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Patient-specific models of blood flow are being used clinically to diagnose and plan treatment for coronary artery disease. A remaining challenge is bridging scales from flow in arteries to the micro-circulation supplying the myocardium. Previously proposed models are descriptive rather than predictive and have not been applied to human data. The goal here is to develop a multiscale patient-specific model enabling blood flow simulation from large coronary arteries to myocardial tissue. Patient vasculatures are segmented from coronary computed tomography angiography data and extended from the image-based model down to the arteriole level using a space-filling forest of synthetic trees. Blood flow is modeled by coupling a 1D model of the coronary arteries to a single-compartment Darcy myocardium model. Simulated results on five patients with non-obstructive coronary artery disease compare overall well to [$$^{15}$$ 15 O]$$\text {H}_{{2}}$$ H 2 O PET exam data for both resting and hyperemic conditions. Results on a patient with severe obstructive disease link coronary artery narrowing with impaired myocardial blood flow, demonstrating the model’s ability to predict myocardial regions with perfusion deficit. This is the first report of a computational model for simulating blood flow from the epicardial coronary arteries to the left ventricle myocardium applied to and validated on human data.

Author(s): Chen, Zhennong; Contijoch, Francisco; Schluchter, Andrew; Grady, Leo; Schaap, Michiel; Stayman, Web; Pack, Jed; McVeigh, Elliot | Abstract: PurposeCoronary x-ray computed tomography angiography (CCTA) continues to develop as a noninvasive method for the assessment of coronary vessel geometry and the identification of physiologically significant lesions. The uncertainty of quantitative lesion diameter measurement due to limited spatial resolution and vessel motion reduces the accuracy of CCTA diagnoses. In this paper, we introduce a new technique called computed tomography (CT)-number-Calibrated Diameter to improve the accuracy of the vessel and stenosis diameter measurements with CCTA.MethodsA calibration phantom containing cylindrical holes (diameters spanning from 0.8nmm through 4.0nmm) capturing the range of diameters found in human coronary vessels was three-dimensional printed. We also printed a human stenosis phantom with 17 tubular channels having the geometry of lesions derived from patient data. We acquired CT scans of the two phantoms with seven different imaging protocols. Calibration curves relating vessel intraluminal maximum voxel value (maximum CT number of a voxel, described in Hounsfield Units, HU) to true diameter, and full-width-at-half maximum (FWHM) to true diameter were constructed for each CCTA protocol. In addition, we acquired scans with a small constant motion (15nmm/s) and used a motion correction reconstruction (Snapshot Freeze) algorithm to correct motion artifacts. We applied our technique to measure the lesion diameter in the 17 lesions in the stenosis phantom and compared the performance of CT-number-Calibrated Diameter to the ground truth diameter and a FWHM estimate.ResultsIn all cases, vessel intraluminal maximum voxel value vs diameter was found to have a simple functional form based on the two-dimensional point spread function yielding a constant maximum voxel value region above a cutoff diameter, and a decreasing maximum voxel value vs decreasing diameter below a cutoff diameter. After normalization, focal spot size and reconstruction kernel were the principal determinants of cutoff diameter and the rate of maximum voxel value reduction vs decreasing diameter. The small constant motion had a significant effect on the CT number calibration; however, the motion-correction algorithm returned the maximum voxel value vs diameter curve to that of stationary vessels. The CT number Calibration technique showed better performance than FWHM estimation of diameter, yielding a high accuracy in the tested range (0.8nmm through 2.5nmm). We found a strong linear correlation between the smallest diameter in each of 17 lesions measured by CT-number-Calibrated Diameter (DC ) and ground truth diameter (Dgt ), (DC n=n0.951n×nDgt n+n0.023nmm, rn=n0.998 with a slope very close to 1.0 and intercept very close to 0nmm.ConclusionsComputed tomography-number-Calibrated Diameter is an effective method to enhance the accuracy of the estimate of small vessel diameters and degree of coronary stenosis in CCTA.

Image registration with deep neural networks has become an active field of research and exciting avenue for a long standing problem in medical imaging. The goal is to learn a complex function that maps the appearance of input image pairs to parameters of a spatial transformation in order to align corresponding anatomical structures. We argue and show that the current direct, non-iterative approaches are sub-optimal, in particular if we seek accurate alignment of Structures-of-Interest (SoI). Information about SoI is often available at training time, for example, in form of segmentations or landmarks. We introduce a novel, generic framework, Image-and-Spatial Transformer Networks (ISTNs), to leverage SoI information allowing us to learn new image representations that are optimised for the downstream registration task. Thanks to these representations we can employ a test-specific, iterative refinement over the transformation parameters which yields highly accurate registration even with very limited training data. Performance is demonstrated on pairwise 3D brain registration and illustrative synthetic data., Comment: Accepted at MICCAI 2019. Code available on https://github.com/biomedia-mira/istn

Predictive modeling of blood flow and pressure have numerous applications ranging from non-invasive assessment of functional significance of disease to planning invasive procedures. While several such predictive modeling techniques have been proposed, their use in the clinic has been limited due in part to the significant time required to perform virtual interventions and compute the resultant changes in hemodynamic conditions. We propose a fast hemodynamic assessment method to aid in interventional planning based on first constructing an exploration space of geometries, tailored to each patient, and subsequently building a physics driven reduced order model in this space. We demonstrate that this method can predict fractional flow reserve derived from coronary computed tomography angiography in response to changes to a patient-specific lumen geometry in real time while achieving high accuracy when compared to computational fluid dynamics simulations. We validated this method on over 1300 patients that received a coronary CT scan and demonstrated a correlation coefficient of 0.98 with an error of 0 . 005 ± 0 . 015 (95% CI: (-0.020, 0.031)) as compared to three-dimensional blood flow calculations. This technology is implemented in a product that has received clearance by the U.S. Food and Drug Administration and is being used clinically to enable physicians to predict changes in blood flow resulting from removal of coronary stenoses as might occur with percutaneous coronary interventions. This technology is also cleared for use in Japan and pending regulatory approval in Europe.

AIMS The aim of this study was to evaluate the accuracy of minimum lumen area (MLA) by coronary computed tomography angiography (cCTA) and its impact on fractional flow reserve (FFRCT). METHODS AND RESULTS Fifty-seven patients (118 lesions, 72 vessels) who underwent cCTA and optical coherence tomography (OCT) were enrolled. OCT and cCTA were co-registered and MLAs were measured with both modalities. FFROCT was calculated using OCT-updated models with cCTA-based lumen geometry replaced by OCT-derived geometry. Lesions were grouped by Agatston score (AS) and minimum lumen diameter (MLD) using the OCT catheter and guidewire size (1.0 mm) as a threshold. For all lesions, the average absolute difference between cCTA and OCT MLA was 0.621±0.571 mm2. Pearson correlation coefficients between cCTA and OCT MLAs in lesions with low-intermediate and high AS were 0.873 and 0.787, respectively (both p

Rationale and Objectives The aim of this study was to automatically detect and quantify calcium lesions for the whole heart as well as per coronary artery on non-contrast-enhanced cardiac computed tomographic images. Materials and Methods Imaging data from 366 patients were randomly selected from patients who underwent computed tomographic calcium scoring assessments between July 2004 and May 2009 at Erasmum MC, Rotterdam. These data included data sets with 1.5-mm and 3.0-mm slice spacing reconstructions and were acquired using four different scanners. The scores of manual observers, who annotated the data using commercially available software, served as ground truth. An automatic method for detecting and quantifying calcifications for each of the four main coronary arteries and the whole heart was trained on 209 data sets and tested on 157 data sets. Statistical testing included determining Pearson's correlation coefficients and Bland-Altman analysis to compare performance between the system and ground truth. Wilcoxon's signed-rank test was used to compare the interobserver variability to the system's performance. Results Automatic detection of calcified objects was achieved with sensitivity of 81.2% per calcified object in the 1.5-mm data set and sensitivity of 86.6% per calcified object in the 3.0-mm data set. The system made an average of 2.5 errors per patient in the 1.5-mm data set and 2.2 errors in the 3.0-mm data set. Pearson's correlation coefficients of 0.97 ( P R values over Agatston, mass, and volume scores for each of the arteries (left circumflex coronary artery, right coronary artery, and left main and left anterior descending coronary arteries) were 0.93, 0.96, and 0.99, respectively, for the 1.5-mm scans. Similarly, for 3.0-mm scans, R values were 0.94, 0.94, and 0.99, respectively. Risk category assignment was correct in 95% and 89% of the data sets in the 1.5-mm and 3-mm scans. Conclusions An automatic vessel-specific coronary artery calcium scoring system was developed, and its feasibility for calcium scoring in individual vessels and risk category classification has been demonstrated.

Quantitative information about the geometry of the carotid artery bifurcation is relevant for investigating the onset and progression of atherosclerotic disease. This paper proposes an automatic approach for quantifying the carotid bifurcation angle, carotid area ratio, carotid bulb size and the vessel tortuosity from multispectral MRI. First, the internal and external carotid centerlines are determined by finding a minimum cost path between user-defined seed points where the local costs are based on medialness and intensity. The minimum cost path algorithm is iteratively applied after curved multi-planar reformatting to refine the centerline. Second, the carotid lumen is segmented using a topology preserving geodesic active contour which is initialized by the extracted centerlines and steered by the MR intensities. Third, the bifurcation angle and vessel tortuosity are automatically extracted from the segmented lumen. The methods for centerline tracking and lumen segmentation are evaluated by comparing their accuracy to the inter- and intra-observer variability on 48 datasets (96 carotid arteries) acquired as part of a longitudinal population study. The evaluation reveals that 94 of 96 carotid arteries are segmented successfully. The distance between the tracked centerlines and the reference standard (0.33 mm) is similar to the inter-observer variation (0.32 mm). The lumen segmentation accuracy (average DSC = 0.89, average mean absolute surface distance = 0.31 mm) is close to the inter-observer variation (average dice = 0.92, average mean surface distance = 0.23 mm). The correlation coefficient of manually and automaticly derived bifurcation angle, carotid proximal area ratio, carotid proximal bulb size and vessel totuosity quantifications are close to the correlation of these measures between observers. This demonstrates that the automated method can be used for replacing manual centerline annotation and manual contour drawing for lumen segmentation in MRIs data prior to quantifying the carotid bifurcation geometry. (c) 2012 Elsevier B.V. All rights reserved.

Studying joint kinematics is of interest to improve prosthesis design and to characterize postoperative motion. State of the art techniques register bones segmented from prior computed tomography or magnetic resonance scans with X-ray fluoroscopic sequences. Elimination of the prior 3D acquisition could potentially lower costs and radiation dose. Therefore, we propose to substitute the segmented bone surface with a statistical shape model based estimate. A dedicated dynamic reconstruction and tracking algorithm was developed estimating the shape based on all frames, and pose per frame. The algorithm minimizes the difference between the projected bone contour and image edges. To increase robustness, we employ a dynamic prior, image features, and prior knowledge about bone edge appearances. This enables tracking and reconstruction from a single initial pose per sequence. We evaluated our method on the distal femur using eight biplane fluoroscopic drop-landing sequences. The proposed dynamic prior and features increased the convergence rate of the reconstruction from 71% to 91%, using a convergence limit of 3 mm. The achieved root mean square point-to-surface accuracy at the converged frames was 1.48 +/- 0.41 mm. The resulting tracking precision was 1-1.5 mm, with the largest errors occurring in the rotation around the femoral shaft (about 2.5 degrees precision).

Objective: We evaluated the ability of 64-slice multidetector computed tomography (MDCT)-derived plaque parameters to detect and quantify coronary atherosclerosis, using intravascular ultrasound (IVUS) as the reference standard. Methods: In 32 patients, IVUS and 64-MDCT was performed. The MDCT and IVUS datasets of 44 coronary arteries were co-registered using a newly developed fusion technique and quantitative parameters were derived from both imaging modalities. The threshold of >0.5mm of maximum wall thickness was used to establish plaque presence on MDCT and IVUS. Results: We analyzed 1364 coregistered 1-mm coronary cross-sections and 255 segments of 5-mm length. Compared with IVUS, 64-MDCT enabled correct detection in 957 of 1109 cross-sections containing plaque (sensitivity 86%). In 180 of 255 cross-sections atherosclerosis was correctly excluded (specificity 71%). On the segmental level, MDCT detected 213 of 220 segments with any atherosclerotic plaque (sensitivity 96%), whereas the presence of any plaque was correctly ruled out in 28 of 32 segments (specificity 88%). Interobserver agreement for the detection of atherosclerotic cross-sections was moderate (Cohen's kappa coefficient K = 0.51), but excellent for the atherosclerotic segments (K = 1.0). Pearson's correlation coefficient for vessel plaque volumes measured by MDCT and IVUS was r = 0.91 (p < 0.001). Bland-Altman analysis showed a slight non-significant underestimation of any plaque volume by MDCT (p = 0.5), with a trend to underestimate noncalcified and overestimate mixed/calcified plaque volumes (p = 0.22 and p = 0.87 respectively). Conclusion: MDCT is able to detect and quantify atherosclerotic plaque. Further improvement in CT resolution is necessary for more reliable assessment of very small and distal coronary plaques. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

Purpose: To evaluate dual energy based methods for bone removal in computed tomography angiography (CTA) images and compare these with single energy based methods that use an additional, nonenhanced, CT scan. Methods: Four different bone removal methods were applied to CT scans of an anthropomorphic thorax phantom, acquired with a second generation dual source CT scanner. The methods differed by the way information on the presence of bone was obtained (either by using an additional, nonenhanced scan or by scanning with two tube voltages at the same time) and by the way the bone was removed from the CTA images (either by masking or subtracting the bone). The phantom contained parts which mimic vessels of various diameters in direct contact with bone. Both a quantitative and qualitative analysis of image quality after bone removal was performed. Image quality was quantified by the contrast-to-noise ratio (CNR) normalized to the square root of the dose (CNRD). At locations where vessels touch bone, the quality of the bone removal and the vessel preservation were visually assessed. The dual energy based methods were assessed with and without the addition of a 0.4 mm tin filter to the high voltage x-ray tube filtration. For each bone removal method, the dose required to obtain a certain CNR after bone removal was compared with the dose of a reference scan with the same CNR but without automated bone removal. The CNRD value of the reference scan was maximized by choosing the lowest tube voltage available. Results: All methods removed the bone completely. CNRD values were higher for the masking based methods than for the subtraction based methods. Single energy based methods had a higher CNRD value than the corresponding dual energy based methods. For the subtraction based dual energy method, tin filtration improved the CNRD value with approximately 50%. For the masking based dual energy method, it was easier to differentiate between iodine and bone when tin filtration was applied. The CNRD value decreased only with 4% in that case. Compared to the dual scan based methods, the dual energy based methods had the advantage that only a single scan was made without the need of image registration. This might be easier to implement in clinical practice. Vessel preservation was better with bone subtraction than with bone masking. Smaller vessels were completely occluded by the bone mask. None of the bone removal methods was dose neutral. Conclusions: In general, dual scan based methods that use the lowest tube voltage available, have a higher CNR than the dual energy based approaches at the same dose level. Tin filtration improves the ability to differentiate between iodine and bone for the dual energy based masking method. In clinical practice, the advantages of the dual energy masking method might outweigh its disadvantage of a slightly higher dose penalty compared to the conventional dual scan masking method. (C) 2011 American Association of Physicists in Medicine. [DOI: 10.1118/1.3651475]

A registration method for motion estimation in dynamic medical imaging data is proposed. Registration is performed directly on the dynamic image, thus avoiding a bias towards a specifically chosen reference time point. Both spatial and temporal smoothness of the transformations are taken into account. Optionally, cyclic motion can be imposed, which can be useful for visualization (viewing the segmentation sequentially) or model building purposes. The method is based on a 3D (2D + time) or 4D (3D + time) free-form B-spline deformation model, a similarity metric that minimizes the intensity variances over time and constrained optimization using a stochastic gradient descent method with adaptive step size estimation. The method was quantitatively compared with existing registration techniques on synthetic data and 3D + t computed tomography data of the lungs. This showed subvoxel accuracy while delivering smooth transformations, and high consistency of the registration results. Furthermore, the accuracy of semi-automatic derivation of left ventricular volume curves from 3D + t computed tomography angiography data of the heart was evaluated. On average, the deviation from the curves derived from the manual annotations was approximately 3%. The potential of the method for other imaging modalities was shown on 2D + t ultrasound and 2D + t magnetic resonance images. The software is publicly available as an extension to the registration package elastix.

International audience; This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of the reference standard and the evaluation measures. This framework has been introduced at the MICCAI 2009 workshop 3D Segmentation in the Clinic: A Grand Challenge III, and we compare the results of eight teams that participated. These results show that automated segmentation of the vessel lumen is possible with a precision that is comparable to manual annotation. The framework is open for new submissions through the website http://cls2009.bigr.nl. The final accepted version of this paper is published in Medical Image Analysis, vol 15(4) ,doi:10.1016/j.media.2011.02.004

Purpose: Computed tomography angiography (CTA) is increasingly used for the diagnosis of coronary artery disease (CAD). However, CTA is not commonly used for the assessment of ventricular and atrial function, although functional information extracted from CTA data is expected to improve the diagnostic value of the examination. In clinical practice, the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction, requires accurate delineation of cardiac chambers. In this paper, we investigated the accuracy and robustness of cardiac chamber delineation using a multiatlas based segmentation method on multicenter and multivendor CTA data. Methods: A fully automatic multiatlas based method for segmenting the whole heart (i.e., the outer surface of the pericardium) and cardiac chambers from CTA data is presented and evaluated. In the segmentation approach, eight atlas images are registered to a new patient's CTA scan. The eight corresponding manually labeled images are then propagated and combined using a per voxel majority voting procedure, to obtain a cardiac segmentation. Results: The method was evaluated on a multicenter/multivendor database, consisting of (1) a set of 1380 Siemens scans from 795 patients and (2) a set of 60 multivendor scans (Siemens, Philips, and GE) from different patients, acquiredmore » in six different institutions worldwide. A leave-one-out 3D quantitative validation was carried out on the eight atlas images; we obtained a mean surface-to-surface error of 0.94{+-}1.12 mm and an average Dice coefficient of 0.93 was achieved. A 2D quantitative evaluation was performed on the 60 multivendor data sets. Here, we observed a mean surface-to-surface error of 1.26{+-}1.25 mm and an average Dice coefficient of 0.91 was achieved. In addition to this quantitative evaluation, a large-scale 2D and 3D qualitative evaluation was performed on 1380 and 140 images, respectively. Experts evaluated that 49% of the 1380 images were very accurately segmented (below 1 mm error) and that 29% were accurately segmented (error between 1 and 3 mm), which demonstrates the robustness of the presented method. Conclusions: A fully automatic method for whole heart and cardiac chamber segmentation was presented and evaluated using multicenter/multivendor CTA data. The accuracy and robustness of the method were demonstrated by successfully applying the method to 1420 multicenter/multivendor data sets.« less

Wall shear stress, the force per area acting on the lumen wall due to the blood flow, is an important biomechanical parameter in the localization and progression of atherosclerosis. To calculate shear stress and relate it to atherosclerosis, a 3D description of the lumen and vessel wall is required. We present a framework to obtain the 3D reconstruction of human coronary arteries by the fusion of intravascular ultrasound (IVUS) and coronary computed tomography angiography (CT). We imaged 23 patients with IVUS and CT. The images from both modalities were registered for 35 arteries, using bifurcations as landmarks. The IVUS images together with IVUS derived lumen and wall contours were positioned on the 3D centerline, which was derived from CT. The resulting 3D lumen and wall contours were transformed to a surface for calculation of shear stress and plaque thickness. We applied variations in selection of landmarks and investigated whether these variations influenced the relation between shear stress and plaque thickness. Fusion was successfully achieved in 31 of the 35 arteries. The average length of the fused segments was 36.4 ± 15.7 mm. The length in IVUS and CT of the fused parts correlated excellently (R (2) = 0.98). Both for a mildly diseased and a very diseased coronary artery, shear stress was calculated and related to plaque thickness. Variations in the selection of the landmarks for these two arteries did not affect the relationship between shear stress and plaque thickness. This new framework can therefore successfully be applied for shear stress analysis in human coronary arteries.

Item does not contain fulltext We propose and validate a semi-automatic method for lumen segmentation of the carotid bifurcation in computed tomography angiography (CTA). First, the central vessel axis is obtained using path tracking between three user-defined points. Second, starting from this path, the segmentation is automatically obtained using a level set. The cost and speed functions for path tracking and segmentation make use of intensity and homogeneity slice-based image features. The method is validated on a large data set of 234 carotid bifurcations of 129 ischemic stroke patients with atherosclerotic disease. The results are compared to manually obtained lumen segmentations. Parameter optimization is carried out on a subset of 30 representative carotid bifurcations. With the optimized parameter settings the method successfully tracked the central vessel paths in 201 of the remaining 204 bifurcations (99%) which were not part of the training set. Comparison with manually drawn segmentations shows that the average overlap between the method and observers is similar (for the inter-observer set the results were 92% vs. 87% and for the intra-observer set 94% vs. 94%). Therefore the method has potential to replace the manual procedure of lumen segmentation of the atherosclerotic bifurcation in CTA. 01 december 2010

Purpose: The application and large-scale evaluation of minimum cost path approaches for coronary centerline extraction from computed tomography coronary angiography (CTCA) data and the development and evaluation of a novel method to reduce the user-interaction time. Methods: A semiautomatic method based on a minimum cost path approach is evaluated for two different cost functions. The first cost function is based on a frequently used vesselness measure and intensity information, and the second is a recently proposed cost function based on region statistics. User interaction is minimized to one or two mouse clicks distally in the coronary artery. The starting point for the minimum cost path search is automatically determined using a newly developed method that finds a point in the center of the aorta in one of the axial slices. This step ensures that all computationally expensive parts of the algorithm can be precomputed. Results: The performance of the aorta localization procedure was demonstrated by a success rate of 100% in 75 images. The success rate and accuracy of centerline extraction was quantitatively evaluated on 48 coronary arteries in 12 images by comparing extracted centerlines with a manually annotated reference standard. The method was able to extract 88% and 47% of the vessel centerlines correctly using the vesselness/intensity and region statistics cost function, respectively. For only the proximal part of the vessels these values were 97% and 86%, respectively. Accuracy of centerline extraction, defined as the average distance from correctly automatically extracted parts of the centerline to the reference standard, was 0.64 mm for the vesselness/intensity and 0.51 mm for the region statistics cost function. The interobserver variability was 99% for the success rate measure and 0.42 mm for the accuracy measure. Qualitative evaluation using the best performing cost function resulted in successful centerline extraction for 233 out of the 252 coronaries (92%) in 63 additional CTCA images. Conclusions: The presented results, in combination with minimal user interaction and low computation time, show that minimum cost path approaches can effectively be applied as a preprocessing step for subsequent analysis in clinical practice and biomedical research.

Objectives: To support policies to tackle socio-economic inequalities in smoking, monitoring systems should include information on smoking according to socio-economic position (SEP). This paper aims to review the methods applied in recent scientific studies on inequalities in smoking, with the aim of drawing lessons for the monitoring of smoking inequalities. Study design: Literature review. Methods: Seventy studies on socio-economic inequalities in smoking, published since 1990, were selected and reviewed, with particular focus on study design, indicators of SEP and smoking outcomes. Results: Most studies had a cross-sectional design and measured smoking prevalence rates among adults in relation to educational level. In addition to educational level, measures of household wealth and occupational class had strong associations with smoking outcomes. In addition to smoking prevalence, other Outcome measures such as initiation rates, cessation rates and consumption level are needed to provide in-depth knowledge of the effect of SEP on smoking, especially from a life-course perspective. Conclusions: It is recommended that, as well as educational level, other socio-economic indicators should be used to identify socio-economic groups where smoking rates are highest. Estimates of inequalities in initiation and cessation rates are needed to identify the most important age groups and entry points for policies to tackle inequalities in smoking. (C) 2008 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Accurate detection and quantification of coronary artery stenoses is an essential requirement for treatment planning of patients with suspected coronary artery disease. We present a method to automatically detect and quantify coronary artery stenoses in computed tomography coronary angiography. First, centerlines are extracted using a two-point minimum cost path approach and a subsequent refinement step. The resulting centerlines are used as an initialization for lumen segmentation, performed using graph cuts. Then, the expected diameter of the healthy lumen is estimated by applying robust kernel regression to the coronary artery lumen diameter profile. Finally, stenoses are detected and quantified by computing the difference between estimated and expected diameter profiles. We evaluated our method using the data provided in the Coronary Artery Stenoses Detection and Quantification Evaluation Framework. Using 30 testing datasets, the method achieved a detection sensitivity of 29 % and a positive predictive value (PPV) of 24 % as compared to quantitative coronary angiography (QCA), and a sensitivity of 21 % and a PPV of 23 % as compared manual assessment based on consensus reading of CTA by 3 observers. The stenoses degree was estimated with an absolute average difference of 31 %, a root mean square difference of 39.3 % when compared to QCA, and a weighted kappa value of 0.29 when compared to CTA. A Dice of 68 and 65 % was reported for lumen segmentation of healthy and diseased vessel segments respectively. According to the ranking of the evaluation framework, our method finished fourth for stenosis detection, second for stenosis quantification and second for lumen segmentation.

The accuracy of fractional flow reserve derived from coronary CT (FFRCT) depends on the fidelity of the lumen boundary. It is unknown whether the lower spatial resolution of coronary CT compared to invasive imaging limits the accuracy of FFRCT or whether imprecision in minimum lumen area (MLA) at a

The relationship between hemodynamic forces acting on plaques and plaque vulnerability remain unclear. A total of 21 patients with 25 lesions underwent coronary computed tomography angiography (CCTA) and optical coherence tomography (OCT). Each plaque was partitioned into 8 equally-separated radial

Wall shear stress (WSS) is hypothesized to influence plaque initiation and progression. Axial plaque stress (APS), the axial component of force acting on plaque, is a recently introduced hemodynamic metric which may affect plaque rupture (Figure 1). Whether APS affects plaque initiation or

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3 +/- 0.5 mm, compared to values of 2.7 +/- 0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.

Atherosclerosis is characterized by lipid accumulation in the arterial wall, followed by an inflammatory response. Plaque formation is generally observed near bifurcations in coronary arteries. The composition of atherosclerotic plaques depends on the location, and it was hypothesized that blood flow induced shear stress influences plaque composition2. To study the impact of shear stress on atherosclerotic disease in human coronary arteries, we developed a technique that enables us to generate 3D lumen reconstruction based on multislice computer tomography (MSCT) and intravascular ultrasound (IVUS).We describe two approaches to generate 3D reconstructions of human coronary artery bifurcations and apply them to coronary segments with bifurcations. We will evaluate the effect on shear stress distribution and its relationship to wall thickness.Copyright © 2012 by ASME

Lumen segmentation is relevant for quantification of the degree of stenoses and as preprocessing step for the analysis of the vessel wall, and for the simulation and planing of interventions. Although lumen segmentation in CTA has received considerable interest, it remains a challenge for atherosclerotic vessels. In this paper we propose a semi-automatic lumen segmentation method using a levelset approach, which utilizes both boundary information and regional intensity information to accurately segment the lumen of atherosclerotic vessels. The method was trained and evaluated on a publicly available database of 56 carotid arteries. The average Dice similarity coefficient was 88.9 %, the mean absolute surface distance was 0.31 mm. Compared to using only boundary information, this method yields better segmentation in severely atheroslerotic vessels.

In clinical practice, detection and quantification of calcified lesions in the coronary arteries and aorta is normally performed on non contrast enhanced Computed Tomography (CT) scans. This CT scan is generally followed by a contrast enhanced angiography (CTA) scan for better plaque visualization. We propose and evaluate a method for the detection of calcified lesions on CTA scans. Calcium lesion detection is straightforward on a CT scan, however performing the same task on a contrast enhanced angiography (CTA) scan is challenging, as the intensity of the contrast enhanced vessel lumen is very similar to that of calcium. Our method is based on semi-automatic centreline extraction and subsequent segmentation of high intensity objects to detect calcified lesions in the descending aorta. The method is evaluated on 19 datasets, and the results are compared to manual detection of calcifications on the CT scan. The proposed method has an accuracy of 82.4%, and 91% of the calcified lesions were successfully detected on the CTA scans.

In clinical practice, both coronary anatomy and myocardial perfusion information are needed to assess coronary artery disease (CAD). The extent and severity of coronary stenoses can be determined using computed tomography coronary angiography (CTCA); the presence and amount of ischemia can be identified using myocardial perfusion imaging, such as perfusion magnetic resonance imaging (PMR). To determine which specific stenosis is associated with which ischemic region, experts use assumptions on coronary perfusion territories. Due to the high variability between patient's coronary artery anatomies, as well as the uncertain relation between perfusion territories and supplying coronary arteries, patient-specific systems are needed. We present a patient-specific visualization system, called Synchronized Multimodal heART Visualization (SMARTVis), for relating coronary stenoses and perfusion deficits derived from CTCA and PMR, respectively. The system consists of the following comprehensive components: (1) two or three-dimensional fusion of anatomical and functional information, (2) automatic detection and ranking of coronary stenoses, (3) estimation of patient-specific coronary perfusion territories. The potential benefits of the SMARTVis tool in assessing CAD were investigated through a case-study evaluation (conventional vs. SMARTVis tool): two experts analyzed four cases of patients with suspected multivessel coronary artery disease. When using the SMARTVis tool, a more reliable estimation of the relation between perfusion deficits and stenoses led to a more accurate diagnosis, as well as a better interobserver diagnosis agreement. The SMARTVis comprehensive visualization system can be effectively used to assess disease status in multivessel CAD patients, offering valuable new options for the diagnosis and management of these patients.

We propose a 3D+t/2D+t registration strategy to relate preoperative CTA to intraoperative X-ray angiography for improved image guidance during coronary interventions. We first derive 4D coronary models from CTA and then align these models both temporally and spatially to the intraoperative images. Temporal alignment is based on aligning ECG signals, whereas the spatial alignment uses a similarity metric based on centerline projection and fuzzy X-ray segmentation. In the spatial alignment step we use information from multiple time points simultaneously and take into account rigid respiratory motion. Evaluation shows improved performance compared to a 3D/2D approach with respect to both registration success and reproducibility.

This paper presents a vessel segmentation method which learns the geometry and appearance of vessels in medical images from annotated data and uses this knowledge to segment vessels in unseen images. Vessels are segmented in a coarse-to-fine fashion. First, the vessel boundaries are estimated with multi-variate linear regression using image intensities sampled in a region of interest around an initialization curve. Subsequently, the position of the vessel boundary is refined with a robust nonlinear regression technique using intensity profiles sampled across the boundary of the rough segmentation and using information about plausible cross-sectional vessel shapes. The method was evaluated by quantitatively comparing segmentation results to manual annotations of 229 coronary arteries. On average the difference between the automatically obtained segmentations and manual contours was smaller than the inter-observer variability, which is an indicator that the method outperforms manual annotation. The method was also evaluated by using it for centerline refinement on 24 publicly available datasets of the Rotterdam Coronary Artery Evaluation Framework. Centerlines are extracted with an existing method and refined with the proposed method. This combination is currently ranked second out of 10 evaluated interactive centerline extraction methods. An additional qualitative expert evaluation in which 250 automatic segmentations were compared to manual segmentations showed that the automatically obtained contours were rated on average better than manual contours.

We propose a minimum cost path approach to track the centerlines of the internal and external carotid arteries in multispectral MR data. User interaction is limited to the annotation of three seed points. The cost image is based on both a measure of vessel medialness and lumen intensity similarity in two MRA image sequences: Black Blood MRA and Phase Contrast MRA. After intensity inhomogeneity correction and noise reduction, the two images are aligned using affine registration. The two parameters that control the contrast of the cost image were determined in an optimization experiment on 40 training datasets. Experiments on the training datasets also showed that a cost image composed of a combination of gradient-based medialness and lumen intensity similarity increases the tracking accuracy compared to using only one of the constituents. Furthermore, centerline tracking using both MRA sequences outperformed tracking using only one of these MRA images. An independent test set of 152 images from 38 patients served to validate the technique. The centerlines of 148 images were successfully extracted using the parameters optimized on the training sets. The average mean distance to the reference standard, manually annotated centerlines, was 0.98 mm, which is comparable to the in-plane resolution. This indicates that the proposed method has a high potential to replace the manual centerline annotation.

Despite the growing interest in regression based shape estimation, no study has yet systematically compared different regression methods for shape estimation. We aimed to fill this gap by comparing linear regression methods with a special focus on shapes with landmark position uncertainties. We investigate two scenarios: In the first, the uncertainty of the landmark positions was similar in the training and test dataset, whereas in the second the uncertainty of the training and test data were different. Both scenarios were tested on simulated data and on statistical models of the left ventricle estimating the end-systolic shape from end-diastole with landmark uncertainties derived from the segmentation process, and of the femur estimating the 3D shape from one projection with landmark uncertainties derived from the imaging setup. Results show that in the first scenario linear regression methods tend to perform similar. In the second scenario including estimates of the test shape landmark uncertainty in the regression improved results.

A method to obtain motion artifact-free reconstructed images of the coronary arteries is proposed and evaluated. The method relies on the integration of coronary motion estimation in an iterative computed tomography reconstruction technique. Coronary motion fields are derived from a set of coronary centerlines extracted at multiple cardiac phases within the R-R interval. Start and end points are provided by the user in one time-frame only. Corresponding centerline positions are used to determine the motion fields from phase to phase. Finally, dense motion fields are achieved by thin-plate-spline interpolation and are used to perform a motion-corrected iterative reconstruction of a selected region of interest, which results in an effective improvement of the reconstructed image quality.

In this paper a method which combines iterative computed tomography reconstruction and coronary centerline extraction technique to obtain motion artifact-free reconstructed images of the coronary arteries are proposed and evaluated. The method relies on motion-vector fields derived from a set of coronary centerlines extracted at multiple cardiac phases within the R-R interval. Hereto, start and end points are provided by the user in one time-frame only. Using an elastic image registration, these points are propagated to all the remaining cardiac phases. Consequently, a multi-phase three-dimensional coronary centerline is determined by applying a semi-automatic minimum cost path based extraction method. Corresponding centerline positions are used to determine the relative motion-vector fields from phase to phase. Finally, dense motion-vector fields are achieved by thin-plate-spline interpolation and used to perform a motion-corrected iterative reconstruction of a selected region of interest. The performance of the method is validated on five patients, showing the improved sharpness of cardiac motion-corrected gated iterative reconstructions compared to the results achieved by a classical gated iterative method. The results are also compared to known manual and fully automatic coronary artery motion estimation methods. (C) 2012 Elsevier Ltd. All rights reserved.

Computed tomography angiography (CTA), a non-invasive imaging technique, is becoming increasingly popular for cardiac examination, mainly due to its superior spatial resolution compared to MRI. This imaging modality is currently widely used for the diagnosis of coronary artery disease (CAD) but it is not commonly used for the diagnosis of ventricular and atrial function. In this paper, we present a fully automatic method for segmenting the whole heart (i.e. the outer surface of the myocardium) and cardiac chambers from CTA datasets. Cardiac chamber segmentation is particularly valuable for the extraction of ventricular and atrial functional information, such as stroke volume and ejection fraction. With our approach, we aim to improve the diagnosis of CAD by providing functional information extracted from the same CTA data, thus not requiring additional scanning. In addition, the whole heart segmentation method we propose can be used for visualization of the coronary arteries and for obtaining a region of interest for subsequent segmentation of the coronaries, ventricles and atria. Our approach is based on multi-atlas segmentation, and performed within a non-rigid registration framework. A leave-one-out quantitative validation was carried out on 8 images. The method showed a high accuracy, which is reflected in both a mean segmentation error of 1.05±1.30 mm and an average Dice coefficient of 0.93. The robustness of the method is demonstrated by successfully applying the method to 243 additional datasets, without any significant failure.

We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic alignment of pre-operative CTA data with intra-operative X-ray imaging. Due to a trend towards prospective electrocardiogram gating techniques, 4D imaging data, from which motion information could be extracted, is not commonly available. The prediction of motion from shape information is thus relevant for this purpose. Evaluation of the accuracy of the predicted motion was performed using CTA scans of 50 patients, showing an average accuracy of 1.1 mm.

A reliable density estimate for the position of the coronary arteries in Computed Tomography (CT) data is beneficial for many coronary image processing applications, such as vessel tracking, lumen segmentations, and calcium scoring. This paper presents a method for obtaining an estimate of the coronary artery location in CT and CT angiography (CTA). The proposed method constructs a patient-specific coronary density estimate using CTA atlas registration. The method is evaluated by quantifying the overlap of the obtained density estimate with 24 manually annotated centrelines of the lumen. Furthermore, the method is quantitatively evaluated when applied in automatic calcium scoring of the coronary arteries, which is an important risk predictor of coronary artery disease. The obtained results were compared to manual annotations for 170 CT datasets.

Quantitative information about the geometry of the carotid artery bifurcation may help in predicting the development of atherosclerosis. A geodesic active contours based segmentation method combining both gradient and intensity information was developed for semi-automatic, accurate and robust quantification of the carotid bifurcation angle in Black Blood MRA data. The segmentation method was evaluated by comparing its accuracy to inter and intra observer variability on a large dataset that has been acquired as part of a longitudinal population study which investigates the natural progression of carotid atherosclerosis. Furthermore, the method is shown to be robust to initialization differences. The bifurcation angle obtained from the segmented lumen corresponds well with the angle derived from the manual lumen segmentation, which demonstrates that the method has large potential to replace manual segmentations for extracting the carotid bifurcation angle from Black Blood MRA data.

We present a framework for statistical analysis in large cohorts of structural brain connectivity, derived from diffusion weighted MRI. A brain network is defined between subcortical gray matter structures and a cortical parcellation obtained with FreeSurfer. Connectivity is established through minimum cost paths with an anisotropic local cost function and is quantified per connection. The connectivity network potentially encodes important information about brain structure, and can be analyzed using multivariate regression methods. The proposed framework can be used to study the relation between connectivity and e.g. brain function or neurodegenerative disease. As a proof of principle, we perform principal component regression in order to predict age and gender, based on the connectivity networks of 979 middle-aged and elderly subjects, in a 10-fold cross-validation. The results are compared to predictions based on fractional anisotropy and mean diffusivity averaged over the white matter and over the corpus callosum. Additionally, the predictions are performed based on the best predicting connection in the network. Principal component regression outperformed all other prediction models, demonstrating the age and gender information encoded in the connectivity network.

This paper presents a 2-D/3-D registration method for the alignment of cardiac X-ray images to ECG gated CTA data of the coronary arteries. The purpose of our work is to provide visualization of instruments in relation to pre-operative CTA data during interventional cardiology for improved image guidance, especially in complex procedures. The method utilizes the graphical processing unit (GPU) for rendering of digitally reconstructed radiographs (DRRs) from a 3-D CTA-derived coronary model. Using a multi-scale gradient ascent framework, the normalized cross correlation between the simulated and real X-ray image is optimized. Quantitative evaluation is performed by computing the projection error of the vessel centerlines, which was on average 1.34 mm.

Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: (1) a method is described to create a consensus centerline with multiple observers, (2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, (3) a database containing 32 cardiac CTA datasets with corresponding reference standard is described and made available. and (4) 13 coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms. (C) 2009 Elsevier B.V. All rights reserved.

This paper presents a novel method for segmenting the coronary lumen in CTA data. The method is based on graph cuts, with edge-weights depending on the intensity of the centerline, and robust kernel regression. A quantitative evaluation in 28 coronary arteries from 12 patients is performed by comparing the semi-automatic segmentations to manual annotations. This evaluation showed that the method was able to segment the coronary arteries with high accuracy, compared to manually annotated segmentations, which is reflected in a Dice coefficient of 0.85 and average symmetric surface distance of 0.22 mm.

Computed tomography (CT) has a trend towards higher resolution and higher noise. This development has increased the interest in anisotropic smoothing techniques for CT, which aim to reduce noise while preserving structures of interest. However, existing smoothing techniques are slow, which makes clinical application difficult. Furthermore, the published methods have limitations with respect to preserving small details in CT data. This paper presents a widely applicable speed optimized framework for anisotropic smoothing techniques. A second contribution of this paper is an extension to an existing smoothing technique aimed at better preserving small structures of interest in CT data. Based on second-order image structure, the method first determines an importance map, which indicates potentially relevant structures that should be preserved. Subsequently an anisotropic diffusion process is started. The diffused data is used in most parts of the images, while structures with significant second-order information are preserved. The method is qualitatively evaluated against an anisotropic diffusion method without structure preservation in an observer study to assess the improvement of 3-D visualizations of CT series and quantitatively by determining the reduction of the difference between low and high dose CT scans of in vitro carotid plaques.

This paper presents a Bayesian framework for tracking of tubular structures such as vessels. Compared to conventional tracking schemes, its main advantage is its non-deterministic character, which strongly increases the robustness of the method. A key element of our approach is a dedicated observation model for tubular structures in regions with varying intensities. Furthermore, we show how the tracking method can be used to obtain a probabilistic segmentation of the tracked tubular structure. The method has been applied to track the internal carotid artery from CT angiography data of 14 patients (28 carotids) through the skull base. This is a challenging problem, owing to the close proximity of bone, overlap in intensity values of lumen voxels and (partial volume) bone voxels, and the tortuous path of the vessels. The tracking was successful in 25 cases, and the extracted path were found to be close (< 1.0mm) to manually traced paths by two observers.

Tracking of tubular elongated structures is an important goal in a wide range of biomedical imaging applications. A Bayesian tube tracking algorithm is presented that allows to easily incorporate a priori knowledge. Because probabilistic tube tracking algorithms are computationally complex, steps towards a computational efficient implementation are suggested in this paper. The algorithm is evaluated on 2D and 3D synthetic data with different noise levels and clinical CTA data. The approach shows good performance on data with high levels of Gaussian noise.

Featured article on the cover of the April issue of TBME - https://www.embs.org/tbme/articles/generation-of-patient-specific-cardiac-vascular-networks-a-hybrid-image-based-and-synthetic-geometric-model/; International audience; Objective: In this paper, we propose an algorithm for the generation of a patient-specific cardiac vascular network starting from segmented epicardial vessels down to the arterioles. Method: We extend a tree generation method based on satisfaction of functional principles, named Constrained Constructive Optimization (CCO), to account for multiple, competing vascular trees. The algorithm simulates angiogenesis under vascular volume minimization with flow-related and geometrical constraints adapting the simultaneous tree growths to patient priors. The generated trees fill the entire left ventricle myocardium up to the arterioles. Results: From actual vascular tree models segmented from CT images, we generated networks with 6000 terminal segments for 6 patients. These networks contain between 33 and 62 synthetic trees. All vascular models match morphometry properties previously described. Conclusion and significance: Image-based models derived from CT angiography are being used clinically to simulate blood flow in the coronary arteries of individual patients to aid in the diagnosis of disease and planning treatments. However, image resolution limits vessel segmentation to larger epicardial arteries. The generated model can be used to simulate blood flow and derived quantities from the aorta into the myocardium. This is an important step for diagnosis and treatment planning of coronary artery disease.

We present an approach to derive patient specific coronary models from ECG-gated CTA data and their application for the alignment of CTA with mono-plane X-ray imaging during interventional cardiology. A 4D (3D+t) deformation model of the coronary arteries is derived by (i) extraction of a 3D coronary model at an appropriate cardiac phase and (ii) non-rigid registration of the CTA images at different ECG phases to obtain a deformation model. The resulting 4D coronary model is aligned with the X-ray data using a novel 2D+t/3D+t registration approach. Model consistency and accuracy is evaluated using manually annotated coronary centerlines at systole and diastole as reference. Improvement of registration robustness by using the 2D+t/3D+t registration is successfully demonstrated by comparison of the actual X-ray cardiac phase with the automatically determined best matching phase in the 4D coronary model.

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/. (C) 2013 Elsevier B.V. All rights reserved.

This paper presents a semi-automatic coronary centerline extraction algorithm for computed tomography angiography data. The method applies region growing to computed tomography angiography data and incorporates bifurcation and leak detection. The presented method is evaluated either on the original data and on data in which vessel-like structures have been enhanced. Semi-automatically extracted centerlines of the three main coronary arteries are compared with centerlines manually annotated by three observers, using an overlap and distance measure. The method successfully extracted vessel centerlines in up to 15 out of 18 evaluated cases, with a localization accuracy which was in the range of the interobserver variability. Vessel enhancement prior to centerline extraction did not improve the results