Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies

Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.Fil: Maso Talou, Gonzalo Daniel. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Blanco, Pablo Javier. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Ares, Gonzalo Damián. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Departamento de Mecanica. Grupo de Ingeniería Asistida Por Computador; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Guedes Bezerra, Cristiano. Heart Institute (Incor); BrasilFil: Lemos, Pedro A.. Heart Institute (Incor); BrasilFil: Feijóo, Raúl Antonino. Laboratorio Nacional de Computacao Cientifica; Brasi

Autómata de Lattice Boltzmann para modelar la difusión óptica en materiales translúcidos

La interrogación de objetos traslúcidos mediante luz láser en el rango infrarrojo cercano es una técnica para recabar información tomográfica que está siendo usada cada vez más en diagnóstico médico y en inspecciones industriales. En este trabajo se presenta una estrategia para la simulación de la difusión de luz visible en materiales translúcidos basada en el método de Lattice Bolzmann (LBM). LBM es un autómata celular que simula fenómenos de transporte a nivel macroscópico mediante una representación mesoscópica, muy fácil de implementar y altamente paralelizable. En nuestro caso el transporte de fotones en la materia se modela mediante una matriz de colisión y absorción definida en cada celda del dominio espacial simulado. La grilla de soporte es tridimensional y los resultados son visualizados superponiendo los elementos de una malla triangular. El modelo fue validado con datos experimentales medidos en un fantoma de laboratorio. Se presentan también las posibles aplicaciones del autómata en un motor de visualizaciónSociedad Argentina de Informática e Investigación Operativ

Automated lumen segmentation using multi-frame convolutional neural networks in Intravascular Ultrasound datasets

Aims: Assessment of minimum lumen areas in intravascular ultrasound (IVUS) pullbacks is time-consuming and demands adequately trained personnel. In this work, we introduce a novel and fully automated pipeline to segment the lumen boundary in IVUS datasets. Methods and results First, an automated gating is applied to select end-diastolic frames and bypass saw-tooth artefacts. Second, within a machine learning (ML) environment, we automatically segment the lumen boundary using a multi-frame (MF) convolutional neural network (MFCNN). Finally, we use the theory of Gaussian processes (GPs) to regress the final lumen boundary. The dataset consisted of 85 IVUS pullbacks (52 patients). The dataset was partitioned at the pullback-level using 73 pullbacks for training (20 586 frames), 6 pullbacks for validation (1692 frames), and 6 for testing (1692 frames). The degree of overlapping, between the ground truth and ML contours, median (interquartile range, IQR) systematically increased from 0.896 (0.874–0.933) for MF1 to 0.925 (0.911–0.948) for MF11. The median (IQR) of the distance error was also reduced from 3.83 (2.94–4.98)% for MF1 to 3.02 (2.25–3.95)% for MF11-GP. The corresponding median (IQR) in the lumen area error remained between 5.49 (2.50–10.50)% for MF1 and 5.12 (2.15–9.00)% for MF11-GP. The dispersion in the relative distance and area errors consistently decreased as we increased the number of frames, and also when the GP regressor was coupled to the MFCNN output. Conclusion: These results demonstrate that the proposed ML approach is suitable to effectively segment the lumen boundary in IVUS scans, reducing the burden of costly and time-consuming manual delineation.Fil: Ziemer, Paulo G. P.. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Bulant, Carlos Alberto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Orlando, José Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Exactas. Grupo de Plasmas Densos Magnetizados. Provincia de Buenos Aires. Gobernación. Comision de Investigaciones Científicas. Grupo de Plasmas Densos Magnetizados; ArgentinaFil: Maso Talou, Gonzalo D.. University of Auckland; Nueva ZelandaFil: Mansilla Álvarez, Luis A.. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Guedes Bezerra, Cristiano. Universidade de Sao Paulo; BrasilFil: Lemos, Pedro A.. Universidade de Sao Paulo; BrasilFil: García García, Héctor M.. Georgetown University School of Medicine; Estados UnidosFil: Blanco, Pablo J.. Laboratorio Nacional de Computacao Cientifica; Brasi

500.05 Comparison Between Fractional Flow Reserve (FFR) vs. Computational Fractional Flow Reserve Derived from Three-dimensional Intravascular Ultrasound (IVUSFR) and Quantitative Flow Ratio (QFR)

BACKGROUND The determination of the ischemic status of a coronary artery by wireless physiologic assessment derived from angiography has been validated and approved in the US. However, the use ofplain angiography quantitative variables does not add much to thephysiology data since it has low correlation with fractional flowreserve (FFR) and predicts clinical outcomes poorly. Recently, a grayscale intravascular ultrasound (IVUS) derived physiology method(IVUSFR) was developed and showed a good correlation with invasiveFFR by combining the geometric advantages of IVUS with physiology.The aim of this study is to assess the coefficient of correlation (R) ofinvasive FFR compared to IVUSFR and quantitative flow ratio (QFR).METHODS Stable coronary artery disease (CAD) patients with intermediate lesions (i.e. 40?80% of diameter stenosis) were assessed by angiography and IVUS. QFR was derived from the angiography images, andIVUSFR was derived from quantitative IVUS data using computationalfluid dynamics. Coefficient of correlation (R) was used in this report.RESULTS Twenty-four patients with 34 lesions were included in theanalysis. The IVUSFR, invasive FFR, Vessel QFR fixed flow (vQFRf),and Vessel QFR contrast flow (vQFRc) values varied from 0.52 to 1.00,0.71 to 0.99, 0.55 to 1.00, and 0.34 to 1.00, respectively. The coefficient of correlation (R) of FFR vs. IVUSFR was 0.79; FFR vs. vQFRf was0.72; FFR vs. vQFRc was 0.65 (Figure).CONCLUSION Compared to invasive FFR, IVUSFR and vQFRf showed asimilar coefficient of correlation and were better than vQFR contrast flowFil: Kajita, Alexandre. Medstart; Estados UnidosFil: Bezerra, Cristiano Guedes. Universidade Federal da Bahia; BrasilFil: Ozaki, Yuichi. Medstart; Estados UnidosFil: Dan, Kazuhiro. Medstart; Estados UnidosFil: Melaku, Gebremedhin D.. Medstart; Estados UnidosFil: Pinton, Fabio A.. Universidade de Sao Paulo; BrasilFil: Falcão, Breno A. A.. Hospital of Messejana; BrasilFil: Mariani, José. Universidade de Sao Paulo; BrasilFil: Bulant, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. National Laboratory For Scientific Computing; BrasilFil: Maso Talou, Gonzalo Daniel. National Laboratory For Scientific Computing; BrasilFil: Esteves, Antonio. Universidade de Sao Paulo; BrasilFil: Blanco, Pablo Javier. National Laboratory For Scientific Computing; BrasilFil: Waksman, Ron. Medstart; Estados UnidosFil: Garcia Garcia, Hector M.. Medstart; Estados UnidosFil: Lemons, Pedro Alves. Universidade de Sao Paulo; Brasi

Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies

Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed

Improving Cardiac Phase Extraction in IVUS Studies by Integration of Gating Methods

Goal: Coronary intravascular ultrasound (IVUS) is a fundamental imaging technique for atherosclerotic plaque assessment. However, volume-based data retrieved from IVUS studies can be misleading due to the artifacts generated by the cardiac motion, hindering diagnostic, and visualization of the vessel condition. Then, we propose an image-based gating method that improves the performance of the preexisting methods, delivering a gating in an appropriate time for clinical practice. Methods: We propose a fully automatic method to synergically integrate motion signals from different gating methods to improve the cardiac phase estimation. Additionally, we present a local extrema identification method that provides a more accurate extraction of a cardiac phase and, also, a scheme for multiple phase extraction mandatory for elastography-type studies. Results: A comparison with three state-of-the-art methods is performed over 61 in-vivo IVUS studies including a wide range of physiological situations. The results show that the proposed strategy offers: 1) a more accurate cardiac phase extraction; 2) a lower frame oversampling and/or omission in the extracted phase data (error of 1.492 ± 0.977 heartbeats per study, mean ± SD); 3) a more accurate and robust heartbeat period detection with a Bland-Altman coefficient of reproducibility (RPC) of 0.23 s, while the second closest method presents an RPC of 0.36 s. Significance: The integration of motion signals performed by our method shown an improvement of the gating accuracy and reliability.Fil: Maso Talou, Gonzalo D.. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Larrabide, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Centro de la Provincia de Buenos Aires; ArgentinaFil: Blanco, Pablo Javier. Laboratorio Nacional de Computacao Cientifica; BrasilFil: Bezerra, Cristiano Guedes. Universidade de Sao Paulo; BrasilFil: Lemos, Pedro A.. Universidade de Sao Paulo; BrasilFil: Feijóo, Raúl Antonino. Laboratorio Nacional de Computacao Cientifica; Brasi

Quantifying changes in shoulder orientation between the prone and supine positions from magnetic resonance imaging

Background: Predicting breast tissue motion using biomechanical models can provide navigational guidance during breast cancer treatment procedures. These models typically do not account for changes in posture between procedures. Difference in shoulder position can alter the shape of the pectoral muscles and breast. A greater understanding of the differences in the shoulder orientation between prone and supine could improve the accuracy of breast biomechanical models. Methods: 19 landmarks were placed on the sternum, clavicle, scapula, and humerus of the shoulder girdle in prone and supine breast MRIs (N = 10). These landmarks were used in an optimization framework to fit subject-specific skeletal models and compare joint angles of the shoulder girdle between these positions. Findings: The mean Euclidean distance between joint locations from the fitted skeletal model and the manually identified joint locations was 15.7 mm ± 2.7 mm. Significant differences were observed between prone and supine. Compared to supine position, the shoulder girdle in the prone position had the lateral end of the clavicle in more anterior translation (i.e., scapula more protracted) (P \u3c 0.05), the scapula in more protraction (P \u3c 0.01), the scapula in more upward rotation (associated with humerus elevation) (P \u3c 0.05); and the humerus more elevated (P \u3c 0.05) for both the left and right sides. Interpretation: Shoulder girdle orientation was found to be different between prone and supine. These differences would affect the shape of multiple pectoral muscles, which would affect breast shape and the accuracy of biomechanical models

MITEA: a dataset for machine learning segmentation of the left ventricle in 3D echocardiography using subject-specific labels from cardiac magnetic resonance imaging

Segmentation of the left ventricle (LV) in echocardiography is an important task for the quantification of volume and mass in heart disease. Continuing advances in echocardiography have extended imaging capabilities into the 3D domain, subsequently overcoming the geometric assumptions associated with conventional 2D acquisitions. Nevertheless, the analysis of 3D echocardiography (3DE) poses several challenges associated with limited spatial resolution, poor contrast-to-noise ratio, complex noise characteristics, and image anisotropy. To develop automated methods for 3DE analysis, a sufficiently large, labeled dataset is typically required. However, ground truth segmentations have historically been difficult to obtain due to the high inter-observer variability associated with manual analysis. We address this lack of expert consensus by registering labels derived from higher-resolution subject-specific cardiac magnetic resonance (CMR) images, producing 536 annotated 3DE images from 143 human subjects (10 of which were excluded). This heterogeneous population consists of healthy controls and patients with cardiac disease, across a range of demographics. To demonstrate the utility of such a dataset, a state-of-the-art, self-configuring deep learning network for semantic segmentation was employed for automated 3DE analysis. Using the proposed dataset for training, the network produced measurement biases of −9 ± 16 ml, −1 ± 10 ml, −2 ± 5 %, and 5 ± 23 g, for end-diastolic volume, end-systolic volume, ejection fraction, and mass, respectively, outperforming an expert human observer in terms of accuracy as well as scan-rescan reproducibility. As part of the Cardiac Atlas Project, we present here a large, publicly available 3DE dataset with ground truth labels that leverage the higher resolution and contrast of CMR, to provide a new benchmark for automated 3DE analysis. Such an approach not only reduces the effect of observer-specific bias present in manual 3DE annotations, but also enables the development of analysis techniques which exhibit better agreement with CMR compared to conventional methods. This represents an important step for enabling more efficient and accurate diagnostic and prognostic information to be obtained from echocardiography

QU-BraTS : MICCAI BraTS 2020 Challenge on QuantifyingUncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019-2020 task on uncertainty quantification (QU-BraTS), and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions, and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentages of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, and hence highlight the need for uncertainty quantification in medical image analyses. Our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraT