Fast and accurate Gaussian derivatives based on B-splines

Gaussian derivatives are often used as differential operators to analyze the structure in images. In this paper, we will analyze the accuracy and computational cost of the most common implementations for differentiation and interpolation of Gaussian-blurred multi-dimensional data. We show that – for the computation of multiple Gaussian derivatives– the method based on B-splines obtains a higher accuracy than the truncated Gaussian at equal computational cost

Unbiased vessel-diameter quantification based on the FWHM criterion

The full-width at half-max (FWHM) criterion is often used for both manual and automatic quantification of the vessel diameter in medical images. The FWHM criterion is easy to understand and it can be implemented with low computational cost. However, it is well known that the FWHM criterion can give an over- and underestimation of the vessel diameter. In this paper, we propose a simple and original method to create an unbiased estimation of the vessel diameter based on the FWHM criterion and we compared the robustness to noise of several edge detectors. The quantitative results of our experiments show that the proposed method is accurate and precise in comparison to other (more complex) edge detectors, even for small vessels

CoViCAD : comprehensive visualization of coronary artery disease

We present novel, comprehensive visualization techniques for the diagnosis of patients with Coronary Artery Disease using segmented cardiac MRI data. We extent an accepted medical visualization technique called the bull’s eye plot by removing discontinuities, preserving the volumetric nature of the left ventricular wall and adding anatomical context. The resulting volumetric bull’s eye plot can be used for the assessment of transmurality. We link these visualizations to a 3D view that presents viability information in a detailed anatomical context. We combine multiple MRI scans (whole heart anatomical data, late enhancement data) and multiple segmentations (polygonal heart model, late enhancement contours, coronary artery tree). By selectively combining different rendering techniques we obtain comprehensive yet intuitive visualizations of the various data sources

Fast and accurate Gaussian derivatives based on B-splines

Gaussian derivatives are often used as differential operators to analyze the structure in images. In this paper, we will analyze the accuracy and computational cost of the most common implementations for differentiation and interpolation of Gaussian-blurred multi-dimensional data. We show that – for the computation of multiple Gaussian derivatives– the method based on B-splines obtains a higher accuracy than the truncated Gaussian at equal computational cost

Assisting vascular access surgery planning for hemodialysis by using MR, image segmentation techniques, and computer simulations

The surgical creation of a vascular access, used for hemodialysis treatment of renal patients, has considerable complication rates (30–50 %). Image-based computational modeling might assist the surgeon in planning by enhanced analysis of preoperative hemodynamics, and in the future might serve as platform for outcome prediction. The objective of this study is to investigate preoperative personalization of the computer model using magnetic resonance (MR). MR-angiography and MR-flow data were obtained for eight patients and eight volunteers. Blood vessels were extracted for model input by a segmentation algorithm. Windkessel elements were added at the ends to represent the peripheral beds. Monte Carlo-based calibration was used to estimate the most influential non-measurable parameters. The predicted flow waveforms were compared with the MR-flow measurements for framework evaluation. The vasculature of all subjects were segmented in on averag

Clinical study protocol for the ARCH project Computational modeling for improvement of outcome after vascular access creation

Despite clinical guidelines and the possibility of diagnostic vascular imaging, creation and maintenance of a vascular access (VA) remains problematic: avoiding short- and long-term VA dysfunction is challenging. Although prognostic factors for VA dysfunction have been identified in previous studies, their potential interplay at a systemic level is disregarded. Consideration of multiple prognostic patient specific factors and their complex interaction using dedicated computational modeling tools might improve outcome after VA creation by enabling a better selection of VA configuration. These computational modeling tools are developed and validated in the ARCH project: a joint initiative of four medical centers and three industrial partners (FP7-ICT-224390). This paper reports the rationale behind computational modeling and presents the clinical study protocol designed for calibrating and validating these modeling tools. The clinical study is based on the pre-operative collection of structural and functional data at a vascular level, as well as a VA functional evaluation during the follow-up period. The strategy adopted to perform the study and for data collection is also described her