CRIMSON: An open-source software framework for cardiovascular integrated modelling and simulation.

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization.

A multi-stage neural network approach for coronary 3D reconstruction from uncalibrated X-ray angiography images.

We present a multi-stage neural network approach for 3D reconstruction of coronary artery trees from uncalibrated 2D X-ray angiography images. This method uses several binarized images from different angles to reconstruct a 3D coronary tree without any knowledge of image acquisition parameters. The method consists of a single backbone network and separate stages for vessel centerline and radius reconstruction. The output is an analytical matrix representation of the coronary tree suitable for downstream applications such as hemodynamic modeling of local vessel narrowing (i.e., stenosis). The network was trained using a dataset of synthetic coronary trees from a vessel generator informed by both clinical image data and literature values on coronary anatomy. Our multi-stage network achieved sub-pixel accuracy in reconstructing vessel radius (RMSE = 0.16 ± 0.07 mm) and stenosis radius (MAE = 0.27 ± 0.18 mm), the most important feature used to inform diagnostic decisions. The network also led to 52% and 38% reduction in vessel centerline reconstruction errors compared to a single-stage network and projective geometry-based methods, respectively. Our method demonstrated robustness to overcome challenges such as vessel foreshortening or overlap in the input images. This work is an important step towards automated analysis of anatomic and functional disease severity in the coronary arteries.

Primary Cardiac Sarcoma: Clinical Characteristics and Prognostic Factors over the Past 2 Decades.

BACKGROUND: Primary cardiac sarcomas (PCS) are extremely rare malignant tumors involving the heart. Only isolated case reports have been described in the literature over different periods of time. This pathology has been associated with a dismal prognosis and given its rarity; treatment options are very limited. Furthermore, there are contrasting data about the effectiveness of current treatment modalities in improving the survival of patients with PCS, including surgical resection which is the mainstay of therapy. There is a paucity of data on the epidemiological characteristics of PCS. This study has the objective of investigating the epidemiologic characteristics, survival outcomes, and independent prognostic factors of PCS. METHODS: A total of 362 patients were ultimately registered in our study from the Surveillance, Epidemiology, and End Results (SEER) database. The study period was from 2000 to 2017. Demographics such as clinical characteristics, overall mortality (OM), and PCS-specific mortality (CSM) were taken into account. A p value of <0.1 in the univariate analysis leads to the incorporation of the variable into multivariate analysis adjusting for covariates. Adverse prognostic factors were represented by a Hazard Ratio (HR) greater than one. The five-year survival analysis was carried out using the Kaplan-Meier method and the log-rank test was used to compare survival curves. RESULTS: Crude analysis revealed a high OM in age 80+ (HR = 5.958, 95% CI 3.357-10.575, p < 0.001), followed by age 60-79 (HR = 1.429, 95% CI 1.028-1.986, p = 0.033); and PCS with distant metastases (HR = 1.888, 95% CI 1.389-2.566, p < 0.001). Patients that underwent surgical resection of the primary tumor and patients with malignant fibrous histiocytomas (HR = 0.657, 95% CI 0.455-0.95, p = 0.025) had a better OM (HR = 0.606, 95% CI 0.465-0.791, p < 0.001). The highest cancer-specific mortality was observed in age 80+ (HR = 5.037, 95% CI 2.606-9.736, p < 0.001) and patients with distant metastases (HR = 1.953, 95% CI 1.396-2.733, p < 0.001). Patients with malignant fibrous histiocytomas (HR = 0.572, 95% CI 0.378-0.865, p = 0.008) and those who underwent surgery (HR = 0.581, 95% CI 0.436-0.774, p < 0.001) had a lower CSM. Patients in the age range 80+ (HR = 13.261, 95% CI 5.839-30.119, p < 0.001) and advanced disease with distant metastases (HR = 2.013, 95% CI 1.355-2.99, p = 0.001) were found to have a higher OM in the multivariate analyses adjusting for covariates). Lower OM was found in patients with rhabdomyosarcoma (HR = 0.364, 95% CI 0.154-0.86, p = 0.021) and widowed patients (HR = 0.506, 95% CI 0.263-0.977, p = 0.042). Multivariate cox proportional hazard regression analyses of CSM also revealed higher mortality of the same groups, and lower mortality in patients with Rhabdomyosarcoma. CONCLUSION: In this United States population-based retrospective cohort study using the SEER database, we found that cardiac rhabdomyosarcoma was associated with the lowest CSM and OM. Furthermore, as expected, age and advanced disease at diagnosis were independent factors predicting poor prognosis. Surgical resection of the primary tumor showed lower CSM and OM in the crude analysis but when adjusted for covariates in the multivariate analysis, it did not significantly impact the overall mortality or the cancer-specific mortality. These findings allow for treating clinicians to recognize patients that should be referred to palliative/hospice care at the time of diagnosis and avoid any surgical interventions as they did not show any differences in mortality. Surgical resection, adjuvant chemotherapy, and/or radiation in patients with poor prognoses should be reserved as palliative measures rather than an attempt to cure the disease.

AngioNet: a convolutional neural network for vessel segmentation in X-ray angiography.

Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow.

Keratinocyte Migration in a Three-Dimensional In Vitro Wound Healing Model Co-Cultured with Fibroblasts.

BACKGROUND: Because three-dimensional (3D) models more closely mimic native tissues, one of the goals of 3D in vitro tissue models is to aid in the development and toxicity screening of new drug therapies. In this study, a 3D skin wound healing model comprising of a collagen type I construct with fibrin-filled defects was developed. METHODS: Optical imaging was used to measure keratinocyte migration in the presence of fibroblasts over 7 days onto the fibrin-filled defects. Additionally, cell viability and growth of fibroblasts and keratinocytes was measured using the alamarBlue® assay and changes in the mechanical stiffness of the 3D construct was monitored using compressive indentation testing. RESULTS: Keratinocyte migration rate was significantly increased in the presence of fibroblasts with the cells reaching the center of the defect as early as day 3 in the co-culture constructs compared to day 7 for the control keratinocyte monoculture constructs. Additionally, constructs with the greatest rate of keratinocyte migration had reduced cell growth. When fibroblasts were cultured alone in the wound healing construct, there was a 1.3 to 3.4-fold increase in cell growth and a 1.2 to 1.4-fold increase in cell growth for keratinocyte monocultures. However, co-culture constructs exhibited no significant growth over 7 days. Finally, mechanical testing showed that fibroblasts and keratinocytes had varying effects on matrix stiffness with fibroblasts degrading the constructs while keratinocytes increased the construct's stiffness. CONCLUSION: This 3D in vitro wound healing model is a step towards developing a mimetic construct that recapitulates the complex microenvironment of healing wounds and could aid in the early studies of novel therapeutics that promote migration and proliferation of epithelial cells.