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Accurate geometric modeling of blood vessel lumen from 3D images is crucial for vessel quantification as part of the diagnosis, treatment, and monitoring of vascular diseases. Our method, unlike other approaches which assume a circular or elliptical vessel cross-section, employs parametric B-splines combined with image formation system equations to accurately localize the highly curved lumen boundaries. This approach avoids the need for image segmentation, which may reduce the localization accuracy due to spatial discretization. We demonstrate that the model parameters can be reliably identified by a feedforward neural network which, driven by the cross-section images, predicts the parameter values many times faster than a reference least-squares (LS) model fitting algorithm. We present and discuss two example applications, modeling the lower extremities of artery–vein complexes visualized in steady-state contrast-enhanced magnetic resonance images (MRI) and the coronary arteries pictured in computed tomography angiograms (CTA). Beyond applications in medical diagnosis, blood-flow simulation and vessel-phantom design, the method can serve as a tool for automated annotation of image datasets to train machine-learning algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this study was to differentiate acute from chronic myocardial infarction using machine learning techniques and texture features extracted from cardiac magnetic resonance imaging (MRI). The study group comprised 22 cases with acute myocardial infarction (AMI) and 22 cases with chronic myocardial infarction (CMI). Cine and late gadolinium enhancement (LGE) MRI were analyzed independently to differentiate AMI from CMI. A total of 279 texture features were extracted from predefined regions of interest (ROIs): the infarcted area on LGE MRI, and the entire myocardium on cine MRI. Classification performance was evaluated by a nested cross-validation approach combining a feature selection technique with three predictive models: random forest, support vector machine (SVM) with Gaussian Kernel, and SVM with polynomial kernel. The polynomial SVM yielded the best classification performance. Receiver operating characteristic curves provided area-under-the-curve (AUC) (mean±standard deviation) of 0.86±0.06 on LGE MRI using 72 features; AMI sensitivity=0.81±0.08 and specificity=0.84±0.09. On cine MRI, AUC=0.82±0.06 using 75 features; AMI sensitivity=0.79±0.10 and specificity=0.80±0.10. We concluded that texture analysis can be used for differentiation of AMI from CMI on cardiac LGE MRI, and also on standard cine sequences in which the infarction is visually imperceptible in most cases. [ABSTRACT FROM AUTHOR]

In this paper we present a numerical framework for validating methods of quantitative analysis of non-invasive MR angiography imaging protocols such as Time-of-Flight (ToF) and Phase Contrast Angiography (PCA). Our study is motivated by the need to reliably and objectively verify blood flow and geometry measurements derived from image data. Both factors are important predictors in diagnosing of carotid artery stenosis. Credibility of the tested image processing methods is verified by comparing their results against reference models designed using integrated flow and MRA imaging simulator. [ABSTRACT FROM PUBLISHER]

Introduction: The aim of the study was to propose and evaluate textural entropy as a parameter for bone healing assessment. Material and methods: One hundred and twenty radiographs with loss of bone architecture were investigated (a bone defect was circumscribed - ROI DEF). A reference region (ROI REF) of the same surface area as the ROI DEF was placed in a field distant from the defect, where a normal, trabecular pattern of bone structure was well visualized. Data of three time points were investigated: T0 - immediately after the surgical procedure, T1 - 3 months post-op, and T2 - 12 months post-op. Results: Textural entropy as a parameter describing bone structure regeneration was selected based on Fisher coefficient (F) evaluation. F was highest in T0 (3.4) and was decreasing later in T1 (1.7) and T2 (1.0 - means final lack of difference in the structure to reference bone). Textural entropy is a measure of structure disarrangement which in a bone defect region attains minimal value due to structural homogeneity, i.e. low complexity of the texture. The calculated parameter in the investigated material revealed a gradual increase inside the bone defect (p < 0.05), i.e. increase of complexity in a time-dependent manner starting from immediate post-op (T0 = 2.51; T1 = 2.68) up to most complex 1 year post-operational (T2 = 2.73), reaching the reference level of a normal bone. Conclusions: Textural entropy may be useful for computer assisted evaluation of bone regeneration process. The complexity of the texture corresponds to mature trabecular bone formation. [ABSTRACT FROM AUTHOR]

Background and objective Accurate vessel segmentation of magnetic resonance angiography (MRA) images is essential for computer-aided diagnosis of cerebrovascular diseases such as stenosis or aneurysm. The ability of a segmentation algorithm to correctly reproduce the geometry of the arterial system should be expressed quantitatively and observer-independently to ensure objectivism of the evaluation. Methods This paper introduces a methodology for validating vessel segmentation algorithms using a custom-designed MRA simulation framework. For this purpose, a realistic reference model of an intracranial arterial tree was developed based on a real Time-of-Flight (TOF) MRA data set. With this specific geometry blood flow was simulated and a series of TOF images was synthesized using various acquisition protocol parameters and signal-to-noise ratios. The synthesized arterial tree was then reconstructed using a level-set segmentation algorithm available in the Vascular Modeling Toolkit (VMTK). Moreover, to present versatile application of the proposed methodology, validation was also performed for two alternative techniques: a multi-scale vessel enhancement filter and the Chan–Vese variant of the level-set-based approach, as implemented in the Insight Segmentation and Registration Toolkit (ITK). The segmentation results were compared against the reference model. Results The accuracy in determining the vessels centerline courses was very high for each tested segmentation algorithm (mean error rate = 5.6% if using VMTK). However, the estimated radii exhibited deviations from ground truth values with mean error rates ranging from 7% up to 79%, depending on the vessel size, image acquisition and segmentation method. Conclusions We demonstrated the practical application of the designed MRA simulator as a reliable tool for quantitative validation of MRA image processing algorithms that provides objective, reproducible results and is observer independent. [ABSTRACT FROM AUTHOR]