Whole heart detailed and quantitative anatomy, myofibre structure and vasculature from X-ray phase-contrast synchrotron radiation-based micro computed tomography

Background

While individual cardiac myocytes only have a limited ability to shorten, the heart efficiently pumps a large volume-fraction thanks to a cell organization in a complex 3D fibre structure. Subclinical subtle cardiac structural remodelling is often present before symptoms arise. Understanding and early detection of these subtle changes is crucial for diagnosis and prevention. Additionally, personalized computational modelling requires knowledge on the multi-scale structure of the whole heart and vessels.

Methods and Results

We developed a rapid acquisition together with visualization and quantification methods of the integrated microstructure of whole in-vitro rodents hearts using synchrotron based X-ray phase-contrast tomography. These images are formed not only by X-ray absorption by the tissue but also by wave propagation phenomena, enhancing structural information, thus allowing to raise tissue contrast to an unprecedented level. We used a (ex-vivo) normal rat heart and fetal rabbit hearts suffering intrauterine growth restriction as a model of subclinical cardiac remodelling to illustrate the strengths and potential of the technique. For comparison, histology and diffusion tensor magnetic resonance imaging was performed.

Conclusions

We have developed a novel, high resolution, image acquisition, and quantification approach to study a whole in-vitro heart at myofibre resolution, providing integrated 3D structural information at microscopic level without any need of tissue slicing and processing. This superior imaging approach opens up new possibilities for a systems approach towards analysing cardiac structure and function, providing rapid acquisition of quantitative microstructure of the heart in a near native state.

3D membrane segmentation and quantification of intact thick cells using cryo soft X-ray transmission microscopy: A pilot study

Structural analysis of biological membranes is important for understanding cell and sub-cellular organelle function as well as their interaction with the surrounding environment. Imaging of whole cells in three dimension at high spatial resolution remains a significant challenge, particularly for thick cells. Cryo-transmission soft X-ray microscopy (cryo-TXM) has recently gained popularity to image, in 3D, intact thick cells (∼10μm) with details of sub-cellular architecture and organization in near-native state. This paper reports a new tool to segment and quantify structural changes of biological membranes in 3D from cryo-TXM images by tracking an initial 2D contour along the third axis of the microscope, through a multi-scale ridge detection followed by an active contours-based model, with a subsequent refinement along the other two axes. A quantitative metric that assesses the grayscale profiles perpendicular to the membrane surfaces is introduced and shown to be linearly related to the membrane thickness. Our methodology has been validated on synthetic phantoms using realistic microscope properties and structure dimensions, as well as on real cryo-TXM data. Results demonstrate the validity of our algorithms for cryo-TXM data analysis.

Estimation of Purkinje trees from electro-anatomical mapping of the left ventricle using minimal cost geodesics

The electrical activation of the heart is a complex physiological process that is essential for the understanding of several cardiac dysfunctions, such as ventricular tachycardia (VT). Nowadays, patient-specific activation times on ventricular chambers can be estimated from electro-anatomical maps, providing crucial information to clinicians for guiding cardiac radio-frequency ablation treatment. However, some relevant electrical pathways such as those of the Purkinje system are very difficult to interpret from these maps due to sparsity of data and the limited spatial resolution of the system. We present here a novel method to estimate these fast electrical pathways from the local activations maps (LATs) obtained from electro-anatomical maps. The location of Purkinje-myocardial junctions (PMJs) is estimated considering them as critical points of a distance map defined by the activation maps, and then minimal cost geodesic paths are computed on the ventricular surface between the detected junctions. Experiments to validate the proposed method have been carried out in simplified and realistic simulated data, showing good performance on recovering the main characteristics of simulated Purkinje networks (e.g. PMJs). A feasibility study with real cases of fascicular VT was also performed, showing promising results.

Anatomical labeling of the Circle of Willis using maximum a posteriori probability estimation

Anatomical labeling of the cerebral arteries forming the Circle of Willis (CoW) enables inter-subject comparison, which is required for geometric characterization and discovering risk factors associated with cerebrovascular pathologies. We present a method for automated anatomical labeling of the CoW by detecting its main bifurcations. The CoW is modeled as rooted attributed relational graph, with bifurcations as its vertices, whose attributes are characterized as points on a Riemannian manifold. The method is first trained on a set of pre-labeled examples, where it learns the variability of local bifurcation features as well as the variability in the topology. Then, the labeling of the target vasculature is obtained as maximum a posteriori probability (MAP) estimate where the likelihood of labeling individual bifurcations is regularized by the prior structural knowledge of the graph they span. The method was evaluated by cross-validation on 50 subjects, imaged with magnetic resonance angiography, and showed a mean detection accuracy of 95%. In addition, besides providing the MAP, the method can rank the labelings. The proposed method naturally handles anatomical structural variability and is demonstrated to be suitable for labeling arterial segments of the CoW.

Computational fluid dynamic simulations of image-based stented coronary bifurcation models

One of the relevant phenomenon associated with in-stent restenosis in coronary arteries is an altered haemodynamics in the stented region. Computational fluid dynamics (CFD) offers the possibility to investigate the haemodynamics at a level of detail not always accessible within experimental techniques. CFD can quantify and correlate the local haemodynamics structures which might lead to in-stent restenosis. The aim of this work is to study the fluid dynamics of realistic stented coronary artery models which replicate the complete clinical procedure of stent implantation. Two cases of pathologic left anterior descending coronary arteries with their bifurcations are reconstructed from computed tomography angiography and conventional coronary angiography images. Results of wall shear stress and relative residence time show that the wall regions more prone to the risk of restenosis are located next to stent struts, to the bifurcations and to the stent overlapping zone for both investigated cases. Considering a bulk flow analysis, helical flow structures are generated by the curvature of the zone upstream from the stent and by the bifurcation regions. Helical recirculating microstructures are also visible downstream from the stent struts. This study demonstrates the feasibility to virtually investigate the haemodynamics of patient-specific coronary bifurcation geometries.

Patient-specific simulations of stenting procedures in coronary bifurcations: two clinical cases

Computational simulations of stenting procedures in idealized geometries can only provide general guidelines and their use in the patient-specific planning of percutaneous treatments is inadequate. Conversely, image-based patient-specific tools that are able to realistically simulate different interventional options might facilitate clinical decision-making and provide useful insights on the treatment for each individual patient. The aim of this work is the implementation of a patient-specific model that uses image-based reconstructions of coronary bifurcations and is able to replicate real stenting procedures following clinical indications. Two clinical cases are investigated focusing the attention on the open problems of coronary bifurcations and their main treatment, the provisional side branch approach. Image-based reconstructions are created combining the information from conventional coronary angiography and computed tomography angiography while structural finite element models are implemented to replicate the real procedure performed in the patients. First, numerical results show the biomechanical influence of stents deployment in the coronary bifurcations during and after the procedures. In particular, the straightening of the arterial wall and the influence of two overlapping stents on stress fields are investigated here. Results show that a sensible decrease of the vessel tortuosity occurs after stent implantation and that overlapping devices result in an increased stress state of both the artery and the stents. Lastly, the comparison between numerical and image-based post-stenting configurations proved the reliability of such models while replicating stent deployment in coronary arteries.

Performance assessment of isolation methods for geometrical cerebral aneurysm analysis

Geometrical aneurysm quantification is considered an important topic for the study of aneurysm formation, growth, risk of rupture and also in treatment planning. Usually, quantification involves aneurysm isolation, consisting in the operation of detecting the boundary between the aneurysm dome and its feeding arteries. This operation is sometimes performed manually, but it is a tedious task, subject to user variability. To obtain reproducible measurements, automatic techniques have been proposed. In this paper, we compare different aneurysm isolation techniques, two automatic and one manual-based on a cutting plane. All of them are compared against the results obtained by manual delineations of 26 real cases. We show from the results that automatic methods have good performance, providing results similar to manual methods in average. We also show that automatic methods improve reproducibility compared to direct measurements performed on volume rendering views. Each automatic method presents strengths and weaknesses in particular cases such as small aneurysms, aneurysms with multiple parent vessels or terminal aneurysms, but their reproducibility makes them suitable for robust population studies. Finally, based on this study, we have proposed a criterion that allows to use a combination of the two methods studied and that outperforms each of them individually.

AngioLab--a software tool for morphological analysis and endovascular treatment planning of intracranial aneurysms

Determining whether and how an intracranial aneurysm should be treated is a tough decision that clinicians face everyday. Emerging computational tools could help clinicians analyze clinical data and make these decisions. AngioLab is a single graphical user interface, developed on top of the open source framework GIMIAS, that integrates some of the latest image analysis and computational modeling tools for intracranial aneurysms. Two workflows are available: Advanced Morphological Analysis (AMA) and Endovascular Treatment Planning (ETP). AngioLab has been evaluated by a total of 62 clinicians, who considered the information provided by AngioLab relevant and meaningful. They acknowledged the emerging need of these type of tools and the potential impact they might have on the clinical decision-making process.

Automatic aneurysm neck detection using surface Voronoi diagrams

A new automatic approach for saccular intracranial aneurysm isolation is proposed in this work. Due to the inter- and intra-observer variability in manual delineation of the aneurysm neck, a definition based on a minimum cost path around the aneurysm sac is proposed that copes with this variability and is able to make consistent measurements along different data sets, as well as to automate and speedup the analysis of cerebral aneurysms. The method is based on the computation of a minimal path along a scalar field obtained on the vessel surface, to find the aneurysm neck in a robust and fast manner. The computation of the scalar field on the surface is obtained using a fast marching approach with a speed function based on the exponential of the distance from the centerline bifurcation between the aneurysm dome and the parent vessels. In order to assure a correct topology of the aneurysm sac, the neck computation is constrained to a region defined by a surface Voronoi diagram obtained from the branches of the vessel centerline. We validate this method comparing our results in 26 real cases with manual aneurysm isolation obtained using a cut-plane, and also with results obtained using manual delineations from three different observers by comparing typical morphological measures.

Three-dimensional morphological analysis of intracranial aneurysms: a fully automated method for aneurysm sac isolation and quantification

PURPOSE

Morphological descriptors are practical and essential biomarkers for diagnosis and treatment selection for intracranial aneurysm management according to the current guidelines in use. Nevertheless, relatively little work has been dedicated to improve the three-dimensional quantification of aneurysmal morphology, to automate the analysis, and hence to reduce the inherent intra and interobserver variability of manual analysis. In this paper we propose a methodology for the automated isolation and morphological quantification of saccular intracranial aneurysms based on a 3D representation of the vascular anatomy.

METHOD

This methodology is based on the analysis of the vasculature skeleton's topology and the subsequent application of concepts from deformable cylinders. These are expanded inside the parent vessel to identify different regions and discriminate the aneurysm sac from the parent vessel wall. The method renders as output the surface representation of the isolated aneurysm sac, which can then be quantified automatically. The proposed method provides the means for identifying the aneurysm neck in a deterministic way. The results obtained by the method were assessed in two ways: they were compared to manual measurements obtained by three independent clinicians as normally done during diagnosis and to automated measurements from manually isolated aneurysms by three independent operators, nonclinicians, experts in vascular image analysis. All the measurements were obtained using in-house tools. The results were qualitatively and quantitatively compared for a set of the saccular intracranial aneurysms (n = 26).

RESULTS

Measurements performed on a synthetic phantom showed that the automated measurements obtained from manually isolated aneurysms where the most accurate. The differences between the measurements obtained by the clinicians and the manually isolated sacs were statistically significant (neck width: p <0.001, sac height: p = 0.002). When comparing clinicians' measurements to automatically isolated sacs, only the differences for the neck width were significant (neck width: p <0.001, sac height: p = 0.95). However, the correlation and agreement between the measurements obtained from manually and automatically isolated aneurysms for the neck width: p = 0.43 and sac height: p = 0.95 where found.

CONCLUSIONS

The proposed method allows the automated isolation of intracranial aneurysms, eliminating the interobserver variability. In average, the computational cost of the automated method (2 min 36 s) was similar to the time required by a manual operator (measurement by clinicians: 2 min 51 s, manual isolation: 2 min 21 s) but eliminating human interaction. The automated measurements are irrespective of the viewing angle, eliminating any bias or difference between the observer criteria. Finally, the qualitative assessment of the results showed acceptable agreement between manually and automatically isolated aneurysms.

Saturn: a software application of tensor utilities for research in neuroimaging

We present an advanced software tool designed for visualization and quantitative analysis of Diffusion Tensor Imaging (DTI) called Saturn. The software is specially developed to help clinicians and researchers in neuroimaging, and includes a complete set of visualization capabilities to browse and analyze efficiently DTI data, making this application a powerful tool also for diagnosis purposes. The software includes a robust quantification method for DTI data, using an atlas-based method to automatically obtain equivalent anatomical fiber bundles and regions of interest among different DTI data sets. Consequently, a set of measurements is also implemented to perform robust group studies among subjects affected by neurological disorders and control groups in order to look for significant differences. Finally, a comparison study with five similar DTI applications is presented, showing the advantages offered by this tool.

Automatic identification of internal carotid artery from 3DRA images

Geometric characteristics and arrangement of the cerebral vessels are assumed to be related to the development of vascular diseases. Identifying anatomical segments and bifurcations of the cerebral vasculature allows the comparison of these characteristics across and within subjects. In this paper, we focus on the automatic identification of internal carotid artery (ICA) from 3D rotational angiographic images. The steps of the proposed method are the following: Arterial vascular tree is first segmented and centerlines are computed. From a set of centerlines, vascular tree topology is constructed and its bifurcations geometrically characterized. Finally, ICA terminal bifurcation is detected, which enables ICA identification. To detect ICA terminal bifurcation, a support vector machine classifier is trained. We processed 82 images to obtain 274 feature vectors of bifurcations around the ICA. 10×5-fold cross-validation showed an average accuracy of 99.6%, with 99.5% specificity and 100% sensitivity. The two most discriminating bifurcation features were: radius ratio between the smaller branch and its parent vessel, and the long-axis component of the smaller branch vector.

A multidimensional segmentation evaluation for medical image data

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.