor when treating these cases by radiofrequency ablation. Extracting this information from images obtained by magnetic resonance imaging has great advantages over other more invasive imaging techniques. The most commonly used reconstruction technique in MRI-2D is the Magnitude sequence. Recently, another sequence called Phase Sensitive Inversion Recovery (PSIR) is beginning to be established, which takes into account the polarity of the protons, apart from their magnitude, when generating the image. In this project the level of validity of the PSIR reconstruction sequence to determine the arrhythmogenic channels has been demonstrated, comparing data obtained using this sequence with data obtained using the strongly validated and referenced Magnitude sequence. Data from 21 patients with specific conditions have been used for this study. The images have been segmented and processed in order to extract the parameters that have allowed to solve the question raised by means of a statistical analysis of the information obtained. We have worked with ADAS-3D rendering software to study the cases and have determined the configuration that allows the highest quality in the visualization of PSIR images, specifically the setting of contrast thresholds. From the information provided by the ADAS-3D we selected the information considered relevant for the statistical analysis, descriptive information about the channels and characteristics of the tissue. These data, together with the contrast thresholds set in the study, have been statistically analysed with the RStudio programme. Valuable information has been obtained from the results. The ideal thresholds for studying PSIR images have been found and it has been concluded that there is a considerable similarity between both sequences when interpreting MRI images clinically, although not enough to validate it completely. Regarding the characterisation of channels, a high accuracy in the calculation of their mass has been determined, but a great inaccuracy in their counting. In terms of quantification, identification and classification of ventricular tissue, considerable correlation and acceptable measurement accuracy have been demonstrated

Assessment of a novel patient-specific 3D printed multi-material simulator for endoscopic sinus surgery

Background: Three-dimensional (3D) printing is an emerging tool in the creation of anatomical models for surgical training. Its use in endoscopic sinus surgery (ESS) has been limited because of the difficulty in replicating the anatomical details. Aim: To describe the development of a patient-specific 3D printed multi-material simulator for use in ESS, and to validate it as a training tool among a group of residents and experts in ear-nose-throat (ENT) surgery. Methods: Advanced material jetting 3D printing technology was used to produce both soft tissues and bony structures of the simulator to increase anatomical realism and tactile feedback of the model. A total of 3 ENT residents and 9 ENT specialists were recruited to perform both non-destructive tasks and ESS steps on the model. The anatomical fidelity and the usefulness of the simulator in ESS training were evaluated through specific questionnaires. Results: The tasks were accomplished by 100% of participants and the survey showed overall high scores both for anatomy fidelity and usefulness in training. Dacryocystorhinostomy, medial antrostomy, and turbinectomy were rated as accurately replicable on the simulator by 75% of participants. Positive scores were obtained also for ethmoidectomy and DRAF procedures, while the replication of sphenoidotomy received neutral ratings by half of the participants. Conclusion: This study demonstrates that a 3D printed multi-material model of the sino-nasal anatomy can be generated with a high level of anatomical accuracy and haptic response. This technology has the potential to be useful in surgical training as an alternative or complementary tool to cadaveric dissection

Design of a testing device for an anatomical part of the ascending aorta

Aortic aneurysms are life-threatening pathologies that cause thousands of deaths worldwide. The current main clinical criteria for surgical intervention is aortic diameter, although a large percentage of patients with dissection or rupture has a normal diameter. Computation methods have been adopted to model the biomechanical behaviour of biological tissue in view of adding in the diagnosis of this pathology. Furthermore, experimental testing on aneurismatic aortic tissue has been performed to validate these models. The objective of this study is to integrate com- putational mechanical methods into an innovative experimental test with a specifically designed device where material parameters are obtained by inverse methods assisted by Digital Image Correlation (DIC). Axiomatic Design (AD) is taken into consideration to develop the testing device in a clear, methodical, and efficient way. A case study is analysed, and a patient-specific 3D geometry of an Ascending Thoracic Aortic Aneurysm (ATAA) is obtained by segmenting Computed Tomography Angiography (CTA) images. A methodology is presented by attribut- ing a hyperelastic constitutive model to the geometry and executing Finite Element Analysis (FEA). Future work should rely on real experimental tests where Finite Element Model Up- dating (FEMU) should be adopted to fit the constitutive model more accurately to the actual specimen material.O aneurisma da aorta é uma patologia de risco que provoca milhares de mortes mundialmente. O critério atual para intervenção cirúrgica é o diâmetro da aorta, no entanto, uma grande percentagem de pacientes com dissecção ou rutura da aorta apresenta um diâmetro normal. Métodos computacionais têm sido adotados para modelar o comportamento biomecânico de tecido biológico e auxiliar no diagnóstico desta patologia. Testes experimentais nestes tecidos são executados para validar os modelos. O objetivo deste estudo é um contributo para uma plataforma digital integrando métodos computacionais para o desenvolvimento de um mecan- ismo de ensaio experimental, cuja identificação de parâmetros material deve ser auxiliada pela técnica de correlação digital de imagem 3D. Esta abordagem segue um desenvolvimento de pro- duto orientado por simulação numérica, em que a análise computacional é totalmente integrada como parte do projeto mecânico. Teoria Axiomática de Projeto é tida em consideração para desenvolver o dispositivo de uma forma clara, metódica e eficiente. Um caso de estudo é anal- isado e uma geometria da peça anatómica 3D, específica de um paciente, é obtida através da segmentação de imagens de uma angiotomografia. Uma metodologia é apresentada atribuindo um modelo constitutivo hiperelástico ao material e executando análise de elementos finitos. Como trabalho futuro a identificação dos parametros constitutivos deve ser obtida com recurso a métodos inversos avançados baseados em campos de deformação obtidos por correlação digital de imagem

Medical imaging analysis with artificial neural networks

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging

Investigation of 3DP technology for fabrication of surgical simulation phantoms

The demand for affordable and realistic phantoms for training, in particular for functional endoscopic sinus surgery (FESS), has continuously increased in recent years. Conventional training methods, such as current physical models, virtual simulators and cadavers may have restrictions, including fidelity, accessibility, cost and ethics. In this investigation, the potential of three-dimensional printing for the manufacture of biologically representative simulation materials for surgery training phantoms has been investigated. A characterisation of sinus anatomical elements was performed through CT and micro-CT scanning of a cadaveric sinus portion. In particular, the relevant constituent tissues of each sinus region have been determined. Secondly, feedback force values experienced during surgical cutting have been quantified with an actual surgical instrument, specifically modified for this purpose. Force values from multiple post-mortem subjects and different areas of the paranasal sinuses have been gathered and used as a benchmark for the optimisation of 3D-printing materials. The research has explored the wide range of properties achievable in 3DP through post-processing methods and variation of printing parameters. For this latter element, a machine-vision system has been developed to monitor the 3DP in real time. The combination of different infiltrants allowed the reproduction of force values comparable to those registered from cadaveric human tissue. The internal characteristics of 3D printed samples were shown to influence their fracture behaviour under resection. Realistic appearance under endoscopic conditions has also been confirmed. The utilisation of some of the research has also been demonstrated in another medical (non-surgical) training application. This investigation highlights a number of capabilities, and also limitations, of 3DP for the manufacturing of representative materials for application in surgical training phantoms

Radiogenomics Framework for Associating Medical Image Features with Tumour Genetic Characteristics

Significant progress has been made in the understanding of human cancers at the molecular genetics level and it is providing new insights into their underlying pathophysiology. This progress has enabled the subclassification of the disease and the development of targeted therapies that address specific biological pathways. However, obtaining genetic information remains invasive and costly. Medical imaging is a non-invasive technique that captures important visual characteristics (i.e. image features) of abnormalities and plays an important role in routine clinical practice. Advancements in computerised medical image analysis have enabled quantitative approaches to extract image features that can reflect tumour genetic characteristics, leading to the emergence of ‘radiogenomics’. Radiogenomics investigates the relationships between medical imaging features and tumour molecular characteristics, and enables the derivation of imaging surrogates (radiogenomics features) to genetic biomarkers that can provide alternative approaches to non-invasive and accurate cancer diagnosis. This thesis presents a new framework that combines several novel methods for radiogenomics analysis that associates medical image features with tumour genetic characteristics, with the main objectives being: i) a comprehensive characterisation of tumour image features that reflect underlying genetic information; ii) a method that identifies radiogenomics features encoding common pathophysiological information across different diseases, overcoming the dependence on large annotated datasets; and iii) a method that quantifies radiogenomics features from multi-modal imaging data and accounts for unique information encoded in tumour heterogeneity sub-regions. The present radiogenomics methods advance radiogenomics analysis and contribute to improving research in computerised medical image analysis