[Show abstract][Hide abstract] ABSTRACT: To assess (1) whether normal and degenerated menisci exhibit different T1GD on delayed gadolinium-enhanced MRI of the meniscus (dGEMRIM), (2) the reproducibility of dGEMRIM and (3) the correlation between meniscus and cartilage T1GD in knee osteoarthritis (OA) patients.
In 17 OA patients who underwent dGEMRIM twice within 7 days, meniscus and cartilage T1GD was calculated. Meniscus pathology was evaluated on conventional MRI. T1GD in normal and degenerated menisci were compared using a Student's t-test. Reproducibility was assessed using ICCs. Pearson's correlation was calculated between meniscus and cartilage T1GD.
A trend towards lower T1GD in degenerated menisci (mean: 402 ms; 95 % CI: 359-444 ms) compared to normal menisci (mean: 448 ms; 95 % CI: 423-473 ms) was observed (p = 0.05). Meniscus T1GD ICCs were 0.85-0.90. The correlation between meniscus and cartilage T1GD was moderate in the lateral (r = 0.52-0.75) and strong in the medial compartment (r = 0.78-0.94).
Our results show that degenerated menisci have a clear trend towards lower T1GD compared to normal menisci. Since these results are highly reproducible, meniscus degeneration may be assessed within one delayed gadolinium-enhanced MRI simultaneously with cartilage. The strong correlation between meniscus and cartilage T1GD suggests concomitant degeneration in both tissues in OA, but also suggests that dGEMRIC may not be regarded entirely as sulphated glycosaminoglycan specific.
• dGEMRIM T1 GD can possibly be used to assess meniscal degeneration; • dGEMRIM yields highly reproducible meniscal T1 GD in early stage osteoarthritic patients; • Concomitant degeneration of cartilage and meniscus tissue occurs in early stage osteoarthritis; • dGEMRIC cannot be regarded as entirely sulphated glycosaminoglycan specific.
[Show abstract][Hide abstract] ABSTRACT: To introduce a semiautomatic algorithm to perform the registration of free-hand B-Mode ultrasound (US) and magnetic resonance imaging (MRI) of the carotid artery.
The authors' approach combines geometrical features and intensity information. The only user interaction consists of placing three seed points in US and MRI. First, the lumen centerlines are used as landmarks for point based registration. Subsequently, in a joint optimization the distance between centerlines and the dissimilarity of the image intensities is minimized. Evaluation is performed in left and right carotids from six healthy volunteers and five patients with atherosclerosis. For the validation, the authors measure the Dice similarity coefficient (DSC) and the mean surface distance (MSD) between carotid lumen segmentations in US and MRI after registration. The effect of several design parameters on the registration accuracy is investigated by an exhaustive search on a training set of five volunteers and three patients. The optimum configuration is validated on the remaining images of one volunteer and two patients.
On the training set, the authors achieve an average DSC of 0.74 and a MSD of 0.66 mm on volunteer data. For the patient data, the authors obtain a DSC of 0.77 and a MSD of 0.69 mm. In the independent set composed of patient and volunteer data, the DSC is 0.69 and the MSD is 0.87 mm. The experiments with different design parameters show that nonrigid registration outperforms rigid registration, and that the combination of intensity and point information is superior to approaches that use intensity or points only.
The proposed method achieves an accurate registration of US and MRI, and may thus enable multimodal analysis of the carotid plaque.
Medical Physics 05/2014; 41(5):052904. · 2.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Patients with carotid atherosclerotic plaques carry an increased risk of cardiovascular events such as stroke. Ultrasound has been employed as a standard for diagnosis of carotid atherosclerosis. To assess atherosclerosis, the intima contour of the carotid artery lumen should be accurately outlined. For this purpose, we use simultaneously acquired side-by-side longitudinal contrast enhanced ultrasound (CEUS) and B-mode ultrasound (BMUS) images and exploit the information in the two imaging modalities for accurate lumen segmentation. First, nonrigid motion compensation is performed on both BMUS and CEUS image sequences, followed by averaging over the 150 time frames to produce an image with improved signal-to-noise ratio (SNR). After that, we segment the lumen from these images using a novel method based on dynamic programming which uses the joint histogram of the CEUS and BMUS pair of images to distinguish between background, lumen, tissue and artifacts. Finally, the obtained lumen contour in the improved-SNR mean image is transformed back to each time frame of the original image sequence. Validation was done by comparing manual lumen segmentations of two independent observers with automated lumen segmentations in the improved-SNR images of 9 carotid arteries from 7 patients. The root mean square error between the two observers was 0.17±0.10mm and between automated and average of manual segmentation of two observers was 0.19±0.06mm. In conclusion, we present a robust and accurate carotid lumen segmentation method which overcomes the complexity of anatomical structures, noise in the lumen, artifacts and echolucent plaques by exploiting the information in this combined imaging modality.
[Show abstract][Hide abstract] ABSTRACT: Atherosclerotic plaque composition can indicate plaque vulnerability. We segment atherosclerotic plaque components from the carotid artery on a combination of in vivo MRI and CT-angiography (CTA) data using supervised voxelwise classification. In contrast to previous studies the ground truth for training is directly obtained from 3D registration with histology for fibrous and lipid-rich necrotic tissue, and with [Formula: see text]CT for calcification. This registration does, however, not provide accurate voxelwise correspondence. We therefore evaluate three approaches that incorporate uncertainty in the ground truth used for training: I) soft labels are created by Gaussian blurring of the original binary histology segmentations to reduce weights at the boundaries between components, and are weighted by the estimated registration accuracy of the histology and in vivo imaging data (measured by overlap), II) samples are weighted by the local contour distance of the lumen and outer wall between histology and in vivo data, and III) 10% of each class is rejected by Gaussian outlier rejection. Classification was evaluated on the relative volumes (% of tissue type in the vessel wall) for calcified, fibrous and lipid-rich necrotic tissue, using linear discriminant (LDC) and support vector machine (SVM) classification. In addition, the combination of MRI and CTA data was compared to using only one imaging modality. Best results were obtained by LDC and outlier rejection: the volume error per vessel was 0.9[Formula: see text]1.0% for calcification, 12.7[Formula: see text]7.6% for fibrous and 12.1[Formula: see text]8.1% for necrotic tissue, with Spearman rank correlation coefficients of 0.91 (calcification), 0.80 (fibrous) and 0.81 (necrotic). While segmentation using only MRI features yielded low accuracy for calcification, and segmentation using only CTA features yielded low accuracy for necrotic tissue, the combination of features from MRI and CTA gave good results for all studied components.
PLoS ONE 01/2014; 9(4):e94840. · 3.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Purpose: There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans.Methods: Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability.Results: Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P < 0.001) for computation of the epicardial fat volume.Conclusions: The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.
Medical Physics 09/2013; 40(9):091910. · 2.91 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Multiresolution strategies are commonly used in the nonrigid registration to avoid local minima in the optimization space. Generally, a step-by-step hierarchical approach is adopted, in which the registration starts on a level with reduced complexity (downsampled images, global transformations), then continuing to levels with increased complexity, until the finest level is reached. In this work we propose two alternative multiresolution strategies for both the data model and transformation model, in which different resolution levels are considered simultaneously instead of subsequently. By combining the different strategies for data and transformation, we systematically define 3 3 multiresolution schemes, including both existing and novel methods. Experiments on 10 pairs of CT lung datasets showed that the best performing strategy resulted in a reduction of the upper quartile of the mean target registration error from 2mm to 1.5 mm, compared with the conventionally hierarchical multiresolution method, while achieving smoother deformations. Experiments with intersubject registration of 18 3D T1-weighted MRI brain scans confirmed that simultaneous multiresolution strategies produce more accurate registration results (median of mean overlap increased from 0.55 to 0.57) and smoother deformation fields than the traditionally hierarchical method. Evaluation of robustness indicated that the largest differences in accuracy between methods are observed for structures with a relatively large initial misalignment.
IEEE Transactions on Image Processing 08/2013; · 3.20 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R(2) 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.
[Show abstract][Hide abstract] ABSTRACT: BACKGROUND AND PURPOSE: It is unknown whether white matter lesions (WML) develop abruptly in previously normal brain areas, or whether tissue changes are already present before WML become apparent on MRI. We therefore investigated whether development of WML is preceded by quantifiable changes in normal-appearing white matter (NAWM). METHODS: In 689 participants from the general population (mean age 67 years), we performed 2 MRI scans (including diffusion tensor imaging and Fluid Attenuation Inversion Recovery [FLAIR] sequences) 3.5 years apart using the same 1.5-T scanner. Using automated tissue segmentation, we identified NAWM at baseline. We assessed which NAWM regions converted into WML during follow-up and differentiated new WML into regions of WML growth and de novo WML. Fractional anisotropy, mean diffusivity, and FLAIR intensity of regions converting to WML and regions of persistent NAWM were compared using 3 approaches: a whole-brain analysis, a regionally matched approach, and a voxel-wise approach. RESULTS: All 3 approaches showed that low fractional anisotropy, high mean diffusivity, and relatively high FLAIR intensity at baseline were associated with WML development during follow-up. Compared with persistent NAWM regions, NAWM regions converting to WML had significantly lower fractional anisotropy (0.337 vs 0.387; P<0.001), higher mean diffusivity (0.910×10(-3) mm(2)/s vs 0.729×10(-3) mm(2)/s; P<0.001), and relatively higher normalized FLAIR intensity (1.233 vs -0.340; P<0.001). This applied to both NAWM developing into growing and de novo WML. CONCLUSIONS: White matter changes in NAWM are present and can be quantified on diffusion tensor imaging and FLAIR before WML develop. This suggests that WML develop gradually, and that visually appreciable WML are only the tip of the iceberg of white matter pathology.
[Show abstract][Hide abstract] ABSTRACT: Histology sections provide accurate information on atherosclerotic plaque composition, and are used in various applications. To our knowledge, no automated systems for plaque component segmentation in histology sections currently exist.
We perform pixel-wise classification of fibrous, lipid, and necrotic tissue in Elastica Von Gieson-stained histology sections, using features based on color channel intensity and local image texture and structure. We compare an approach where we train on independent data to an approach where we train on one or two sections per specimen in order to segment the remaining sections. We evaluate the results on segmentation accuracy in histology, and we use the obtained histology segmentations to train plaque component classification methods in ex vivo Magnetic resonance imaging (MRI) and in vivo MRI and computed tomography (CT).
In leave-one-specimen-out experiments on 176 histology slices of 13 plaques, a pixel-wise accuracy of 75.7 ± 6.8% was obtained. This increased to 77.6 ± 6.5% when two manually annotated slices of the specimen to be segmented were used for training. Rank correlations of relative component volumes with manually annotated volumes were high in this situation (P = 0.82-0.98). Using the obtained histology segmentations to train plaque component classification methods in ex vivo MRI and in vivo MRI and CT resulted in similar image segmentations for training on the automated histology segmentations as for training on a fully manual ground truth. The size of the lipid-rich necrotic core was significantly smaller when training on fully automated histology segmentations than when manually annotated histology sections were used. This difference was reduced and not statistically significant when one or two slices per section were manually annotated for histology segmentation.
Good histology segmentations can be obtained by automated segmentation, which show good correlations with ground truth volumes. In addition, these can be used to develop segmentation methods in other imaging modalities. Accuracy increases when one or two sections of the same specimen are used for training, which requires a limited amount of user interaction in practice.
Journal of pathology informatics. 01/2013; 4(Suppl):S3.
[Show abstract][Hide abstract] ABSTRACT: Viscosupplementation with hyaluronic acid (HA) of osteoarthritic (OA) knee joints has a well-established positive effect on clinical symptoms. This effect, however, is only temporary and the working mechanism of HA injections is not clear. It was suggested that HA might have disease modifying properties because of its beneficial effect on cartilage sulphated glycosaminoglycan (sGAG) content. Delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) is a highly reproducible, non-invasive surrogate measure for sGAG content and hence composition of cartilage. The aim of this study was to assess whether improvement in cartilage structural composition is detected using dGEMRIC 14 weeks after 3 weekly injections with HA in patients with early-stage knee OA.
In 20 early-stage knee OA patients (KLG I-II), 3D dGEMRIC at 3T was acquired before and 14 weeks after 3 weekly injections with HA. To evaluate patient symptoms, the knee injury and osteoarthritis outcome score (KOOS) and a numeric rating scale (NRS) for pain were recorded. To evaluate cartilage composition, six cartilage regions in the knee were analyzed on dGEMRIC. Outcomes of dGEMRIC, KOOS and NRS before and after HA were compared using paired t-testing. Since we performed multiple t-tests, we applied a Bonferroni-Holm correction to determine statistical significance for these analyses.
All KOOS subscales ('pain', 'symptoms', 'daily activities', 'sports' and 'quality of life') and the NRS pain improved significantly 14 weeks after Viscosupplementation with HA. Outcomes of dGEMRIC did not change significantly after HA compared to baseline in any of the cartilage regions analyzed in the knee.
Our results confirm previous findings reported in the literature, showing persisting improvement in symptomatic outcome measures in early-stage knee OA patients 14 weeks after Viscosupplementation. Outcomes of dGEMRIC, however, did not change after Viscosupplementation, indicating no change in cartilage structural composition as an explanation for the improvement of clinical symptoms.
PLoS ONE 01/2013; 8(11):e79785. · 3.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We present a new approach for automated segmentation of the carotid lumen bifurcation from 3D free-hand ultrasound using a 3D surface graph cut method. The method requires only the manual selection of single seed points in the internal, external, and common carotid arteries. Subsequently, the centerline between these points is automatically traced, and the optimal lumen surface is found around the centerline using graph cuts. To refine the result, the latter process was iterated. The method was tested on twelve carotid arteries from six subjects including three patients with a moderate carotid artery stenosis. Our method successfully segmented the lumen in all cases. We obtained an average dice overlap with respect to a manual segmentation of 84% for healthy volunteers. For the patient data, we obtained a dice overlap of 66.7%.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2013; 16(Pt 2):542-9.
[Show abstract][Hide abstract] ABSTRACT: Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration. Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of 2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88 and 90%, respectively, both for standard and accelerated registration. We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.
[Show abstract][Hide abstract] ABSTRACT: 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 < .001) for both 1.5-mm and 3.0-mm scans with respect to the calcium volume score of the whole heart were found. The average 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.
[Show abstract][Hide abstract] ABSTRACT: We perform a comparative evaluation of different regression techniques for 3D-2D registration-by-regression. In registration-by-regression, image registration is treated as a nonlinear regression problem that relates image features of 2D projection images to the transformation parameters of the 3D image. In this work, we evaluate seven regression methods: Multiple Linear and Polynomial Regression (LR and PR), k-Nearest Neighbour (k-NN), Multiple Layer Perceptron with conjugate gradient optimization (MLP-CG) and with Levenberg-Marquardt optimization (MLP-LM), Radial Basis Function network (RBF) and Support Vector Regression (SVR). The experiments are performed using simulated X-ray images (DRRs) of nine coronary vessel trees, allowing us to compute the mean target registration error (mTRE) to the ground truth. All methods were robust to large initial misalignment and the highest accuracy was achieved using MLP-LM and RBF.
Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II; 09/2012
[Show abstract][Hide abstract] ABSTRACT: RATIONALE AND OBJECTIVES: Aneurysm morphodynamics is potentially relevant for assessing aneurysm rupture risk. A method is proposed for automated quantification and visualization of intracranial aneurysm morphodynamics from electrocardiogram (ECG)-gated computed tomography angiography (CTA) data. MATERIALS AND METHODS: A prospective study was performed in 19 aneurysms from 14 patients with diagnostic workup for recently discovered aneurysms (n = 15) or follow-up of untreated known aneurysms (n = 4). The study was approved by the Institutional Review Board of the hospital and written informed consent was obtained from each patient. An image postprocessing method was developed for quantifying aneurysm volume changes and visualizing local displacement of the aneurysmal wall over a heart cycle using multiphase ECG-gated (four-dimensional) CTA. Percentage volume changes over the heart cycle were determined for aneurysms, surrounding arteries, and the skull. RESULTS: Pulsation of the aneurysm and its surrounding vasculature during the heart cycle could be assessed from ECG-gated CTA data. The percentage aneurysmal volume change ranged from 3% to 18%. CONCLUSION: ECG-gated CTA can be used to study morphodynamics of intracranial aneurysms. The proposed image analysis method is capable of quantifying the volume changes and visualizing local displacement of the vascular structures over the cardiac cycle.
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVES: To evaluate the effect of automated registration in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) of the knee on the occurrence of movement artefacts on the T1 map and the reproducibility of region-of-interest (ROI)-based measurements. METHODS: Eleven patients with early-stage knee osteoarthritis and ten healthy controls underwent dGEMRIC twice at 3 T. Controls underwent unenhanced imaging. ROIs were manually drawn on the femoral and tibial cartilage. T1 calculation was performed with and without registration of the T1-weighted images. Automated three-dimensional rigid registration was performed on the femur and tibia cartilage separately. Registration quality was evaluated using the square root Cramér-Rao lower bound (CRLB(σ)). Additionally, the reproducibility of dGEMRIC was assessed by comparing automated registration with manual slice-matching. RESULTS: Automated registration of the T1-weighted images improved the T1 maps as the 90% percentile of the CRLB(σ) was significantly (P < 0.05) reduced with a median reduction of 55.8 ms (patients) and 112.9 ms (controls). Manual matching and automated registration of the re-imaged T1 map gave comparable intraclass correlation coefficients of respectively 0.89/0.90 (patients) and 0.85/0.85 (controls). CONCLUSIONS: Registration in dGEMRIC reduces movement artefacts on T1 maps and provides a good alternative to manual slice-matching in longitudinal studies. KEY POINTS: • Quantitative MRI is increasingly used for biomedical assessment of knee articular cartilage • Image registration leads to more accurate quantification of cartilage quality and damage • Movement artefacts in delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) are reduced • Automated image registration successfully aligns baseline and follow-up dGEMRIC examinations • Reproducibility of dGEMRIC with registration is similar to that using manual slice-matching.
[Show abstract][Hide abstract] ABSTRACT: We propose a methodology to register medical images of carotid arteries from tracked freehand sweep B-Mode ultrasound (US) and magnetic resonance imaging (MRI) acquisitions. Successful registration of US and MR images will allow a multimodal analysis of atherosclerotic plaque in the carotid artery. The main challenge is the difference in the positions of the patient's neck during the examinations. While in MRI the patient's neck remains in a natural position, in US the neck is slightly bent and rotated. Moreover, the image characteristics of US and MRI around the carotid artery are very different. Our technique uses the estimated centerlines of the common, internal and external carotid arteries in each modality as landmarks for registration. For US, we used an algorithm based on a rough lumen segmentation obtained by robust ellipse fitting to estimate the lumen centerline. In MRI, we extract the centerline using a minimum cost path approach in which the cost is defined by medialness and an intensity based similarity term. The two centerlines are aligned by an iterative closest point (ICP) algorithm, using rigid and thin-plate spline transformation models. The resulting point correspondences are used as a soft constraint in a subsequent intensity-based registration, optimizing a weighted sum of mutual information between the US and MRI and the Euclidean distance between corresponding points. Rigid and B-spline transformation models were used in this stage. Experiments were performed on datasets from five healthy volunteers. We compared different registration approaches, in order to evaluate the necessity of each step, and to establish the optimum algorithm configuration. For the validation, we used the Dice similarity index to measure the overlap between lumen segmentations in US and MRI.
Proceedings of the 5th international conference on Biomedical Image Registration; 07/2012
[Show abstract][Hide abstract] ABSTRACT: 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 (0.33mm) is similar to the inter-observer variation (0.32mm). The lumen segmentation accuracy (average DSC=0.89, average mean absolute surface distance=0.31mm) is close to the inter-observer variation (average dice=0.92, average mean surface distance=0.23mm). 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.
Medical image analysis 06/2012; 16(6):1202-15. · 3.09 Impact Factor