Modification of Woven Endo-Bridge After Intracranial Aneurysm Treatment: A Methodology for Three-Dimensional Analysis of Shape and Relative Position Changes.

During follow-up of patients treated with WEB devices, shape changes have been observed. The quantitative three-dimensional measurement of the WEB shape modification (WSM) would offer useful information to be studied in association with the anatomical results and try to better understand mechanisms implicated in this modification phenomenon. We present a methodology to quantify the morphology and position of the WEB device in relation to the vascular anatomy. Three-dimensional rotational angiography (3DRA) images of seven aneurysms patients treated with WEBs were used, which also accompanied by a post-treatment 3DRA image and a follow-up 3DRA image. The device was manually segmented, obtaining the 3D models after treatment and at the follow-up. Volume, surface area, height, maximum diameter and WSM ratio of both surfaces were calculated. Position changes were evaluated measuring WEB axis and relative position between post-treatment and follow-up. Changes in WEB volume and surface area were observed with a mean modification of - 5.04 % ( ± 14.19 ) and - 1.68 % ( ± 8.29 ) , respectively. The positional variables also showed differences, mean change of device axis direction was 26.25 % ( ± 24.09 ) and mean change of distance l b was 5.87 % ( ± 10.59 ) . Inter-observer and intra-observer variability analyses did not show differences (ANOVA p > 0.05 ). This methodology allows quantifying the morphological and position changes suffered by the WEB device after treatment, offering new information to be studied in relation to the occurrence of WEB shape modification.

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance.

Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier. From this study, we observed that these variables do affect the automatic model performance, improving the classification of some sleep stages and worsening others.

Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA.

PURPOSE: When performing a brain arteriovenous malformation (bAVMs) intervention, computer-assisted analysis of bAVMs can aid clinicians in planning precise therapeutic alternatives. Therefore, we aim to assess currently available methods for bAVMs nidus extent identification over 3DRA. To this end, we establish a unified framework to contrast them over the same dataset, fully automatising the workflows. MATERIALS AND METHODS: We retrospectively collected contrast-enhanced 3DRA scans of patients with bAVMs. A segmentation network was used to automatically acquire the brain vessels segmentation for each case. We applied the nidus extent identification algorithms over each of the segmentations, computing overlap measurements against manual nidus delineations. RESULTS: We evaluated the methods over a private dataset with 22 3DRA scans of individuals with bAVMs. The best-performing alternatives resulted in [Formula: see text] and [Formula: see text] dice coefficient values. CONCLUSIONS: The mathematical morphology-based approach showed higher robustness through inter-case variability. The skeleton-based approach leverages the skeleton topomorphology characteristics, while being highly sensitive to anatomical variations and the skeletonisation method employed. Overall, nidus extent identification algorithms are also limited by the quality of the raw volume, as the consequent imprecise vessel segmentation will hinder their results. Performance of the available alternatives remains subpar. This analysis allows for a better understanding of the current limitations.

NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features.

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer's Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.

Simulation of intra-saccular devices for pre-operative device size selection: Method and validation for sizing and porosity simulation.

Intra-saccular devices (ID) are novel braided devices used for complex intracranial aneurysms treatment. Treatment success is associated with correct device size selection. A technique that predicts the ID size within the aneurysm before intervention will provide a powerful computational tool to aid the interventionist during device selection. We present a method to calculate the device's final height, radial expansion and porosity within the patient's anatomy, which allows assessing different device sizes before treatment takes place. The proposed sizing technique was tested in-vitro and in real patient's geometries obtained from 3DRA angiographic images of 8 unruptured aneurysms previously treated with IDs. The obtained simulated height was compared to the real height, with a mean error of less than 0.28 mm (±0.44). The porosity calculation method was tested in-vitro with an error of 0.02 (±0.022). The results of both sizing and porosity experiments resemble well measures from real patients. This methodology could be used before treatment to provide the interventionist with additional information that allows selecting the device that best fits the patient's aneurysm to be treated.

Intra-saccular device modeling for treatment planning of intracranial aneurysms: from morphology to hemodynamics.

MOTIVATION: Intra-saccular devices (ID), developed for the treatment of bifurcation aneurysms, offer new alternatives for treating complex terminal and bifurcation aneurysms. In this work, a complete workflow going from medical images to post-treatment CFD analysis is described and used in the assessment of a concrete clinical problem. MATERIALS AND METHODS: Two different intra-saccular device sizes were virtually implanted in 3D models of the patient vasculature using the ID-Fit method. After deployment, the local porosity at the closed end of the device in contact with the blood flow was computed. This porosity was then used to produce a CFD porous medium model of the device. Velocities and wall shear stress were assessed for each model. RESULTS: Six patients treated with intra-saccular devices were included in this work. For each case, 2 different device sizes were virtually implanted and 3 CFD simulations were performed: after deployment simulation with each size and before deployment simulation (untreated). A visible reduction in velocities was observed after device implantation. Velocity and WSS reduction was statistically significant (K-S statistics, [Formula: see text]). CONCLUSIONS: Placement of different device size can lead to a partial filling of the aneurysm, either at the dome or at the neck, depending on the particular positioning by the interventionist. The methodology used in this work can have a strong clinical impact, since it provides additional information in the process of device selection using preoperative data.

Cerebral Aneurysm Occlusion at 12-Month Follow-Up After Flow-Diverter Treatment: Statistical Modeling for V&V With Real-World Data.

Background: Flow-Diverter (FD) porosity has been pointed as a critical factor in the occlusion of cerebral aneurysms after treatment. Objective: Verification and Validation of computational models in terms of predictive capacity, relating FD porosity and occlusion after cerebral aneurysms treatment. Methods: Sixty-four aneurysms, with pre-treatment and follow-up images, were considered. Patient demographics and aneurysm morphological information were collected. The computational simulation provided by ANKYRAS provided FD porosity, expansion, and mesh angle. FD occlusion was assessed and recorded from follow-up images. Multiple regression Logit and analysis of covariance (ANCOVA) models were used to model the data with both categorical and continuous models. Results: Occlusion of the aneurysm after 12 months was affected by aneurysm morphology but not by FD mesh morphology. A Time-To-Occlusion (TTO) of 6.92 months on average was observed with an SE of 0.24 months in the aneurysm population surveyed. TTO was estimated with statistical significance from the resulting model for the data examined and was capable of explaining 92% of the data variation. Conclusions: Porosity was found to have the most correction power when assessing TTO, proving its importance in the process of aneurysm occlusion. Still, further Verification and Validation (V&V) of treatment simulation in more extensive, multi-center, and randomized databases is required.

Machine learning for filtering out false positive grey matter atrophies in single subject voxel based morphometry: A simulation based study.

Single subject VBM (SS-VBM), has been used as an alternative tool to standard VBM for single case studies. However, it has the disadvantage of producing an excessively large number of false positive detections. In this study we propose a machine learning technique widely used for automated data classification, namely Support Vector Machine (SVM), to refine the findings produced by SS-VBM. A controlled set of experiments was conducted to evaluate the proposed approach using three-dimensional T1 MRI scans from control subjects collected from the publicly available IXI dataset. The scans were artificially atrophied at different locations and with different sizes to mimic the behavior of neurological disorders. Results empirically demonstrated that the proposed method is able to significantly reduce the amount of false positive clusters (p < 0.05), with no statistical differences in the true positive findings (p > 0.05). This evidence was observed to be consistent for different atrophied areas and sizes of atrophies. This approach could be potentially be applied to alleviate the intensive manual analysis that radiologists and clinicians have to perform to filter out miss-detections of SS-VBM, increasing its usability for image reading.

Stenting as porous media in anatomically accurate geometries. A comparison of models and spatial heterogeneity.

Modelling intracranial aneurysm blood flow after flow diverter treatment has proven to be of great scientific and clinical interest. One of the reasons for not having CFD as an everyday clinical tool yet is the time required to set-up such simulations plus the required computational time. The speed-up of these simulations can have a considerable impact during treatment planning and device selection. Modelling flow diverters as a porous medium (PM) can considerably improve the computational time. Many models have been presented in literature, but quantitative comparisons between models are scarce. In this study, the untreated case, the explicit definition of the flow diverter wires as no-slip boundary condition and five different porous medium models were chosen for comparison, and evaluated on intracranial aneurysm of 14 patients with different shapes, sizes, and locations. CFD simulations were made using finite volume method on steady flow conditions. Velocities, kinetic energy, wall shear stress, and computational time were assessed for each model. Then, all models are compared against the no-slip boundary condition using non parametric Kolmogorov-Smirnov test. The model with least performance showed a mean K-S statistic of 0.31 and deviance of 0.2, while the model with best values always gave K-S statistics below 0.2. Kinetic energy between PM models varied between an over estimation of 218.3% and an under estimation of 73.06%. Also, speedups were between 4.75x and 5.3x (stdev: 0.38x and 0.15x) when using PM models. Flow diverters can be simulated with PM with a good agreement to standard CFD simulations were FD wires are represented with no-slip boundary condition in less than a quarter of the time. Best results were obtained on PM models based on geometrical properties, in particular, when using a heterogeneous medium based on equations for flat rhomboidal wire frames.

VBM sensitivity to localization and extent of mouse brain lesions: A simulation approach.

BACKGROUND AND OBJECTIVES: Voxel-based morphometry (VBM) is a popular neuroimaging technique, used to detect and quantify morphological differences in brain tissues between groups. Widely used in human studies, VBM approaches have tremendous potential for neuroimaging studies in animal models. A significant challenge for applying VBM to small animal studies is the poor understanding of how the design of preprocessing pipelines impacts quantitative results. This is important because the large differences in size, resolution, and imaging parameters implies that human imaging preprocessing pipelines cannot be uncritically applied to small animal studies. In this work, we assessed and validated the performance of different VBM pipelines for the study of the mouse brain. METHODS: We applied two pipelines -namely DARTEL VBM and Optimized VBM- by varying spatial normalization used during preprocessing. Using an automatic method, we simulated varying levels of volumetric gray matter (GM) loss and sizes of tissue atrophy on specific areas of the mouse brain. We evaluated the performance of each pipeline by comparing location and extent of the differences detected by them with the simulated ones. Finally, we applied both pipelines on magnetic resonance (MR) images of the brain derived from an experimental model of growth restriction on mice. RESULTS: Our results demonstrated that some subtle atrophies were detected by the Optimized workflow but not by the DARTEL VBM workflow. Detection of less subtle atrophies was similar for the two workflows, but DARTEL VBM performed better at estimating their size and anatomical location. Both VBM pipelines had difficulties at finding atrophies with a very small level of volumetric loss and, in general, they underestimated the magnitudes of difference between groups. These results also varied across brain regions, with better performance on brain cortex than other regions such as the cerebellum. CONCLUSIONS: The analysis and quantification of VBM pipelines on different areas of the mouse brain allows a better understanding of the advantages and limitations of their results. We performed a controlled and quantitative analysis of the method providing robust evidence to interpret changes in real contexts.

Regional assessment of vascular morphology and hemodynamics: methodology and evaluation for abdominal aortic aneurysms after endovascular repair.

Abdominal aortic aneurysm is a deadly disease that can be treated with different endovascular devices that will distinctly alter the aortic morphology. Computational methods can be used to understand the effect of anatomical changes on aortic hemodynamics. We propose a standardized method to assess morphological and hemodynamic changes of the abdominal aorta through the longitudinal axis of the vessel. Patient-specific CFD simulations were used to quantify these changes for two different endografts before and after surgery. Differences in cross-sectional area, blood pressure, peak blood velocity, wall shear stress, and retrograde blood flow were accurately evidenced with the proposed methodology.

Role of distal cerebral vasculature in vessel constriction after aneurysm treatment with flow diverter stents.

BACKGROUND: Treatment of intracranial aneurysms with flow diverter stent (FDS) procedures can lead to caliber changes of jailed vessels. The reason some branches remain unchanged and others are affected by narrowing remains unknown. OBJECTIVE: To investigate the influence of resistance to flow from distal vasculature on stent-induced hemodynamic modifications affecting bifurcating vessels. MATERIALS AND METHODS: Radiological images and demographic data were acquired for 142 aneurysms treated with a FDS. Vascular resistance was estimated from patient-specific anatomic data. Correlation analysis was used to identify correspondence between anatomic data and clinical outcome. Computational Fluid Dynamics was performed on a typical patient-specific model to evaluate the influence of FDS on flow. Relevant hemodynamic variables along the bifurcating vessels were quantitatively analyzed and validated with in vitro data obtained using power Doppler ultrasound. RESULTS: Statistical analysis showed a correlation between clinical outcome and FDS resistance to flow considering overall jailed vessel vascular resistance (r=0.5, P<0.001). Computational predictions of blood flow showed that hemodynamics is minimally affected by FDS treatment in the ophthalmic artery. CONCLUSIONS: Jailed vessels are affected by narrowing when resistance to flow from the FDS constitutes a larger proportion of the overall vessel resistance to flow. This knowledge may contribute to better understanding of intracranial hemodynamics after a FDS procedure and reinforce indications for flow diversion in the treatment of intracranial aneurysms.

Improving realism in patient-specific abdominal ultrasound simulation using CycleGANs.

PURPOSE: In this paper, we propose to apply generative adversarial neural networks trained with a cycle consistency loss, or CycleGANs, to improve realism in ultrasound (US) simulation from computed tomography (CT) scans. METHODS: A ray-casting US simulation approach is used to generate intermediate synthetic images from abdominal CT scans. Then, an unpaired set of these synthetic and real US images is used to train CycleGANs with two alternative architectures for the generator, a U-Net and a ResNet. These networks are finally used to translate ray-casting based simulations into more realistic synthetic US images. RESULTS: Our approach was evaluated both qualitatively and quantitatively. A user study performed by 21 experts in US imaging shows that both networks significantly improve realism with respect to the original ray-casting algorithm ([Formula: see text]), with the ResNet model performing better than the U-Net ([Formula: see text]). CONCLUSION: Applying CycleGANs allows to obtain better synthetic US images of the abdomen. These results can contribute to reduce the gap between artificially generated and real US scans, which might positively impact in applications such as semi-supervised training of machine learning algorithms and low-cost training of medical doctors and radiologists in US image interpretation.

Lumen-intima and media-adventitia segmentation in IVUS images using supervised classifications of arterial layers and morphological structures.

BACKGROUND: Intravascular ultrasound (IVUS) provides axial grey-scale images of blood vessels. The large number of images require automatic analysis, specifically to identify the lumen and outer vessel wall. However, the high amount of noise, the presence of artifacts and anatomical structures, such as bifurcations, calcifications and fibrotic plaques, usually hinder the proper automatic segmentation of the vessel wall. METHODS: Lumen, media, adventitia and surrounding tissues are automatically detected using Support Vector Machines (SVMs). The classification performance of the SVMs vary according to the kind of structure present within each region of the image. Random Forest (RF) is used to detect different morphological structures and to modify the initial layer classification depending on the detected structure. The resulting classification maps are fed into a segmentation method based on deformable contours to detect lumen-intima (LI) and media-adventitia (MA) interfaces. RESULTS: The modifications in the layer classifications according to the presence of structures proved to be effective improving LI and MA segmentations. The proposed method reaches a Jaccard Measure (JM) of 0.88 ±â€¯0.08 for LI segmentation, compared with 0.88 ±â€¯0.05 of a semiautomatic method. When looking at MA, our method reaches a JM of 0.84 ±â€¯0.09, and outperforms previous automatic methods in terms of HD, with 0.51mm ±â€¯0.30. CONCLUSIONS: A simple modification to the arterial layer classification produces results that match and improve state-of-the-art fully-automatic segmentation methods for LI and MA in 20MHz IVUS images. For LI segmentation, the proposed automatic method performs accurately as semi-automatic methods. For MA segmentation, our method matched the quality of state-of-the-art automatic methods described in the literature. Furthermore, our implementation is modular and open-source, allowing for future extensions and improvements.

Software-based simulation for preprocedural assessment of braided stent sizing: a validation study.

OBJECTIVEThe authors sought to validate the use of a software-based simulation for preassessment of braided self-expanding stents in the treatment of wide-necked intracranial aneurysms.METHODSThis was a retrospective, observational, single-center study of 13 unruptured and ruptured intracranial aneurysms treated with braided self-expanding stents. Pre- and postprocedural angiographic studies were analyzed. ANKYRAS software was used to compare the following 3 variables: the manufacturer-given nominal length (NL), software-calculated simulated length (SL), and the actual measured length (ML) of the stent. Appropriate statistical methods were used to draw correlations among the 3 lengths.RESULTSIn this study, data obtained in 13 patients treated with braided self-expanding stents were analyzed. Data for the 3 lengths were collected for all patients. Error discrepancy was calculated by mean squared error (NL to ML -22.2; SL to ML -6.14, p < 0.05), mean absolute error (NL to ML 3.88; SL to ML -1.84, p < 0.05), and mean error (NL to ML -3.81; SL to ML -1.22, p < 0.05).CONCLUSIONSThe ML was usually less than the NL given by the manufacturer, indicating significant change in length in most cases. Computational software-based simulation for preassessment of the braided self-expanding stents is a safe and effective way for accurately calculating the change in length to aid in choosing the right-sized stent for optimal placement in complex intracranial vasculature.

Real-World Variability in the Prediction of Intracranial Aneurysm Wall Shear Stress: The 2015 International Aneurysm CFD Challenge.

PURPOSE: Image-based computational fluid dynamics (CFD) is widely used to predict intracranial aneurysm wall shear stress (WSS), particularly with the goal of improving rupture risk assessment. Nevertheless, concern has been expressed over the variability of predicted WSS and inconsistent associations with rupture. Previous challenges, and studies from individual groups, have focused on individual aspects of the image-based CFD pipeline. The aim of this Challenge was to quantify the total variability of the whole pipeline. METHODS: 3D rotational angiography image volumes of five middle cerebral artery aneurysms were provided to participants, who were free to choose their segmentation methods, boundary conditions, and CFD solver and settings. Participants were asked to fill out a questionnaire about their solution strategies and experience with aneurysm CFD, and provide surface distributions of WSS magnitude, from which we objectively derived a variety of hemodynamic parameters. RESULTS: A total of 28 datasets were submitted, from 26 teams with varying levels of self-assessed experience. Wide variability of segmentations, CFD model extents, and inflow rates resulted in interquartile ranges of sac average WSS up to 56%, which reduced to < 30% after normalizing by parent artery WSS. Sac-maximum WSS and low shear area were more variable, while rank-ordering of cases by low or high shear showed only modest consensus among teams. Experience was not a significant predictor of variability. CONCLUSIONS: Wide variability exists in the prediction of intracranial aneurysm WSS. While segmentation and CFD solver techniques may be difficult to standardize across groups, our findings suggest that some of the variability in image-based CFD could be reduced by establishing guidelines for model extents, inflow rates, and blood properties, and by encouraging the reporting of normalized hemodynamic parameters.

A comparative study of porous medium CFD models for flow diverter stents: Advantages and shortcomings.

In computational fluid dynamics, there is a high interest in modeling flow diverter stents as porous media due to its reduced computational loads. One of the main difficulties of such models is proper parameter setup. Most authors assume flow diverter's wire screen as an isotropic and homogeneous medium, while others proposes anisotropic configurations, yet very little is discussed about the effect of these assumptions on model's accuracy. In this paper, we compare the effect of different models on hemodynamics in relation to their parameters. The fidelity and efficiency of the different models to capture wire screen effect on fluid flow are quantitatively analyzed and compared.

Effects on Aortoiliac Fluid Dynamics After Endovascular Sealing of Abdominal Aneurysms.

OBJECTIVES:: To evaluate the effects on aortoiliac fluid dynamics after the implantation of an endograft based on endovascular aneurysm sealing (EVAS) versus endovascular aneurysm repair (EVAR) strategy. METHODS:: An adaptive geometrical deformable model was used for aortic lumen segmentation in 8 patients before and after the surgery. Abdominal aneurysms were treated with an endograft based on the EVAS system (Nellix, n = 4) and with a device based on an anatomical fixation technology (n = 4). Pressure, blood velocity, and wall shear stress (WSS) were estimated at different aortic regions using computational fluid dynamics methods. Physiologic inlet/outlet flow values at the abdominal aorta, the celiac trunk, and the mesenteric and the renal arteries were set. Pressure references were set at iliac arteries outlet. RESULTS:: Maximum aneurysm sizes were similar for both groups in the preoperative scans. The lumen area was lower after EVAR ( P < .05) and EVAS ( P < .01) compared to preoperative aortic lumen sizes. Pressure increase was higher in the proximal abdominal aorta after EVAS compared to EVAR (2.3 ± 0.3 mm Hg vs 0.9 ± 0.3 mm Hg, P < .001). Peak blood velocities inside the endografts were 3-fold higher for EVAS compared to EVAR (54 ± 5 cm/s vs 17 ± 4 cm/s, P < .01). Velocities at the iliac arteries also remained higher for EVAS (38 ± 4 cm/s vs 24 ± 4 cm/s, P < .05). Peak WSS at the iliac arteries remained higher for EVAS compared to EVAR group ( P < .05). CONCLUSION:: The significant modification of the aortic bifurcation anatomy after EVAS alters aortoiliac fluid dynamics, showing a pressure impact at the renal arteries level and an acceleration of the blood velocity at the iliac region with a concomitant increase in peak WSS.

Carotid Intima Media Thickness Reference Intervals for a Healthy Argentinean Population Aged 11-81 Years.

Reference intervals (RIs) of carotid intima media thickness (CIMT) from large healthy population are still lacking in Latin America. The aim of this study was to determine CIMT RIs in a cohort of 1012 healthy subjects from Argentina. We evaluated if RIs for males and females and for left and right carotids were necessary. Second, mean and standard deviation (SD) age-related equations were obtained for left, right, and average (left + right)/2) CIMT using parametric regression methods based on fractional polynomials, in order to obtain age-specific percentiles curves. Age-specific percentile curves were obtained. Males showed higher A-CIMT (0.577 ± 0.003 mm versus 0.566 ± 0.004 mm, P = 0.039) in comparison with females. For males, the equations were as follows: A-CIMT mean = 0.42 + 8.14 × 10-5⁎Age2; A-CIMT SD = 5.9 × 10-2 + 1.09 × 10-5⁎Age2. For females, they were as follows: A-CIMT mean = 0.40 + 8.20 × 10-5⁎Age2; A-CIMT SD = 4.67 × 10-2 + 1.63 × 10-5⁎Age2. Our study provides the largest database concerning RIs of CIMT in healthy people in Argentina. Specific RIs and percentiles of CIMT for children, adolescents, and adults are now available according to age and gender, for right and left common carotid arteries.

Validation of an Open-Source Tool for Measuring Carotid Lumen Diameter and Intima-Media Thickness.

In low- and middle-income regions, a relatively large number of deaths occur from cardiovascular disease or stroke. Carotid intima-media thickness (cIMT) and carotid lumen diameter (cLD) are strong indicators of cardiovascular event risk and stenosis severity, respectively. The interactive open-source software described here, Cimtool, is based on active contours for measuring these indicators in clinical practice and thus helping in preventive diagnosis and treatment. Cimtool was validated using carotid phantoms and real images obtained using ultrasound. Expert users measured cIMT and cLD in regular practice and also with Cimtool. The results obtained with Cimtool were then compared with the results for the manual approach in terms of measurement agreement, time spent on the measurements and usability. Intra-observer variability when using Cimtool was also analyzed. Statistical analysis revealed strong agreement between the manual method and Cimtool (p > 0.01 for cIMT and cLD). The correlation coefficient for both cIMT and cLD measurements was r > 0.9. Moreover, this software allowed the users to spend considerably less time on each measurement (3.5 min per study versus 50 s with Cimtool on average). An open-source, interactive, validated tool for measuring cIMT and cLD clinically was thus developed. Compared with the manual approach, Cimtool's straightforward measurement flow allows the user to spend less time per measurement and has less standard deviation. The coefficients of variation for measurements and intra-observer variability were lower than those reported for recent automated approaches, even with low-quality images.