42 research outputs found
CoA_MRIData
No full textThis datasets contains cardiac MRI images for 20 patients post-coarctation repair. For each patient 2 images of cardiac geometry, and 2-3 phase-contrast flow images are available. This data is suitable for pulse wave velocity measurements.
All data is acquired under ethics 09-H0802-78 as approved by the London - Westminster research ethics committee, and has been fully anonymised.
Related publication - Van Engelen, Arna, Silva Vieira, Miguel, Rafiq, Isma, Cecelja, Marina, Schneider, Torben, de Bliek, Hubrecht, Figueroa, C. Alberto, Hussain, Tarique, Botnar, Rene M. Alastruey, Jordi, (2017), “Aortic length measurements for pulse wave velocity calculation: manual 2D vs automated 3D centreline extraction", Journal of Cardiovascular Magnetic Resonance, 2017, v 19, n 1, p32. http://dx.doi.org/10.1186/s12968-017-0341-
CoA_MRIData
No full textThis datasets contains cardiac MRI images for 20 patients post-coarctation repair. For each patient 2 images of cardiac geometry, and 2-3 phase-contrast flow images are available. This data is suitable for pulse wave velocity measurements. All data is acquired under ethics 09-H0802-78 as approved by the London - Westminster research ethics committee, and has been fully anonymised.Related publication - Van Engelen, Arna, Silva Vieira, Miguel, Rafiq, Isma, Cecelja, Marina, Schneider, Torben, de Bliek, Hubrecht, Figueroa, C. Alberto, Hussain, Tarique, Botnar, Rene M. Alastruey, Jordi, (2017), “Aortic length measurements for pulse wave velocity calculation: manual 2D vs automated 3D centreline extraction", Journal of Cardiovascular Magnetic Resonance, 2017, v 19, n 1, p32. http://dx.doi.org/10.1186/s12968-017-0341-
Multimodal Image Analysis for Carotid Artery Plaque Characterization
Full text linkAtherosclerosis of the carotid artery is a main cause of ischemic cerebrovascular events. There is evidence that the composition of the vessel wall is more strongly related to plaque vulnerability and subsequent events than luminal stenosis, which is currently used for risk stratification in clinical practice. Noninvasive imaging can characterize the composition of the vessel wall. In order to incorporate measures of plaque composition into clinical practice, accurate and robust image segmentation methods are required.
This thesis describes the development and validation of image analysis techniques that aim at the automated characterization of the carotid atherosclerotic vessel wall. The first part of this thesis makes use of a dataset in which ex vivo and in vivo MRI and CT, and annotated histology sections are available and have been spatially aligned. We firstly perform segmentation of plaque components in ex vivo MRI. Voxel classifiers are trained on a ground truth of registered histology and μCT images. We show the importance of different groups of features: intensities, Gaussian filters and wall distances, and use these features in subsequent work on in vivo data. Here we address the problems that arise in training and evaluation of segmentation methods when misregistration between histology and in vivo data occurs. Still, we show that accurate segmentation of the lipid-rich necrotic core, calcification and fibrous tissue is possible when MRI and CTA are combined, and linear discriminant analysis is performed after rejecting outliers from the training set. Finally, in this first part of the thesis we develop a method for automatic segmentation of different plaque components from histology sections, to make the use of histology for training and evaluation more feasible and less time-consuming.
Subsequently we perform plaque component segmentation from in vivo MRI only, and address the fact that MRI datasets acquired in difference centers using different hardware varies considerably in appearance. Firstly, we show that segmentation of lipid, intraplaque hemorrhage, calcification, and fibrous tissue can be performed with similar accuracy as the variation between observers on MRI data from two different centers. Secondly, we show that the accuracy decreases when a method developed on data from one center is used to apply to data from the other center. We propose two methods by which we improve this transferability of segmentation methods: non-linear feature scaling, and transfer learning in which we add only a few annotated slices from the ‘new’ center to the training data.
Lastly, we perform a study on texture analysis of carotid artery plaques in 3D ultrasound images. From a large set of texture parameters we obtain the strongest parameters to form a ‘risk indicator’. In a longitudinal study with 3D ultrasound imaging at two time points, we show that change in texture is a stronger predictor of vascular events than previously used parameters for risk stratification, and that using texture in addition to those parameters improves risk stratification in patients with carotid artery disease
Plaque characterization in ex vivo MRI evaluated by dense 3D correspondence with histology
Full text linkAutomatic quantification of carotid artery plaque composition is important in the development of methods that distinguish vulnerable from stable plaques. MRI has shown to be capable of imaging different components noninvasively. We present a new plaque classification method which uses 3D registration of histology data with ex vivo MRI data, using non-rigid registration, both for training and evaluation. This is more objective than previously presented methods, as it eliminates selection bias that is introduced when 2D MRI slices are manually matched to histological slices before evaluation. Histological slices of human atherosclerotic plaques were manually segmented into necrotic core, fibrous tissue and calcification. Classification of these three components was voxelwise evaluated. As features the intensity, gradient magnitude and Laplacian in four MRI sequences after different degrees of Gaussian smoothing, and the distances to the lumen and the outer vessel wall, were used. Performance of linear and quadratic discriminant classifiers for different combinations of features was evaluated. Best accuracy (72.5 ± 7.7%) was reached with the linear classifier when all features were used. Although this was only a minor improvement to the accuracy of a classifier that only included the intensities and distance features (71.6 ± 7.9%), the difference was statistically significant (paired t-test, p<0.05). Good sensitivity and specificity for calcification was reached (83% and 95% respectively), however, differentiation between fibrous (sensitivity 85%, specificity 60%) and necrotic tissue (sensitivity 49%, specificity 89%) was more difficult.Imaging Science and TechnologyApplied Science
Supervised in-vivo plaque characterization incorporating class label uncertainty
No full textWe segment atherosclerotic plaque components in in-vivo MRI and CT data using supervised voxelwise classification. The most reliable ground truth can be obtained from histology sections, however, it is not straightforward to use this for classifier training as the registration with in-vivo data often shows misalignments. Therefore, for training we incorporate uncertainty in the ground truth via "soft" labels that indicate a probability for each class. Soft labels are created by Gaussian blurring of the original hard segmentations, and weighted by the registration accuracy. Classification is evaluated on the relative volumes for fibrous, lipid-rich necrotic and calcified tissue. Using conventional "hard" labels, the differences between the ground truth and classification result per subject are 0.4±3.6% for calcification, 7.6±14.9% for fibrous and 7.2±14.5% for necrotic tissue. Using the new approach accuracy is improved: for calcification 0.6±1.6%, fibrous 3.6±16.8% and necrotic tissue 2.9±16.1%.</p
Maximization of Regional probabilities using Optimal Surface Graphs: Application to Carotid Artery Segmentation in MRI
Full text link__Purpose__ We present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images.
__Methods__ First, voxel-wise regional probability maps were obtained using a Support Vector Machine classifier trained on local image features. Then the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints.
__Results__ The method was evaluated on 49 carotid arteries from 30 subjects. The proposed method shows good accuracy with a Dice wall overlap of 74:1%+-4:3%, and significantly outperforms a published method based on an OSG using only surface information, the obtained segmentations using voxel-wise classification alone, and another published artery wall segmentation method based on a deformable surface model. Intra-class correlations (ICC) with manually measured lumen and wall volumes were similar to those obtained between observers. Finally, we show a good reproducibility of the method with ICC = 0:86 between the volumes measured in scans repeated within a short time interval.
__Conclusions__ In this work a new segmentation method that uses both an OSG and regional probabilities is presented. The method shows good segmentations of the carotid artery in MRI and outperformed another segmentation method that uses OSG and edge information and the voxel-wise segmentation using the probability maps
An Integrated Software Application for Non-invasive Assessment of Local Aortic Haemodynamic Parameters
Full text linkAbstractNon-invasive assessment of haemodynamic data, such as pressure and flow profiles, is helpful in detecting cardiac disease at an early stage. However, current methods lack spatial accuracy and do not take local variations into account. This paper presents a software tool that extracts the arterial geometry and blood inflow profiles from MR images, which are subsequently used to run a 1D haemodynamic simulation model, and displays its output. The workflow is highly automated but allows user-interaction to correct inaccuracies. The tool was evaluated for inter-observer agreement on one healthy volunteer, and results are shown for one patient with an aortic coarctation. The resulting haemodynamic parameters show high agreement between different users and reveal local changes within a coarctation patient
Estimating central blood pressure from aortic flow: development and assessment of algorithms.
Full text linkCentral blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm's performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data.NEW & NOTEWORTHY First, our proposed methods for CV parameter estimation and a comprehensive set of methods from the literature were tested using in silico and clinical datasets. Second, optimized algorithms for estimating cBP from aortic flow were developed and tested for a wide range of cBP morphologies, including catheter cBP data. Third, a dataset of simulated cBP waves was created using a three-element Windkessel model. Fourth, the Windkessel model dataset and optimized algorithms are freely available
Estimating central blood pressure from aortic flow: development and assessment of algorithms
Full text linkCentral blood pressure (cBP) is a highly prognostic cardiovascular (CV) risk factor whose accurate, invasive assessment is costly and carries risks to patients. We developed and assessed novel algorithms for estimating cBP from noninvasive aortic hemodynamic data and a peripheral blood pressure measurement. These algorithms were created using three blood flow models: the two- and three-element Windkessel (0-D) models and a one-dimensional (1-D) model of the thoracic aorta. We tested new and existing methods for estimating CV parameters (left ventricular ejection time, outflow BP, arterial resistance and compliance, pulse wave velocity, and characteristic impedance) required for the cBP algorithms, using virtual (simulated) subjects (n = 19,646) for which reference CV parameters were known exactly. We then tested the cBP algorithms using virtual subjects (n = 4,064), for which reference cBP were available free of measurement error, and clinical datasets containing invasive (n = 10) and noninvasive (n = 171) reference cBP waves across a wide range of CV conditions. The 1-D algorithm outperformed the 0-D algorithms when the aortic vascular geometry was available, achieving central systolic blood pressure (cSBP) errors ≤ 2.1 ± 9.7 mmHg and root-mean-square errors (RMSEs) ≤ 6.4 ± 2.8 mmHg against invasive reference cBP waves (n = 10). When the aortic geometry was unavailable, the three-element 0-D algorithm achieved cSBP errors ≤ 6.0 ± 4.7 mmHg and RMSEs ≤ 5.9 ± 2.4 mmHg against noninvasive reference cBP waves (n = 171), outperforming the two-element 0-D algorithm. All CV parameters were estimated with mean percentage errors ≤ 8.2%, except for the aortic characteristic impedance (≤13.4%), which affected the three-element 0-D algorithm’s performance. The freely available algorithms developed in this work enable fast and accurate calculation of the cBP wave and CV parameters in datasets containing noninvasive ultrasound or magnetic resonance imaging data
