Image Processing and Analysis for Preclinical and Clinical Applications

Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

NOVEL STRATEGIES FOR THE MORPHOLOGICAL AND BIOMECHANICAL ANALYSIS OF THE CARDIAC VALVES BASED ON VOLUMETRIC CLINICAL IMAGES

This work was focused on the morphological and biomechanical analysis of the heart valves exploiting the volumetric data. Novel methods were implemented to perform cardiac valve structure and sub-structure segmentation by defining long axis planes evenly rotated around the long axis of the valve. These methods were exploited to successfully reconstruct the 3D geometry of the mitral, tricuspid and aortic valve structures. Firstly, the reconstructed models were used for the morphological analysis providing a detailed description of the geometry of the valve structures, also computing novel indexes that could improve the description of the valvular apparatus and help their clinical assessment. Additionally, the models obtained for the mitral valve complex were adopted for the development of a novel biomechanical approach to simulate the systolic closure of the valve, relying on highly-efficient mass-spring models thus obtaining a good trade-off between the accuracy and the computational cost of the numerical simulations. In specific: \u2022 First, an innovative and semi-automated method was implemented to generate the 3D model of the aortic valve and of its calcifications, to quantitively describe its 3D morphology and to compute the anatomical aortic valve area (AVA) based on multi-detector computed tomography images. The comparison of the obtained results vs. effective AVA measurements showed a good correlation. Additionally, these methods accounted for asymmetries or anatomical derangements, which would be difficult to correctly capture through either effective AVA or planimetric AVA. \u2022 Second, a tool to quantitively assess the geometry of the tricuspid valve during the cardiac cycle using multidetector CT was developed, in particular focusing on the 3D spatial relationship between the tricuspid annulus and the right coronary artery. The morphological analysis of the annulus and leaflets confirmed data reported in literature. The qualitative and quantitative analysis of the spatial relationship could standardize the analysis protocol and be pivotal in the procedure planning of the percutaneous device implantation that interact with the tricuspid annulus. \u2022 Third, we simulated the systolic closure of three patient specific mitral valve models, derived from CMR datasets, by means of the mass spring model approach. The comparison of the obtained results vs. finite element analyses (considered as the gold-standard) was performed tuning the parameters of the mass spring model, so to obtain the best trade-off between computational expense and accuracy of the results. A configuration mismatch between the two models lower than two times the in-plane resolution of starting imaging data was yielded using a mass spring model set-up that requires, on average, only ten minutes to simulate the valve closure. \u2022 Finally, in the last chapter, we performed a comprehensive analysis which aimed at exploring the morphological and mechanical changes induced by the myxomatous pathologies in the mitral valve tissue. The analysis of mitral valve thickness confirmed the data and patterns reported in literature, while the mechanical test accurately described the behavior of the pathological tissue. A preliminary implementation of this data into finite element simulations suggested that the use of more reliable patient-specific and pathology-specific characterization of the model could improve the realism and the accuracy of the biomechanical simulations

Towards Patient Specific Mitral Valve Modelling via Dynamic 3D Transesophageal Echocardiography

Mitral valve disease is a common pathologic problem occurring increasingly in an aging population, and many patients suffering from mitral valve disease require surgical intervention. Planning an interventional approach from diagnostic imaging alone remains a significant clinical challenge. Transesophageal echocardiography (TEE) is the primary imaging modality used diagnostically, it has limitations in image quality and field-of-view. Recently, developments have been made towards modelling patient-specific deformable mitral valves from TEE imaging, however, a major barrier to producing accurate valve models is the need to derive the leaflet geometry through segmentation of diagnostic TEE imaging. This work explores the development of volume compounding and automated image analysis to more accurately and quickly capture the relevant valve geometry needed to produce patient-specific mitral valve models. Volume compounding enables multiple ultrasound acquisitions from different orientations and locations to be aligned and blended to form a single volume with improved resolution and field-of-view. A series of overlapping transgastric views are acquired that are then registered together with the standard en-face image and are combined using a blending function. The resulting compounded ultrasound volumes allow the visualization of a wider range of anatomical features within the left heart, enhancing the capabilities of a standard TEE probe. In this thesis, I first describe a semi-automatic segmentation algorithm based on active contours designed to produce segmentations from end-diastole suitable for deriving 3D printable molds. Subsequently I describe the development of DeepMitral, a fully automatic segmentation pipeline which leverages deep learning to produce very accurate segmentations with a runtime of less than ten seconds. DeepMitral is the first reported method using convolutional neural networks (CNNs) on 3D TEE for mitral valve segmentations. The results demonstrate very accurate leaflet segmentations, and a reduction in the time and complexity to produce a patient-specific mitral valve replica. Finally, a real-time annulus tracking system using CNNs to predict the annulus coordinates in the spatial frequency domain was developed. This method facilitates the use of mitral annulus tracking in real-time guidance systems, and further simplifies mitral valve modelling through the automatic detection of the annulus, which is a key structure for valve quantification, and reproducing accurate leaflet dynamics

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

Applying artificial intelligence to big data in hepatopancreatic and biliary surgery: a scoping review

Aim: Artificial Intelligence (AI) and its applications in healthcare are rapidly developing. The healthcare industry generates ever-increasing volumes of data that should be used to improve patient care. This review aims to examine the use of AI and its applications in hepatopancreatic and biliary (HPB) surgery, highlighting studies leveraging large datasets.Methods: A PRISMA-ScR compliant scoping review using Medline and Google Scholar databases was performed (5th August 2022). Studies focusing on the development and application of AI to HPB surgery were eligible for inclusion. We undertook a conceptual mapping exercise to identify key areas where AI is under active development for use in HPB surgery. We considered studies and concepts in the context of patient pathways - before surgery (including diagnostics), around the time of surgery (supporting interventions) and after surgery (including prognostication).Results: 98 studies were included. Most studies were performed in China or the USA (n = 45). Liver surgery was the most common area studied (n = 51). Research into AI in HPB surgery has increased rapidly in recent years, with almost two-thirds published since 2019 (61/98). Of these studies, 11 have focused on using “big data” to develop and apply AI models. Nine of these studies came from the USA and nearly all focused on the application of Natural Language Processing. We identified several critical conceptual areas where AI is under active development, including improving preoperative optimization, image guidance and sensor fusion-assisted surgery, surgical planning and simulation, natural language processing of clinical reports for deep phenotyping and prediction, and image-based machine learning.Conclusion: Applications of AI in HPB surgery primarily focus on image analysis and computer vision to address diagnostic and prognostic uncertainties. Virtual 3D and augmented reality models to support complex HPB interventions are also under active development and likely to be used in surgical planning and education. In addition, natural language processing may be helpful in the annotation and phenotyping of disease, leading to new scientific insights

Navigation system based in motion tracking sensor for percutaneous renal access

Tese de Doutoramento em Engenharia BiomédicaMinimally-invasive kidney interventions are daily performed to diagnose and treat several renal diseases. Percutaneous renal access (PRA) is an essential but challenging stage for most of these procedures, since its outcome is directly linked to the physician’s ability to precisely visualize and reach the anatomical target. Nowadays, PRA is always guided with medical imaging assistance, most frequently using X-ray based imaging (e.g. fluoroscopy). Thus, radiation on the surgical theater represents a major risk to the medical team, where its exclusion from PRA has a direct impact diminishing the dose exposure on both patients and physicians. To solve the referred problems this thesis aims to develop a new hardware/software framework to intuitively and safely guide the surgeon during PRA planning and puncturing. In terms of surgical planning, a set of methodologies were developed to increase the certainty of reaching a specific target inside the kidney. The most relevant abdominal structures for PRA were automatically clustered into different 3D volumes. For that, primitive volumes were merged as a local optimization problem using the minimum description length principle and image statistical properties. A multi-volume Ray Cast method was then used to highlight each segmented volume. Results show that it is possible to detect all abdominal structures surrounding the kidney, with the ability to correctly estimate a virtual trajectory. Concerning the percutaneous puncturing stage, either an electromagnetic or optical solution were developed and tested in multiple in vitro, in vivo and ex vivo trials. The optical tracking solution aids in establishing the desired puncture site and choosing the best virtual puncture trajectory. However, this system required a line of sight to different optical markers placed at the needle base, limiting the accuracy when tracking inside the human body. Results show that the needle tip can deflect from its initial straight line trajectory with an error higher than 3 mm. Moreover, a complex registration procedure and initial setup is needed. On the other hand, a real-time electromagnetic tracking was developed. Hereto, a catheter was inserted trans-urethrally towards the renal target. This catheter has a position and orientation electromagnetic sensor on its tip that function as a real-time target locator. Then, a needle integrating a similar sensor is used. From the data provided by both sensors, one computes a virtual puncture trajectory, which is displayed in a 3D visualization software. In vivo tests showed a median renal and ureteral puncture times of 19 and 51 seconds, respectively (range 14 to 45 and 45 to 67 seconds). Such results represent a puncture time improvement between 75% and 85% when comparing to state of the art methods. 3D sound and vibrotactile feedback were also developed to provide additional information about the needle orientation. By using these kind of feedback, it was verified that the surgeon tends to follow a virtual puncture trajectory with a reduced amount of deviations from the ideal trajectory, being able to anticipate any movement even without looking to a monitor. Best results show that 3D sound sources were correctly identified 79.2 ± 8.1% of times with an average angulation error of 10.4º degrees. Vibration sources were accurately identified 91.1 ± 3.6% of times with an average angulation error of 8.0º degrees. Additionally to the EMT framework, three circular ultrasound transducers were built with a needle working channel. One explored different manufacture fabrication setups in terms of the piezoelectric materials, transducer construction, single vs. multi array configurations, backing and matching material design. The A-scan signals retrieved from each transducer were filtered and processed to automatically detect reflected echoes and to alert the surgeon when undesirable anatomical structures are in between the puncture path. The transducers were mapped in a water tank and tested in a study involving 45 phantoms. Results showed that the beam cross-sectional area oscillates around the ceramics radius and it was possible to automatically detect echo signals in phantoms with length higher than 80 mm. Hereupon, it is expected that the introduction of the proposed system on the PRA procedure, will allow to guide the surgeon through the optimal path towards the precise kidney target, increasing surgeon’s confidence and reducing complications (e.g. organ perforation) during PRA. Moreover, the developed framework has the potential to make the PRA free of radiation for both patient and surgeon and to broad the use of PRA to less specialized surgeons.Intervenções renais minimamente invasivas são realizadas diariamente para o tratamento e diagnóstico de várias doenças renais. O acesso renal percutâneo (ARP) é uma etapa essencial e desafiante na maior parte destes procedimentos. O seu resultado encontra-se diretamente relacionado com a capacidade do cirurgião visualizar e atingir com precisão o alvo anatómico. Hoje em dia, o ARP é sempre guiado com recurso a sistemas imagiológicos, na maior parte das vezes baseados em raios-X (p.e. a fluoroscopia). A radiação destes sistemas nas salas cirúrgicas representa um grande risco para a equipa médica, aonde a sua remoção levará a um impacto direto na diminuição da dose exposta aos pacientes e cirurgiões. De modo a resolver os problemas existentes, esta tese tem como objetivo o desenvolvimento de uma framework de hardware/software que permita, de forma intuitiva e segura, guiar o cirurgião durante o planeamento e punção do ARP. Em termos de planeamento, foi desenvolvido um conjunto de metodologias de modo a aumentar a eficácia com que o alvo anatómico é alcançado. As estruturas abdominais mais relevantes para o procedimento de ARP, foram automaticamente agrupadas em volumes 3D, através de um problema de optimização global com base no princípio de “minimum description length” e propriedades estatísticas da imagem. Por fim, um procedimento de Ray Cast, com múltiplas funções de transferência, foi utilizado para enfatizar as estruturas segmentadas. Os resultados mostram que é possível detetar todas as estruturas abdominais envolventes ao rim, com a capacidade para estimar corretamente uma trajetória virtual. No que diz respeito à fase de punção percutânea, foram testadas duas soluções de deteção de movimento (ótica e eletromagnética) em múltiplos ensaios in vitro, in vivo e ex vivo. A solução baseada em sensores óticos ajudou no cálculo do melhor ponto de punção e na definição da melhor trajetória a seguir. Contudo, este sistema necessita de uma linha de visão com diferentes marcadores óticos acoplados à base da agulha, limitando a precisão com que a agulha é detetada no interior do corpo humano. Os resultados indicam que a agulha pode sofrer deflexões à medida que vai sendo inserida, com erros superiores a 3 mm. Por outro lado, foi desenvolvida e testada uma solução com base em sensores eletromagnéticos. Para tal, um cateter que integra um sensor de posição e orientação na sua ponta, foi colocado por via trans-uretral junto do alvo renal. De seguida, uma agulha, integrando um sensor semelhante, é utilizada para a punção percutânea. A partir da diferença espacial de ambos os sensores, é possível gerar uma trajetória de punção virtual. A mediana do tempo necessário para puncionar o rim e ureter, segundo esta trajetória, foi de 19 e 51 segundos, respetivamente (variações de 14 a 45 e 45 a 67 segundos). Estes resultados representam uma melhoria do tempo de punção entre 75% e 85%, quando comparados com o estado da arte dos métodos atuais. Além do feedback visual, som 3D e feedback vibratório foram explorados de modo a fornecer informações complementares da posição da agulha. Verificou-se que com este tipo de feedback, o cirurgião tende a seguir uma trajetória de punção com desvios mínimos, sendo igualmente capaz de antecipar qualquer movimento, mesmo sem olhar para o monitor. Fontes de som e vibração podem ser corretamente detetadas em 79,2 ± 8,1% e 91,1 ± 3,6%, com erros médios de angulação de 10.4º e 8.0 graus, respetivamente. Adicionalmente ao sistema de navegação, foram também produzidos três transdutores de ultrassom circulares com um canal de trabalho para a agulha. Para tal, foram exploradas diferentes configurações de fabricação em termos de materiais piezoelétricos, transdutores multi-array ou singulares e espessura/material de layers de suporte. Os sinais originados em cada transdutor foram filtrados e processados de modo a detetar de forma automática os ecos refletidos, e assim, alertar o cirurgião quando existem variações anatómicas ao longo do caminho de punção. Os transdutores foram mapeados num tanque de água e testados em 45 phantoms. Os resultados mostraram que o feixe de área em corte transversal oscila em torno do raio de cerâmica, e que os ecos refletidos são detetados em phantoms com comprimentos superiores a 80 mm. Desta forma, é expectável que a introdução deste novo sistema a nível do ARP permitirá conduzir o cirurgião ao longo do caminho de punção ideal, aumentado a confiança do cirurgião e reduzindo possíveis complicações (p.e. a perfuração dos órgãos). Além disso, de realçar que este sistema apresenta o potencial de tornar o ARP livre de radiação e alarga-lo a cirurgiões menos especializados.The present work was only possible thanks to the support by the Portuguese Science and Technology Foundation through the PhD grant with reference SFRH/BD/74276/2010 funded by FCT/MEC (PIDDAC) and by Fundo Europeu de Desenvolvimento Regional (FEDER), Programa COMPETE - Programa Operacional Factores de Competitividade (POFC) do QREN

Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie - klinische Anwendung bildbasierter in silico Modellierung

Die richtige Therapie zum richtigen Zeitpunkt, nichtinvasiv und patientenindividuell zu identifizieren, ist das Ziel der Präzisionsmedizin. Durch den stetigen Fortschritt sowohl im Bereich der Bildgebung als auch in mathematischen Modellierungstechniken sowie einer zunehmenden Verfügbarkeit von leistungsstarker Informationstechnologie, gewinnen in silico (angelehnt an das Lateinische „in silicio“, also „in silicium“ bzw. im übertragenden Sinne im Computer ablaufende) Modellierungsverfahren eine immer größere Bedeutung auch im Bereich der kardiovaskulären Medizin. Die bildbasierte in silico Modellierung von Hämodynamik und Funktion des Herzens kann dabei einerseits helfen, die diagnostische Aussagekraft unterschiedlicher Bildgebungsmodalitäten zu erweitern, andererseits aber auch, verschiedene Parameter der postinterventionellen bzw. postoperativen Funktion vorherzusagen und so das geeignetste patientenindividuelle Therapieverfahren zu identifizieren. Im Bereich der pädiatrischen Kardiologie, insbesondere bei Patient*innen mit komplexen angeborenen Herzfehlern, ist eine individualisierte Therapieplanung zudem von ganz besonderer Bedeutung. Da die Anatomie des kardiovaskulären Systems in diesem Patientenkollektiv hoch individuell ist, gibt es häufig keine für das jeweilige Krankheitsbild einheitliche Therapie. Die virtuelle Behandlungsplanung bietet hier ein großes Potential für die multimodale Therapiefindung. Die Translation solcher Modellierungsansätze in die Klinik stellt jedoch eine große Hürde dar. Einerseits muss die Genauigkeit der jeweiligen Simulationsmethode quantifiziert und die Methode selbst validiert werden. Dafür benötigt es in der Regel eine hohe Anzahl an Patientendaten, die insbesondere in der Kinderkardiologie, aber auch aufgrund zunehmend strengerer Datenschutzrichtlinien häufig nicht zur Verfügung stehen. Andererseits sind die Simulationsverfahren sehr komplex und verlangen neben einer hohen technischen Expertise auch beachtliche Rechenkapazitäten und -laufzeiten, wodurch sich ihr routinemäßiger Einsatz in der Klinik ebenfalls verkompliziert. Das Problem der hohen Komplexität könnte durch den Einsatz künstlicher Intelligenz (KI) überwunden werden. Fehlende klinische Daten wiederum könnten mittels synthetischer Patientenkohorten augmentiert werden, sodass sowohl für mögliche Validierungsstudien als auch zum Trainieren des maschinellen Algorithmus‘ ein ausreichend großer Datensatz zur Verfügung stünde. In der vorliegenden Habilitationsschrift werden die Inhalte von fünf wissenschaftlichen Arbeiten zum Thema Präzisionsmedizin in der Kinder- und Erwachsenenkardiologie auf Grundlage bildbasierter in silico Modellierung vorgestellt. Dabei wird in Form einer Proof of Concept Studie die prinzipielle Durchführbarkeit der bildbasierten in silico Modellierung am Beispiel verschiedener Parameter der aortalen Hämodynamik gezeigt sowie die Validierung der Methodik gegen den klinischen Goldstandard des Herzkatheters präsentiert. An komplexen Patient*innen aus dem Bereich der Kinderkardiologie wird die bildbasierte in silico Modellierung für eine konkrete klinische Fragestellung angewandt. Zuletzt werden zwei Optimierungsansätze vorgestellt, die einerseits den komplexen Arbeitsablauf der bildbasierten in silico Modellierung mittels KI vereinfachen sowie andererseits das Problem der existierenden klinischen Datenlücken überwinden sollen