EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment.

High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.

BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation.

BACKGROUND AND OBJECTIVE: Recent advances in neural networks and temporal image processing have provided new results and opportunities for vision-based bronchoscopy tracking. However, such progress has been hindered by the lack of comparative experimental data conditions. We address the issue by sharing a novel synthetic dataset, which allows for a fair comparison of methods. Moreover, as incorporating deep learning advances in temporal structures is not yet explored in bronchoscopy navigation, we investigate several neural network architectures for learning temporal information at different levels of subject personalization, providing new insights and results. METHODS: Using our own shared synthetic dataset for bronchoscopy navigation and tracking, we explore deep learning temporal information architectures (Recurrent Neural Networks and 3D convolutions), which have not been fully explored on bronchoscopy tracking, putting a special focus on network efficiency by using a modern backbone (EfficientNet-B0) and ShuffleNet blocks. Finally, we provide a study of different losses for rotation tracking and population modeling schemes (personalized vs. population) for bronchoscopy tracking. RESULTS: Temporal information architectures provide performance improvements, both in position and angle estimation. Additionally, population scheme analysis illustrates the benefits of offering a personalized model, while loss analysis indicates the benefits of using an adequate metric, improving results. We finally compare with a state-of-the-art model obtaining better results both in performance, with  12.2% and  18.7% improvement for position and rotation respectively, and around  67.6% reduction in memory consumption. CONCLUSIONS: Proposed advances in temporal information architectures, loss configuration, and population scheme definition allow for improving the current state of the art in bronchoscopy analysis. Moreover, the publication of the first synthetic dataset allows for further improving bronchoscopy research by enabling proper comparison grounds among methods.

A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients.

BACKGROUND: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans. METHODS: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classification neural network that optimizes a weighted cross-entropy loss trained to discriminate between three different types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using different configuration of parameters were tested. RESULTS: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining different types of image patterns with PE presented a sensitivity, specificity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting pneumonia presented a sensitivity, specificity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia. CONCLUSION: This radiomic diagnostic system was able to identify the different lung imaging patterns and is a first step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. HIGHLIGHTS: Artificial intelligence applied to Q-SPECT/CT is a diagnostic option in patients with contraindications to CTPA or a non-diagnostic test in times of COVID-19.

First record of Pharyngomyia picta (Diptera: Oestridae) parasitizing Cervus elaphus in Sierra Nevada National Park.

The necropsy made to two adult individuals of red deer shot in Sierra Nevada National Park revealed infestation by second- and third-instar larvae of the oestrid Pharyngomyia picta (Meigen 1824). The animals were shot in Dehesa de las Hoyas, about 2000 m above sea level. This is the first record of this parasite in Sierra Nevada National Park. Furthermore, the occurrence of adult P. picta at this altitude could indicate significant changes of the climatic conditions, now matching better the environmental requirements of this bot fly. Its monitoring may, therefore, be of great help for detecting global change signs.

A CT-based Radiomics Signature Is Associated with Response to Immune Checkpoint Inhibitors in Advanced Solid Tumors.

Background Reliable predictive imaging markers of response to immune checkpoint inhibitors are needed. Purpose To develop and validate a pretreatment CT-based radiomics signature to predict response to immune checkpoint inhibitors in advanced solid tumors. Materials and Methods In this retrospective study, a radiomics signature was developed in patients with advanced solid tumors (including breast, cervix, gastrointestinal) treated with anti-programmed cell death-1 or programmed cell death ligand-1 monotherapy from August 2012 to May 2018 (cohort 1). This was tested in patients with bladder and lung cancer (cohorts 2 and 3). Radiomics variables were extracted from all metastases delineated at pretreatment CT and selected by using an elastic-net model. A regression model combined radiomics and clinical variables with response as the end point. Biologic validation of the radiomics score with RNA profiling of cytotoxic cells (cohort 4) was assessed with Mann-Whitney analysis. Results The radiomics signature was developed in 85 patients (cohort 1: mean age, 58 years ± 13 [standard deviation]; 43 men) and tested on 46 patients (cohort 2: mean age, 70 years ± 12; 37 men) and 47 patients (cohort 3: mean age, 64 years ± 11; 40 men). Biologic validation was performed in a further cohort of 20 patients (cohort 4: mean age, 60 years ± 13; 14 men). The radiomics signature was associated with clinical response to immune checkpoint inhibitors (area under the curve [AUC], 0.70; 95% CI: 0.64, 0.77; P < .001). In cohorts 2 and 3, the AUC was 0.67 (95% CI: 0.58, 0.76) and 0.67 (95% CI: 0.56, 0.77; P < .001), respectively. A radiomics-clinical signature (including baseline albumin level and lymphocyte count) improved on radiomics-only performance (AUC, 0.74 [95% CI: 0.63, 0.84; P < .001]; Akaike information criterion, 107.00 and 109.90, respectively). Conclusion A pretreatment CT-based radiomics signature is associated with response to immune checkpoint inhibitors, likely reflecting the tumor immunophenotype. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Summers in this issue.

Segmentation of distal airways using structural analysis.

Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution.

Ultrathin Bronchoscopy with and without Virtual Bronchoscopic Navigation: Influence of Segmentation on Diagnostic Yield.

BACKGROUND: Bronchoscopy is a safe technique for diagnosing peripheral pulmonary lesions (PPLs), and virtual bronchoscopic navigation (VBN) helps guide the bronchoscope to PPLs. OBJECTIVES: We aimed to compare the diagnostic yield of VBN-guided and unguided ultrathin bronchoscopy (UTB) and explore clinical and technical factors associated with better results. We developed a diagnostic algorithm for deciding whether to use VBN to reach PPLs or choose an alternative diagnostic approach. METHODS: We compared diagnostic yield between VBN-UTB (prospective cases) and unguided UTB (historical controls) and analyzed the VBN-UTB subgroup to identify clinical and technical variables that could predict the success of VBN-UTB. RESULTS: Fifty-five cases and 110 controls were included. The overall diagnostic yield did not differ between the VBN-guided and unguided arms (47 and 40%, respectively; p = 0.354). Although the yield was slightly higher for PPLs ≤20 mm in the VBN-UTB arm, the difference was not significant (p = 0.069). No other clinical characteristics were associated with a higher yield in a subgroup analysis, but an 85% diagnostic yield was observed when segmentation was optimal and the PPL was endobronchial (vs. 30% when segmentation was suboptimal and 20% when segmentation was optimal but the PPL was extrabronchial). CONCLUSIONS: VBN-guided UTB is not superior to unguided UTB. A greater impact of VBN-guided over unguided UTB is highly dependent on both segmentation quality and an endobronchial location of the PPL. Segmentation quality should be considered before starting a procedure, when an alternative technique that may improve yield can be chosen, saving time and resources.

Selection of Radiomics Features based on their Reproducibility.

Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.

BronchoX: bronchoscopy exploration software for biopsy intervention planning.

Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors' solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea.

Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation.

RATIONALE: Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways. OBJECTIVES: To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration. METHODS: CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol. MEASUREMENTS AND MAIN RESULTS: Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001). CONCLUSIONS: CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions.

Objective Endoscopic Measurements of Central Airway Stenosis: A Pilot Study.

BACKGROUND: Endoscopic estimation of the degree of stenosis in central airway obstruction is subjective and highly variable. OBJECTIVE: To determine the benefits of using SENSA (System for Endoscopic Stenosis Assessment), an image-based computational software, for obtaining objective stenosis index (SI) measurements among a group of expert bronchoscopists and general pulmonologists. METHODS: A total of 7 expert bronchoscopists and 7 general pulmonologists were enrolled to validate SENSA usage. The SI obtained by the physicians and by SENSA were compared with a reference SI to set their precision in SI computation. We used SENSA to efficiently obtain this reference SI in 11 selected cases of benign stenosis. A Web platform with three user-friendly microtasks was designed to gather the data. The users had to visually estimate the SI from videos with and without contours of the normal and the obstructed area provided by SENSA. The users were able to modify the SENSA contours to define the reference SI using morphometric bronchoscopy. RESULTS: Visual SI estimation accuracy was associated with neither bronchoscopic experience (p = 0.71) nor the contours of the normal and the obstructed area provided by the system (p = 0.13). The precision of the SI by SENSA was 97.7% (95% CI: 92.4-103.7), which is significantly better than the precision of the SI by visual estimation (p < 0.001), with an improvement by at least 15%. CONCLUSION: SENSA provides objective SI measurements with a precision of up to 99.5%, which can be calculated from any bronchoscope using an affordable scalable interface. Providing normal and obstructed contours on bronchoscopic videos does not improve physicians' visual estimation of the SI.

Anatomical medial surfaces with efficient resolution of branches singularities.

Medial surfaces are powerful tools for shape description, but their use has been limited due to the sensibility of existing methods to branching artifacts. Medial branching artifacts are associated to perturbations of the object boundary rather than to geometric features. Such instability is a main obstacle for a confident application in shape recognition and description. Medial branches correspond to singularities of the medial surface and, thus, they are problematic for existing morphological and energy-based algorithms. In this paper, we use algebraic geometry concepts in an energy-based approach to compute a medial surface presenting a stable branching topology. We also present an efficient GPU-CPU implementation using standard image processing tools. We show the method computational efficiency and quality on a custom made synthetic database. Finally, we present some results on a medical imaging application for localization of abdominal pathologies.

WM-DOVA maps for accurate polyp highlighting in colonoscopy: Validation vs. saliency maps from physicians.

We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WM-DOVA (Window Median Depth of Valleys Accumulation) energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice.

Toward online quantification of tracheal stenosis from videobronchoscopy.

PURPOSE: Lack of objective measurement of tracheal obstruction degree has a negative impact on the chosen treatment prone to lead to unnecessary repeated explorations and other scanners. Accurate computation of tracheal stenosis in videobronchoscopy would constitute a breakthrough for this noninvasive technique and a reduction in operation cost for the public health service. METHODS: Stenosis calculation is based on the comparison of the region delimited by the lumen in an obstructed frame and the region delimited by the first visible ring in a healthy frame. We propose a parametric strategy for the extraction of lumen and tracheal ring regions based on models of their geometry and appearance that guide a deformable model. To ensure a systematic applicability, we present a statistical framework to choose optimal parametric values and a strategy to choose the frames that minimize the impact of scope optical distortion. RESULTS: Our method has been tested in 40 cases covering different stenosed tracheas. Experiments report a non- clinically relevant [Formula: see text] of discrepancy in the calculated stenotic area and a computational time allowing online implementation in the operating room. CONCLUSIONS: Our methodology allows reliable measurements of airway narrowing in the operating room. To fully assess its clinical impact, a prospective clinical trial should be done.

Mathematical modeling of G protein-coupled receptor function: what can we learn from empirical and mechanistic models?

Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists.

Estudio tractográfico de la anatomía helicoidal del miocardio ventricular mediante resonancia magnética por tensor de difusión / Helical structure of the cardiac ventricular anatomy assessed by diffusion tensor magnetic resonance imaging with multiresolution tractography

Introducción y objetivos. La integración de la anatomía y la función del miocardio ventricular es fundamental para una completa comprensión de la fisiología cardiaca, lo que podría revelar conocimientos clave para futuros estudios experimentales y procedimientos clínicos. Se han propuesto varios modelos conceptuales de la organización de las fibras miocárdicas, pero la dificultad para automatizar y analizar objetivamente una estructura anatómica tan compleja ha impedido que se llegue a un acuerdo. El objetivo de nuestro estudio es analizar objetivamente la arquitectura de las fibras miocárdicas mediante métodos avanzados de procesamiento gráfico por computadora aplicados a imágenes de resonancia magnética por tensor de difusión. Métodos. Se han realizado reconstrucciones tractográficas automatizadas de datos de imágenes de resonancia magnética por tensor de difusión sin segmentar de corazones provenientes de la base de datos pública de la Johns Hopkins University. Las reconstrucciones a máxima resolución se han construido con 200 semillas y se componen de perfiles calculados sobre el volumen de vectores propios primarios obtenidos del tensor de difusión. También aportamos una nueva técnica de visualización multiescalar para obtener tractografías simplificadas. Esta metodología permite mantener las principales propiedades geométricas de las fibras y descifrar las principales propiedades de la organización arquitectónica del miocardio. Resultados. En el análisis de las tractografías de todo el espectro multiescalar, encontramos una correlación exacta en los detalles de bajo nivel, así como de la conceptualización abstracta de la disposición helicoidal continua de las fibras miocárdicas que conforman la arquitectura ventricular. Conclusiones. El análisis objetivo de la arquitectura miocárdica mediante un método automatizado que incluye el miocardio completo y utiliza diferentes niveles de complejidad tridimensional revela una organización de las fibras en forma de estructura helicoidal continua que conforma ambos ventrículos. Estos datos concuerdan con el modelo de banda ventricular miocárdica descrita por F. Torrent-Guasp (AU)

Introduction and objectives. Deeper understanding of the myocardial structure linking the morphology and function of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Several conceptual models of myocardial fiber organization have been proposed but the lack of an automatic and objective methodology prevented an agreement. We sought to deepen this knowledge through advanced computer graphical representations of the myocardial fiber architecture by diffusion tensor magnetic resonance imaging. Methods. We performed automatic tractography reconstruction of unsegmented diffusion tensor magnetic resonance imaging datasets of canine heart from the public database of the Johns Hopkins University. Full-scale tractographies have been built with 200 seeds and are composed by streamlines computed on the vector field of primary eigenvectors at the diffusion tensor volumes. We also introduced a novel multiscale visualization technique in order to obtain a simplified tractography. This methodology retains the main geometric features of the fiber tracts, making it easier to decipher the main properties of the architectural organization of the heart. Results. Output analysis of our tractographic representations showed exact correlation with low-level details of myocardial architecture, but also with the more abstract conceptualization of a continuous helical ventricular myocardial fiber array. Conclusions. Objective analysis of myocardial architecture by an automated method, including the entire myocardium and using several 3-dimensional levels of complexity, reveals a continuous helical myocardial fiber arrangement of both right and left ventricles, supporting the anatomical model of the helical ventricular myocardial band described by F. Torrent-Guasp (AU)

Mechanistic analysis of the function of agonists and allosteric modulators: reconciling two-state and operational models.

Two-state and operational models of both agonism and allosterism are compared to identify and characterize common pharmacological parameters. To account for the receptor-dependent basal response, constitutive receptor activity is considered in the operational models. By arranging two-state models as the fraction of active receptors and operational models as the fractional response relative to the maximum effect of the system, a one-by-one correspondence between parameters is found. The comparative analysis allows a better understanding of complex allosteric interactions. In particular, the inclusion of constitutive receptor activity in the operational model of allosterism allows the characterization of modulators able to lower the basal response of the system; that is, allosteric modulators with negative intrinsic efficacy. Theoretical simulations and overall goodness of fit of the models to simulated data suggest that it is feasible to apply the models to experimental data and constitute one step forward in receptor theory formalism.

Helical structure of the cardiac ventricular anatomy assessed by diffusion tensor magnetic resonance imaging with multiresolution tractography.

INTRODUCTION AND OBJECTIVES: Deeper understanding of the myocardial structure linking the morphology and function of the heart would unravel crucial knowledge for medical and surgical clinical procedures and studies. Several conceptual models of myocardial fiber organization have been proposed but the lack of an automatic and objective methodology prevented an agreement. We sought to deepen this knowledge through advanced computer graphical representations of the myocardial fiber architecture by diffusion tensor magnetic resonance imaging. METHODS: We performed automatic tractography reconstruction of unsegmented diffusion tensor magnetic resonance imaging datasets of canine heart from the public database of the Johns Hopkins University. Full-scale tractographies have been built with 200 seeds and are composed by streamlines computed on the vector field of primary eigenvectors at the diffusion tensor volumes. We also introduced a novel multiscale visualization technique in order to obtain a simplified tractography. This methodology retains the main geometric features of the fiber tracts, making it easier to decipher the main properties of the architectural organization of the heart. RESULTS: Output analysis of our tractographic representations showed exact correlation with low-level details of myocardial architecture, but also with the more abstract conceptualization of a continuous helical ventricular myocardial fiber array. CONCLUSIONS: Objective analysis of myocardial architecture by an automated method, including the entire myocardium and using several 3-dimensional levels of complexity, reveals a continuous helical myocardial fiber arrangement of both right and left ventricles, supporting the anatomical model of the helical ventricular myocardial band described by F. Torrent-Guasp.