Cardiac motion and deformation estimation in tagged magnetic resonance imaging

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Electrónica Médica)Cardiovascular diseases are the main cause of death in Europe, with an estimate of 4.3 million deaths each year. The assessment of the regional wall deformation is a relevant clinical indicator, and can be used to detect several cardiac lesions. Nowadays, this study can be performed using several image modalities. In the current thesis, we focus on tagged Magnetic Resonance imaging (t-MRI) technique. Such technique allows acquiring images with tags on the myocardium, which deform with the muscle. The present thesis intends to assess the left ventricle (LV) deformation using radial and circumferential strain. To compute such strain values, both endo- and epicardial contours of the LV are required. As such, a new framework to automatically assess the LV function is proposed. This framework presents: (i) an automatic segmentation technique, based on a tag suppression strategy followed by an active contour segmentation method, and (ii) a tracking approach to extract myocardial deformation, based on a non-rigid registration method. The automatic segmentation uses the B-spline Explicit Active Surface framework, which was previously applied in ultra-sound and cine-MRI images. In both cases, a real-time and accurate contour was achieved. Regarding the registration step, starting from a state-of-art approach, termed sequential 2D, we suggest a new method (termed sequential 2D+t), where the temporal information is included on the model. The tracking methods were first tested on synthetic data to study the registration parameters influence. Furthermore, the proposed and original methods were applied on porcine data with myocardial ischemia. Both methods were able to detect dysfunctional regions. A comparison between the strain curve in the sequential 2D and sequential 2D+t strategies was also shown. As conclusion, a smoothing effect in the strain curve was detected in the sequential 2D+t strategy. The validation of the segmentation approach uses a human dataset. A comparison between the manual contour and the proposed segmentation method results was performed. The results, suggest that proposed method has an acceptable performance, removing the tedious task related with manual segmentation and the intra-observer variability. Finally, a comparison between the proposed framework and the currently available commercial software was performed. The commercial software results were obtained from core-lab analysis. An acceptable result (r = 0.601) was achieved when comparing the strain peak values. Importantly, the proposed framework appears to present a more acceptable result.As doenças cardiovasculares são a principal causa de morte na Europa, com aproximadamente 4.7 milhões de mortes por ano. A avaliação da deformação do miocárdio a um nível local é um importante indicador clínico e pode ser usado para a deteção de lesões cardíacas. Este estudo é normalmente realizado usando várias modalidades de imagem médica. Nesta tese, a Resonância Magnética (RM) marcada foi a técnica selecionada. Estas imagens têm marcadores no músculo cardíaco, os quais se deformam com o miocárdio e podem ser usados para o estudo da deformação cardíaca. Nesta tese, pretende-se estudar a deformação radial e circunferencial do ventrículo esquerdo (VE). Assim, um contorno do endo- e epicárdio no VE é essencial. Desta forma, uma ferramenta para o estudo da deformação do VE foi desenvolvida. Esta possui: (i) um método de segmentação automático, usando uma estratégia de supressão dos marcadores, seguido de uma segmentação c um contorno ativo, e (ii) um método de tracking para determinação da deformação cardíaca, baseado em registo não rígido. A segmentação automática utiliza a ferramenta B-spline Explicit Active Surface, que foi previamente aplicada em imagens de ultrassons e cine-RM. Em ambos os casos, uma segmentação em tempo real e com elevada exatidão foi alcançada. Vários esquemas de registo foram apresentados. Neste ponto, começando com uma técnica do estado da arte (designada de sequencial 2D), uma nova metodologia foi proposta (sequencial 2D+t), onde a informação temporal é incorporada no modelo. De forma a analisar a influência dos parâmetros do registo, estes foram estudados num dataset sintético. De seguida, os diferentes esquemas de registo foram testados num dataset suíno com isquemia. Ambos os métodos foram capazes de detetar as regiões disfuncionais. De igual forma, utilizando as curvas de deformação obtidas para cada um dos métodos propostos, foi possível observar uma suavização na direção temporal para o método sequencial 2D+t. Relativamente à segmentação, esta foi validada com um dataset humano. Um contorno manual foi comparado com o obtido pelo método proposto. Os resultados sugerem que a nova estratégia é aceitável, sendo mais rápida do que a realização de um contorno manual e eliminando a variabilidade entre observadores. Por fim, realizou-se uma comparação entre a ferramenta proposta e um software comercial (com análise de core-lab). A comparação entre os valores de pico da deformação exibe uma correlação plausível (r=0.601). Contudo, é importante notar, que a nova ferramenta tende a apresentar um resultado mais aceitável

Fast left ventricle tracking in CMR images using localized anatomical affine optical flow

"Progress in Biomedical Optics and Imaging, vol. 16, nr. 41"In daily cardiology practice, assessment of left ventricular (LV) global function using non-invasive imaging remains central for the diagnosis and follow-up of patients with cardiovascular diseases. Despite the different methodologies currently accessible for LV segmentation in cardiac magnetic resonance (CMR) images, a fast and complete LV delineation is still limitedly available for routine use. In this study, a localized anatomically constrained affine optical flow method is proposed for fast and automatic LV tracking throughout the full cardiac cycle in short-axis CMR images. Starting from an automatically delineated LV in the end-diastolic frame, the endocardial and epicardial boundaries are propagated by estimating the motion between adjacent cardiac phases using optical flow. In order to reduce the computational burden, the motion is only estimated in an anatomical region of interest around the tracked boundaries and subsequently integrated into a local affine motion model. Such localized estimation enables to capture complex motion patterns, while still being spatially consistent. The method was validated on 45 CMR datasets taken from the 2009 MICCAI LV segmentation challenge. The proposed approach proved to be robust and efficient, with an average distance error of 2.1 mm and a correlation with reference ejection fraction of 0.98 (1.9 ± 4.5%). Moreover, it showed to be fast, taking 5 seconds for the tracking of a full 4D dataset (30 ms per image). Overall, a novel fast, robust and accurate LV tracking methodology was proposed, enabling accurate assessment of relevant global function cardiac indices, such as volumes and ejection fraction.The authors acknowledge funding support from FCT - Fundação para a Ciência e Tecnologia, Portugal, in the scope of the PhD grant SFRH/BD/93443/2013 and the project EXPL/BBB-BMD/2473/2013. D. Barbosa would also like to acknowledge the kind support of the Fundação Luso-Americana para o Desenvolvimento (FLAD), which has funded the travel costs for participation at SPIE Medical Imaging 2015.info:eu-repo/semantics/publishedVersio

Automatic 3D aortic annulus sizing by computed tomography in the planning of transcatheter aortic valve implantation

Background: Accurate imaging assessment of aortic annulus (AoA) dimension is paramount to decide on the correct transcatheter heart valve (THV) size for patients undergoing transcatheter aortic valve implantation (TAVI). We evaluated the feasibility and accuracy of a novel automatic framework for multi detector row computed tomography (MDCT)-based TAVI planning. Methods: Among 122 consecutive patients undergoing TAVI and retrospectively reviewed for this study, 104 patients with preoperative MDCT of sufficient quality were enrolled and analyzed with the proposed software. Fully automatic (FA) and semi-automatic (SA) AoA measurements were compared to manual measurements, with both automated and manual-based interobserver variability (IOV) being assessed. Finally, the effect of these measures on hypothetically selected THV size was evaluated against the implanted size, as well as with respect to manually-derived sizes. Results: FA analysis was feasible in 92.3% of the cases, increasing to 100% if using the SA approach. Automatically-extracted measurements showed excellent agreement with manually-derived ones, with small biases and narrow limits of agreement, and comparable to the interobserver agreement. The SA approach presented a statistically lower IOV than manual analysis, showing the potential to reduce interobserver sizing disagreements. Moreover, the automated approaches displayed close agreement with the implanted sizes, similar to the ones obtained by the experts. Conclusion: The proposed automatic framework provides an accurate and robust tool for AoA measurements and THV sizing in patients undergoing TAVI.FCT - Fundação para a Ciência e a Tecnologia, Portugal, and the European Social Found, European Union, through the Programa Operacional Capital Humano (POCH) in the scope of the PhD grants SFRH/BD/93443/2013 (S. Queirós) and SFRH/BD/95438/2013 (P. Morais), and the project ‘PersonalizedNOS (01-0145-FEDER-000013)’ co-funded by Programa Operacional Regional do Norte (QREN), through Fundo Europeu de Desenvolvimento Regional (FEDER)info:eu-repo/semantics/publishedVersio

Fully Automatic 3D-TEE Segmentation for the Planning of Transcatheter Aortic Valve Implantation

A novel fully automatic framework for aortic valve (AV) trunk segmentation in three-dimensional (3-D) transesophageal echocardiography (TEE) datasets is proposed. The methodology combines a previously presented semiautomatic segmentation strategy by using shape-based B-spline Explicit Active Surfaces with two novel algorithms to automate the quantification of relevant AV measures. The first combines a fast rotation-invariant 3-D generalized Hough transform with a vessel-like dark tube detector to initialize the segmentation. After segmenting the AV wall, the second algorithm focuses on aligning this surface with the reference ones in order to estimate the short-axis (SAx) planes (at the left ventricular outflow tract, annulus, sinuses of Valsalva, and sinotubular junction) in which to perform the measurements. The framework has been tested in 20 3-D-TEE datasets with both stenotic and nonstenotic AVs. The initialization algorithm presented a median error of around 3 mm for the AV axis endpoints, with an overall feasibility of 90%. In its turn, the SAx detection algorithm showed to be highly reproducible, with indistinguishable results compared with the variability found between the experts' defined planes. Automatically extracted measures at the four levels showed a good agreement with the experts' ones, with limits of agreement similar to the interobserver variability. Moreover, a validation set of 20 additional stenotic AV datasets corroborated the method's applicability and accuracy. The proposed approach mitigates the variability associated with the manual quantification while significantly reducing the required analysis time (12 s versus 5 to 10 min), which shows its appeal for automatic dimensioning of the AV morphology in 3-D-TEE for the planning of transcatheter AV implantation.This work was supported by the project "ON.2 SR&TD Integrated Program (Norte-07-0124-FEDER-000017)" cofunded by the Programa Operacional Regional do Norte (ON.2- O Novo Norte), Quadro de Referencia Estrategico Nacional, through Fundo Europeu de Desenvolvimento Regional. The work of S. Queiros and P. Morais was supported by the FCT-Fundacao para a Ciencia e a Tecnologia and the European Social Found through the Programa Operacional Capital Humano in the scope of the Ph.D. Grants SFRH/BD/93443/2013 and SFRH/BD/95438/2013, respectively. J. L. Vilaca and J. D'hooge are joint last authors. Asterisk indicates corresponding author.info:eu-repo/semantics/publishedVersio

Automatic strategy for extraction of anthropometric measurements for the diagnostic and evaluation of deformational plagiocephaly from infant’s head models

Deformational Plagiocephaly (DP) refers to an asymmetrical distortion of an infant's skull resulting from external forces applied over time. The diagnosis of this condition is performed using asymmetry indexes that are estimated from specific anatomical landmarks, whose are manually defined on head models acquired using laser scans. However, this manual identification is susceptible to intra-/inter-observer variability, being also time-consuming. Therefore, automatic strategies for the identification of the landmarks and, consequently, extraction of asymmetry indexes, are claimed. A novel pipeline to automatically identify these landmarks on 3D head models and to estimate the relevant cranial asymmetry indexes is proposed. Thus, a template database is created and then aligned with the unlabelled patient through an iterative closest point (ICP) strategy. Here, an initial rigid alignment followed by an affine one are applied to remove global misalignments between each template and the patient. Next, a non-rigid alignment is used to deform the template information to the patient-specific shape. The final position of each landmark is computed as a local weight average of all candidate results. From the identified landmarks, a head's coordinate system is automatically estimated and later used to estimate cranial asymmetry indexes. The proposed framework was evaluated in 15 synthetic infant head's model. Overall, the results demonstrated the accuracy of the identification strategy, with a mean average distance of 2.8 +/- 0.6 mm between the identified landmarks and the ground-truth. Moreover, for the estimation of cranial asymmetry indexes, a performance comparable to the inter-observer variability was achieved.The present submission corresponds to original research work of the authors and has never been submitted elsewhere. Moreover, this work was funded by the project NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Moreover, this work has been also supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. Furthermore, the authors acknowledge FCT, Portugal, and the European Social Found, European Union, for funding support through the "Programa Operacional Capital Humano" (POCH) in the scope of the PhD grants SFRH/BD/136721/2018 (Bruno Oliveira), SFRH/BD/136670/2018 (Helena R. Torres), and SFRH/BD/131545/2017 (Fernando Veloso)

Voxel-based registration of simulated and real patient CBCT data for accurate dental implant pose estimation

"Progress in Biomedical Optics and Imaging, vol. 16, nr. 42"The success of dental implant-supported prosthesis is directly linked to the accuracy obtained during implant's pose estimation (position and orientation). Although traditional impression techniques and recent digital acquisition methods are acceptably accurate, a simultaneously fast, accurate and operator-independent methodology is still lacking. Hereto, an image-based framework is proposed to estimate the patient-specific implant's pose using cone-beam computed tomography (CBCT) and prior knowledge of implanted model. The pose estimation is accomplished in a threestep approach: (1) a region-of-interest is extracted from the CBCT data using 2 operator-defined points at the implant's main axis; (2) a simulated CBCT volume of the known implanted model is generated through Feldkamp-Davis-Kress reconstruction and coarsely aligned to the defined axis; and (3) a voxel-based rigid registration is performed to optimally align both patient and simulated CBCT data, extracting the implant's pose from the optimal transformation. Three experiments were performed to evaluate the framework: (1) an in silico study using 48 implants distributed through 12 tridimensional synthetic mandibular models; (2) an in vitro study using an artificial mandible with 2 dental implants acquired with an i-CAT system; and (3) two clinical case studies. The results shown positional errors of 67±34μm and 108μm, and angular misfits of 0.15±0.08 and 1.4, for experiment 1 and 2, respectively. Moreover, in experiment 3, visual assessment of clinical data results shown a coherent alignment of the reference implant. Overall, a novel image-based framework for implants' pose estimation from CBCT data was proposed, showing accurate results in agreement with dental prosthesis modelling requirements.This work has been supported by FCT – Fundação para a Ciência e Tecnologia in the scope of the Ph.D. grant SFRH/BD/68270/2010, SFRH/BD/93443/2013 and the project EXPL/BBB-BMD/2146/2013.info:eu-repo/semantics/publishedVersio

Personalized dynamic phantom of the right and left ventricles based on patient-specific anatomy for echocardiography studies — Preliminary results

Dynamic phantoms of the heart are becoming a reality, with their use spread across both medical and research fields. Their purpose is to mimic the cardiac anatomy, as well as its motion. This work aims to create a dynamic, ultrasound-compatible, realistic and flexible phantom of the left and right ventricles, with application in the diagnosis, planning, treatment and training in the cardiovascular field for studies using echocardiography. Here, we focus on its design and production with polyvinyl alcohol cryogel (PVA-C), to be assembled with a pump and an electromechanical (E/M) system in a water tank. Based on a patient-specific anatomical model and produced using a 3D printing technique and molding, the PVA-C phantom mimics the ventricles' natural anatomy and material properties, while the pump and E/M systems mimic the natural movements and pressures. The PVA-C phantom was assessed by imaging and measuring it using a four-dimensional ultrasound machine. The PVA-C phantom demonstrated to be a versatile option to produce patient-specific biventricular models, preserving their shape after manufacturing and presenting good echogenic properties. Both chambers were clearly seen in the ultrasound images, together with the interventricular septum and the myocardial wall. Automated left ventricle measures revealed a decrease of its volume with regard to the designed model (98 ml to 74 ml). Overall, the preliminary results are satisfactory and encourage its use for the abovementioned purposesFEDER funds through the Competitiveness Factors Operational Programme (COMPETE), and by National funds through the Foundation for Science and Technology (FCT) under the project POCI -01-0145-FEDER-007038 and EXPL/BBB-BMD/2473/2013, and by the projects NORTE-01-0145-FEDER-000013 and NORTE-01-0145-FEDER-024300, supported by the NORTE 2020, under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). J. Gomes-Fonseca, P. Morais, S. Queirós, and F. Veloso were funded by FCT under the Ph.D. grants PD/BDE/113597/2015, SFRH/BD/95438/2013, SFRH/BD/93443/2013, and SFRH/BD/131545/2017info:eu-repo/semantics/publishedVersio

Deep learning-based detection of anthropometric landmarks in 3D infants head models

Deformational plagiocephaly (DP) is a cranial deformity characterized by an asymmetrical distortion of an infant's skull. The diagnosis and evaluation of DP are performed using cranial asymmetry indexes obtained from cranial measurements, which can be estimated using anthropometric landmarks of the infant's head. However, manual labeling of these landmarks is a time-consuming and tedious task, being also prone to observer variability. In this paper, a novel framework to automatically detect anthropometric landmarks of 3D infant's head models is described. The proposed method is divided into two stages: (i) unfolding of the 3D head model surface; and (ii) landmarks' detection through a deep learning strategy. In the first stage, an unfolding strategy is used to transform the 3D mesh of the head model to a flattened 2D version of it. From the flattened mesh, three 2D informational maps are generated using specific head characteristics. In the second stage, a deep learning strategy is used to detect the anthropometric landmarks in a 3-channel image constructed using the combination of informational maps. The proposed framework was validated in fifteen 3D synthetic models of infant's head, being achieved, in average for all landmarks, a mean distance error of 3.5 mm between the automatic detection and a manually constructed ground-truth. Moreover, the estimated cranial measurements were comparable to the ones obtained manually, without statistically significant differences between them for most of the indexes. The obtained results demonstrated the good performance of the proposed method, showing the potential of this framework in clinical practice.The present submission corresponds to original research work of the authors and has never been submitted elsewhere. Moreover, this work was funded by the project NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). Moreover, this work has been also supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2019. Furthermore, the authors acknowledge FCT, Portugal, and the European Social Found, European Union, for funding support through the "Programa Operacional Capital Humano" (POCH) in the scope of the PhD grants SFRH/BD/136670/2018 (Helena R. Torres), SFRH/BD/136721/2018 (Bruno Oliveira), and SFRH/BD/131545/2017 (Fernando Veloso)

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio