Aneurysm rupture risk can be assessed by its morphologic and hemodynamics features extracted based on angiographic images. Feature extraction entails aneurysm isolation, typically by manually positioning a cutting plane (MCP). To eliminate intra- and inter-rater variabilities we propose automatic cutting plane (ACP) positioning based on the analysis of vascular surface mesh. Innovative Hough-like and multi-hypothesis based detection of aneurysm center, parent vessel inlets, and centerlines were proposed. These were used for initialization and iterative ACP positioning by geometry-inspired cost function optimization. For validation and baseline comparison we tested MCP and manual neck curve based isolation. Isolated aneurysm morphology was characterized by size, dome height, aspect ratio, and nonsphericity index. Methods were applied to 55 intracranial saccular aneurysms from two sites, involving 3D DSA, CTA, and MRA modalities. Isolation based on ACP resulted in small average inter-curve distances (AICDs), compared to those obtained by MCP. One case had AICD higher than 1.0 mm, while 90% of cases had AICD (0.5 mm. Intra- and inter-rater AICD variability of manual neck curves was higher compared to MCP, validating its robustness for clinical purposes. The ACP method achieved high accuracy and reliability of aneurysm isolation, also confirmed by expert visual analysis. So extracted morphologic features were in good agreement with MCP-based ones therefore ACP has great potential for aneurysm morphology and hemodynamics quantification in clinical applications. Novel method is angiographic modality agnostic, delivers repeatable isolation important in follow-up aneurysm assessment, its performance is comparable to MCP, re-evaluation is fast and simple.
COBISS.SI-ID: 12543316
Latent biomarkers are quantities that strongly relate to patient's disease diagnosis and prognosis, but are difficult to measure or even not directly observable. The objective of this study was to develop, analyze and validate new priors for Bayesian inference of such biomarkers. Theoretical analysis revealed a relationship between the estimates inferred from the model and the true values of measured quantities, and the impact of the priors. This led to a new prior encoding scheme that incorporates objectively measurable domain knowledge, i.e. by performing two measurements with a reference method, which imply scale of the prior distribution. Second, priors on parameters of systematic error are non-informative, which enables biomarker estimation from a set of different quantities. Proposed methodology enabled a comparative evaluation of biomarkers, as obtained from aneurysm quantification, and resulted in approximated true biomarker values from biased and noisy observation based on angiographic image analysis; the latter is especially the case for small aneurysms. The proposed new priors substantially simplify the application of Bayesian inference for latent biomarkers and thus open an avenue for clinical implementation of new biomarkers, which may ultimately advance the evidence-based medicine.
COBISS.SI-ID: 12721492
Image guidance for minimally aneurysm treatment is based on spatial co-registration and fusion of 3D pre-interventional images and treatment plans with the 2D live intra-interventional images. The spatial co-registration or 3D–2D registration is the key enabling technology; however, the performance of state-of-the-art automated methods is rather unclear as they have not been assessed under the same test conditions. We thus performed a quantitative and comparative evaluation of ten state-of-the-art methods for 3D–2D registration on a public dataset of clinical angiograms. Image database consisted of 3D and 2D angiograms of 25 patients undergoing treatment for cerebral aneurysms. On each of the datasets, highly accurate “gold-standard” registrations of 3D and 2D images were established based on patient-attached fiducial markers. The database was used to rigorously evaluate ten state-of-the-art 3D–2D registration methods, namely two intensity-, two gradient-, three feature-based and three hybrid methods, both for registration of 3D pre-interventional image to monoplane or biplane 2D images. Intensity-based methods were most accurate in all tests (0.3 mm). One of the hybrid methods was most robust with 98.75% of successful registrations (SR) and capture range of 18 mm for registrations of 3D to biplane 2D angiograms. In general, registration ccuracy was similar whether registration of 3D image was performed onto mono- or biplanar 2D images; however, the SR was substantially lower in case of 3D to monoplane 2D registration. Two feature-based and two hybrid methods had clinically feasible execution times in the order of a second. Performance of methods seems to fall below expectations in terms of robustness in case of registration of 3D to monoplane 2D images, while translation into clinical image guidance systems used during aneurysm treatment seems readily feasible for methods that perform registration of the 3D pre-interventional image onto biplanar intra-interventional 2D images.
COBISS.SI-ID: 11878228
Registration of 3D pre-interventional to 2D intra-interventional medical images has an increasingly important role in surgical planning, navigation and treatment of intracranial aneurysms, because it enables the physician to co-locate depth information given by pre-interventional 3D images with the live information in intra-interventional 2D images such as x-ray. Most tasks during image-guided interventions are carried out under a monoplane x-ray, which is a highly ill-posed problem for state-of-the-art 3D to 2D registration methods. To address the problem of rigid 3D–2D monoplane registration we propose a novel multi-objective stratified parameter optimization, wherein a small set of high-magnitude intensity gradients are matched between the 3D and 2D images. The stratified parameter optimization matches rotation templates to depth templates, first sampled from projected 3D gradients and second from the 2D image gradients, so as to recover 3D rigid-body rotations and out-of-plane translation. The objective for matching was the gradient magnitude correlation coefficient, which is invariant to in-plane translation. The in-plane translations are then found by locating the maximum of the gradient phase correlation between the best matching pair of rotation and depth templates. On twenty pairs of 3D and 2D images of ten patients undergoing cerebral endovascular image-guided intervention the 3D to monoplane 2D registration experiments were setup with a rather high range of initial mean target registration error from 0 to 100 mm. The proposed method effectively reduced the registration error to below 2 mm, which was further refined by a fast iterative method and resulted in a high final registration accuracy (0.40 mm) and high success rate ( ) 96%). Taking into account a fast execution time below 10 s, the observed performance of the proposed method shows a high potential for application into clinical image-guidance systems for aneurysm treatment.
COBISS.SI-ID: 11896660
A certain quantity can usually be measured by several different measurement methods. For instance, in medical image analysis, there may be different image segmentation methods to delineate and measure the volume of a certain structure, but the true value may still be unknown or accessible using a more costly or even destructive method. We proposed a novel computational framework that automatically determined the most accurate and precise method of measurement of a certain quantity, when there was no access to the true value of the measurand. The accuracy of each measurement method was characterized by systematic error (bias), which was modeled with polynomials in true values of measurand, and the precision as random error modeled with a Gaussian random variable. In contrast to previous works the random errors were modeled jointly across all methods, thereby enabling the framework to analyze measurement methods based on similar principles, which may have correlated random errors. Furthermore, the posterior distribution of the error model parameters is estimated from samples obtained by Markov chain Monte-Carlo and analyzed to estimate the parameter values and the unknown true values of the measurand. The framework was validated on a dataset containing measurements of total volume of brain lesions (ie. aneurysms), which was obtained with four automatic methods by analyzing brain magnetic resonance images. The obtained estimates of bias and random error, and true value estimates, were in a good agreement with the corresponding least squares regression estimates against a reference consensus-based measurements.
COBISS.SI-ID: 11948116