Iván Macía

Knowledge management in image-based analysis of blood vessel structures

We have detected the lack of a widely accepted knowledge representation model in the area of Blood Vessel analysis. We find that such a tool is needed for the future development of the field and our own research efforts. It will allow easy reuse of software pieces through appropriate abstractions, facilitating the development of innovative methods, procedures and applications. We include a thorough review of vascular morphology image analysis. After the identification of the key representation elements and operations, we propose a Vessel Knowledge Representation (VKR) model that would fill this gap. We give insights into its implementation based on standard Object-Oriented Programming tools and paradigms. The VKR would easily integrate with existing medical imaging and visualization software platforms, such as the Insight ToolKit (ITK) and Visualization Toolkit (VTK).

Segmentation of abdominal aortic aneurysms in CT images using a radial model approach

Abdominal Aortic Aneurysm (AAA) is a dangerous condition where the weakening of the aortic wall leads to its deformation and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAAs can be treated non-invasively by means of the Endovascular Aneurysm Repair technique (EVAR), which consists of placing a stent-graft inside the aorta in order to exclude the bulge from the blood circulation and usually leads to its contraction. Nevertheless, the bulge may continue to grow without any apparent leak. In order to effectively assess the changes experienced after surgery, it is necessary to segment the aneurysm, which is a very time-consuming task. Here we describe the initial results of a novel model-based approach for the semi-automatic segmentation of both the lumen and the thrombus of AAAs, using radial functions constrained by a priori knowledge and spatial coherency.

Hybrid decision support system for endovascular aortic aneurysm repair follow-up

An Abdominal Aortic Aneurysm is an abnormal widening of the aortic vessel at abdominal level, and is usually diagnosed on the basis of radiological images One of the techniques for Abdominal Aortic Aneurysm repair is Endovascular Repair The long-term outcome of this surgery is usually difficult to predict in the absence of clearly visible signs, such as leaks, in the images In this paper, we present a hybrid system that combines data extracted from radiological images and data extracted from the Electronic Patient Record in order to assess the evolution of the aneurysm after the intervention The results show that the system proposed by this approach yields valuable qualitative and quantitative information for follow-up of Abdominal Aortic Aneurysm patients after Endovascular Repair.

An automatic segmentation and reconstruction of mandibular structures from CT-data

In any medical data analysis a good visualization of specific parts or tissues are fundamental in order to perform accurate diagnosis and treatments. For a better understanding of the data, a segmentation process of the images to isolate the area or region of interest is important to be applied beforehand any visualization step. In this paper we present a method for mandibular structure surface extraction and reconstruction from CT-data images. We tested several methods and algorithms in order to find a fast and feasible approach that could be applicable in clinical procedures, providing practical and efficient tools for mandibular structures analysis.

Detection of type II endoleaks in abdominal aortic aneurysms after endovascular repair

Abdominal aortic aneurysm (AAA) is a condition where the weakening of the aortic wall leads to its widening and the generation of a thrombus. To prevent a possible rupture of the aortic wall, AAA can be treated non-invasively by means of the endovascular aneurysm repair technique (EVAR), consisting of placing a stent-graft inside the aorta by a cateter to exclude the aneurysm sac from the blood circulation. A major complication is the presence of liquid blood turbulences, called endoleaks, in the thrombus formed in the space between the aortic wall and the stent-graft. In this paper we propose an automatic method for the detection of type II endoleaks in computer tomography angiography (CTA) images. The lumen and thrombus in the aneurysm area are first segmented using a radial model approach. Then, these regions are split into Thrombus Connected Components (TCCs) using a watershed-based segmentation and geometric and image content-based characteristics are obtained for each TCC. Finally, TCCs are classified into endoleaks and non-endoleaks using a multilayer Perceptron (MLP) trained on manual labeled sample TCCs provided by experts.

Ultrasound Image Dataset for Image Analysis Algorithms Evaluation

The use of ultrasound (US) imaging as an alternative for real-time computer assisted interventions is increasing. Growing usage of US occurs despite of US lower imaging quality compared to other techniques and its difficulty to be used with image analysis algorithms. On the other hand, it is still difficult to find sufficient data to develop and assess solutions for navigation, registration and reconstruction at medical research level. At present, manually acquired available datasets present significant usability obstacles due to their lack of control of acquisition conditions, which hinders the study and correction of algorithm design parameters. To address these limitations, we present a database of robotically acquired sequences of US images from medical phantoms, ensuring the trajectory, pose and force control of the probe. The acquired dataset is publicly available, and it is specially useful for designing and testing registration and volume reconstruction algorithms.

Standard and fenestrated endograft sizing in EVAR planning: Description and validation of a semi-automated 3D software

An abdominal aortic aneurysm (AAA) is a pathological dilation of the abdominal aorta that may lead to a rupture with fatal consequences. Endovascular aneurysm repair (EVAR) is a minimally invasive surgical procedure consisting of the deployment and fixation of a stent-graft that isolates the damaged vessel wall from blood circulation. The technique requires adequate endovascular device sizing, which may be performed by vascular analysis and quantification on Computerized Tomography Angiography (CTA) scans. This paper presents a novel 3D CTA image-based software for AAA inspection and EVAR sizing, eVida Vascular, which allows fast and accurate 3D endograft sizing for standard and fenestrated endografts. We provide a description of the system and its innovations, including the underlying vascular image analysis and visualization technology, functional modules and user interaction. Furthermore, an experimental validation of the tool is described, assessing the degree of agreement with a commercial, clinically validated software, when comparing measurements obtained for standard endograft sizing in a group of 14 patients.

Thrombus volume change visualization after endovascular abdominal aortic aneurysm repair

A surgical technique currently used in the treatment of Abdominal Aortic Aneurysms (AAA) is the Endovascular Aneurysm Repair (EVAR) This minimally invasive procedure involves inserting a prosthesis in the aortic vessel that excludes the aneurysm from the bloodstream The stent, once in place acts as a false lumen for the blood current to travel down, and not into the surrounding aneurysm sac This procedure, therefore, immediately takes the pressure off the aneurysm, which thromboses itself after some time Nevertheless, in a long term perspective, different complications such as prosthesis displacement or bloodstream leaks into or from the aneurysmatic bulge (endoleaks) could appear causing a pressure elevation and, as a result, increasing the danger of rupture The purpose of this work is to explore the application of image registration techniques to the visual detection of changes in the thrombus in order to assess the evolution of the aneurysm Prior to registration, both the lumen and the thrombus are segmented

Robust CT to US 3D-3D Registration by Using Principal Component Analysis and Kalman Filtering

Algorithms based on the unscented Kalman filter (UKF) have been proposed as an alternative for registration of point clouds obtained from vertebral ultrasound (US) and computerised tomography (CT) scans, effectively handling the US limited depth and low signal-to-noise ratio. Previously proposed methods are accurate, but their convergence rate is considerably reduced with initial misalignments of the datasets greater than \(30^\circ \) or 30 mm. We propose a novel method which increases robustness by adding a coarse alignment of the datasets’ principal components and batch-based point inclusions for the UKF. Experiments with simulated scans with full coverage of a single vertebra show the method’s capability and accuracy to correct misalignments as large as \(180^\circ \) and 90 mm. Furthermore, the method registers datasets with varying degrees of missing data and datasets with outlier points coming from adjacent vertebrae.

Stent graft change detection after endovascular abdominal aortic aneurysm repair

The use of the endovascular prostheses in Abdominal Aortic Aneurysm (EVAR) has proven to be an effective technique to reduce the pressure and rupture risk of aneurysm. Nevertheless, in a long term perspective different complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear causing a pressure elevation and increasing the danger of rupture consequently. At present computed tomographic angiography (CTA) is the most commonly used examination for imaging surveillance for stent graft monitoring. However, endoleak complications can not always be detected by visual inspection on CTA scans. The purpose of this work was to study the application of image registration techniques to the detection of changes in the stent graft. Previously we segment the lumen using semi-automatic methods.