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Cardiac computed tomography (CT) is a valuable tool for functional mechanical heart valve (MHV) assessment. An important aspect of bileaflet MHV assessment is evaluation and measurement of leaflet opening and closing angles. Performed manually, however, it is a laborious and time consuming task. In this paper, we propose an automated approach for bileaflet MHV leaflet angle computation. This method consists of four steps. After a one click selection of the MHV region on an axial image, an automatic MHV extraction using thresholding, and a connected component analysis based on voxel intensities is performed. Then, the MHV component (valve ring and two leaflets) positions are identified using random sample consensus and least square fitting. Finally, the angles are automatically computed based on the orientation of the components in each timeframe. Five multiphase CT scans from patients with a bileaflet MHV containing between 14 and 17 timepoints were used for development and another 15 were used for evaluation. The detected MHV components were scored for their overlap with real components as successful or unsuccessful. For successful results, the angles were compared to those measured by a radiologist. Qualitatively evaluated on a data set of 222 images, a total of 398 out of 444 angle computations (89.6%) were rated as successful. Compared to the angles measured by the radiologist, the successful angles showed a mean difference of 0.54° ± 3.63° from the manual calculations. The method provides a high success rate and an accurate computation of leaflet opening angles compared to manual measurements.

Purpose: Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle., Methods: We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound-guided radiation therapy. Furthermore, we investigated the trade-off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins., Results: Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid-ventilation margins, as it avoided non-linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%., Conclusions: Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety., (© 2018 American Association of Physicists in Medicine.)

Multimodal image fusion for image guidance in minimally invasive liver interventions generally requires the registration of pre-operatively acquired images with interventional images of the patient. Whereas rigid registration approaches are fast and can be used in an interventional setting, the actual liver deformation may be nonrigid. The purpose of this paper is to assess the magnitude of nonrigid deformation of the liver between pre-operative and interventional CT images in the case of tumor ablations, over the full liver and over parts of the liver that match the volumes typically imaged by a 3D ultrasound transducer. We acquired 3D abdominal CT scans of 38 patients that had undergone the radiofrequency ablation of liver tumors, pre-operative CT images as well as intraoperative CT images. To determine the magnitude of liver deformation due to pose changes and respiration, we nonrigidly registered the pre-operative CT scan with the intraoperative CT scan. By fitting the deformation to a rigid transformation in the region of interest and computing the residual displacements, the nonrigid deformation part can be quantified. We performed quantifications over the complete liver, as well as for two volumes of interest representative of sub-xiphoidal and intercostal 3D ultrasound acquisitions. The results showed that a substantial amount of nonrigid deformation was found, and rotation of the patient's pose and deep inhalation caused significant liver deformation. Hence we concluded that nonrigid motion correction in the interventions should be taken into account.

Percutaneous coronary intervention is a minimally invasive procedure that is usually performed under image guidance using X-ray angiograms in which coronary arteries are opacified with contrast agent. In X-ray images, 3D objects are projected on a 2D plane, generating semi-transparent layers that overlap each other. The overlapping of structures makes robust automatic information processing of the X-ray images, such as vessel extraction which is highly relevant to support smart image guidance, challenging. In this paper, we propose an automatic online layer separation approach that robustly separates interventional X-ray angiograms into three layers: a breathing layer, a quasi-static layer and a vessel layer that contains information of coronary arteries and medical instruments. The method uses morphological closing and an online robust PCA algorithm to separate the three layers. The proposed layer separation method ran fast and was demonstrated to significantly improve the vessel visibility in clinical X-ray images and showed better performance than other related online or prospective approaches. The potential of the proposed approach was demonstrated by enhancing contrast of vessels in X-ray images with low vessel contrast, which would facilitate the use of reduced amount of contrast agent to prevent contrast-induced side effects., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Minimally invasive interventions in the heart such as in electrophysiology are becoming more and more important in clinical practice. Currently, preoperative computed tomography angiography (CTA) is used to provide anatomic information during electrophysiology interventions, but this does not provide real-time feedback and burdens the patient with additional radiation and side effects of the contrast agent. Three-dimensional transesophageal echocardiography (TEE) is an excellent modality for visualization of anatomic structures and instruments in real time, but some cavities, especially the left atrium, suffer from the limited coverage of the 3-D TEE volumes. This leads to difficulty in segmenting the left atrium. We propose replacing or complementing pre-operative CTA imaging with wide-view TEE. We tested this proposal on 20 patients for which TEE image volumes covering the left atrium and CTA images were acquired. The TEE images were manually registered, and wide-view volumes were generated. Five heart cavities in single-view and wide-view TEE were segmented and compared with atlas based-segmentations derived from the CTA images. We found that the segmentation accuracy (Dice coefficients) improved relative to segmentation of single-view images by 5, 15 and 9 percentage points for the left atrium, right atrium and aorta, respectively. Average anatomic coverage was improved by 2, 29, 62 and 49 percentage points for the right ventricle, left atrium, right atrium and aorta, respectively. This finding confirms that wide-view 3-D TEE can be useful in supporting electrophysiology interventions., (Copyright © 2015 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Liver vessel segmentation in CTA images is a challenging task, especially in the case of noisy images. This paper investigates whether pre-filtering improves liver vessel segmentation in 3D CTA images. We introduce a quantitative evaluation of several well-known filters based on a proposed liver vessel segmentation method on CTA images. We compare the effect of different diffusion techniques i.e. Regularized Perona-Malik, Hybrid Diffusion with Continuous Switch and Vessel Enhancing Diffusion as well as the vesselness approaches proposed by Sato, Frangi and Erdt. Liver vessel segmentation of the pre-processed images is performed using a histogram-based region grown with local maxima as seed points. Quantitative measurements (sensitivity, specificity and accuracy) are determined based on manual landmarks inside and outside the vessels, followed by T-tests for statistic comparisons on 51 clinical CTA images. The evaluation demonstrates that all the filters make liver vessel segmentation have a significantly higher accuracy than without using a filter (p  <  0.05); Hybrid Diffusion with Continuous Switch achieves the best performance. Compared to the diffusion filters, vesselness filters have a greater sensitivity but less specificity. In addition, the proposed liver vessel segmentation method with pre-filtering is shown to perform robustly on a clinical dataset having a low contrast-to-noise of up to 3 (dB). The results indicate that the pre-filtering step significantly improves liver vessel segmentation on 3D CTA images.

Purpose: To investigate the feasibility of using deformable registration in clinical practice to fuse MR and CT images of the head and neck for treatment planning., Method and Materials: A state-of-the-art deformable registration algorithm was optimized, evaluated, and compared with rigid registration. The evaluation was based on manually annotated anatomic landmarks and regions of interest in both modalities. We also developed a multiparametric registration approach, which simultaneously aligns T1- and T2-weighted MR sequences to CT. This was evaluated and compared with single-parametric approaches., Results: Our results show that deformable registration yielded a better accuracy than rigid registration, without introducing unrealistic deformations. For deformable registration, an average landmark alignment of approximatively 1.7 mm was obtained. For all the regions of interest excluding the cerebellum and the parotids, deformable registration provided a median modified Hausdorff distance of approximatively 1 mm. Similar accuracies were obtained for the single-parameter and multiparameter approaches., Conclusions: This study demonstrates that deformable registration of head-and-neck CT and MR images is feasible, with overall a significanlty higher accuracy than for rigid registration., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) 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 CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA 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/., (Copyright © 2013 Elsevier B.V. All rights reserved.)

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., (Copyright © 2012 Elsevier B.V. All rights reserved.)

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., (Copyright © 2010 Elsevier B.V. All rights reserved.)

An accurate spatial relationship between 3D in-vivo carotid plaque and lumen imaging and histological cross sections is required to study the relationship between biomechanical parameters and atherosclerotic plaque components. We present and evaluate a fully three-dimensional approach for this registration problem, which accounts for deformations that occur during the processing of the specimens. By using additional imaging steps during tissue processing and semi-automated non-linear registration techniques, a 3D-reconstruction of the histology is obtained. The methodology was evaluated on five specimens obtained from patients, operated for severe atherosclerosis in the carotid bifurcation. In more than 80% of the histology slices, the quality of the semi-automated registration with computed tomography angiography (CTA) was equal to or better than the manual registration. The inter-observer variability was between one and two in-vivo CT voxels and was equal to the manual inter-observer variability. Our technique showed that the angles between the normals of the registered histology slices and the in-vivo CTA scan direction ranged 6-56 degrees , indicating that proper 3D-registration is crucial for establishing a correct spatial relation with in-vivo imaging modalities. This new 3D-reconstruction technique of atherosclerotic plaque tissue opens new avenues in the field of biomechanics as well as in the field of image processing, where it can be used for validation purposes of segmentation algorithms., (Copyright 2010 Elsevier Ltd. All rights reserved.)

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 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.

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.

Generation of a reference standard from multiple manually annotated datasets is a non-trivial problem. This paper discusses the weighted averaging of 3D open curves, which we used to generate a reference standard for vessel tracking data. We show how weighted averaging can be implemented by applying the Mean Shift algorithm to paths, and discuss the details of our implementation. Our approach can handle cases where the observer centerlines take different branches in a natural way. The method has been evaluated on synthetic data, and has been used to generate reference centerlines for evaluation of vessel tracking algorithms.

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.

Minimally invasive interventions are often performed under fluoroscopic guidance. Drawbacks of fluoroscopic guidance are the fact that the presented images are 2D projections and that both the patient and the clinician are exposed to radiation. Image-guided navigation using pre-interventionally acquired 3D MR or CT data is an alternative. However, this often requires invasive anatomical landmark-based, marker-based or surface-based image-to-patient registration. In this paper, a coupling between an image-guided navigation system and an intraoperative C-arm X-ray device with 3D imaging capabilities (3D rotational X-ray (3DRX) system) that enables direct navigation without invasive image-to-patient registration on 3DRX volumes, is described and evaluated. The coupling is established in a one-time preoperative calibration procedure. The individual steps in the registration procedure are explained and evaluated. The acquired navigation accuracy using this coupling is approximately one millimeter.

Recently, three-dimensional (3D) rotational x-ray imaging has been combined with navigation technology, enabling direct 3D navigation for minimally invasive image guided interventions. In this study, phantom experiments are used to determine the accuracy of such a navigation set-up for a mobile C-arm with propeller motion. After calibration of the C-arm system, the accuracy is evaluated by pinpointing divots on a special-purpose phantom with known geometry. This evaluation is performed both with and without C-arm motion in between calibration and registration for navigation. The variation caused by each of the individual transformations in the calibration and registration process is also studied. The feasibility of direct navigation on 3D rotational x-ray images for functional endoscopic sinus surgery has been evaluated in a cadaver navigation experiment. Navigation accuracy was approximately 1.0 mm, which is sufficient for functional endoscopic sinus surgery. C-arm motion in between calibration and registration slightly degraded the registration accuracy by approximately 0.3 mm. Standard deviations of each of the transformations were in the range 0.15-0.31 mm. In the cadaver experiment, the navigation images were considered in good correspondence with the endoscopic images by an experienced ENT surgeon. Availability of 3D localization information provided by the navigation system was considered valuable by the ENT surgeon.

In the past few years, a number of two-dimensional (2-D) to three-dimensional (3-D) (2-D-3-D) registration algorithms have been introduced. However, these methods have been developed and evaluated for specific applications, and have not been directly compared. Understanding and evaluating their performance is therefore an open and important issue. To address this challenge we introduce a standardized evaluation methodology, which can be used for all types of 2-D-3-D registration methods and for different applications and anatomies. Our evaluation methodology uses the calibrated geometry of a 3-D rotational X-ray (3DRX) imaging system (Philips Medical Systems, Best, The Netherlands) in combination with image-based 3-D-3-D registration for attaining a highly accurate gold standard for 2-D X-ray to 3-D MR/CT/3DRX registration. Furthermore, we propose standardized starting positions and failure criteria to allow future researchers to directly compare their methods. As an illustration, the proposed methodology has been used to evaluate the performance of two 2-D-3-D registration techniques, viz. a gradient-based and an intensity-based method, for images of the spine. The data and gold standard transformations are available on the internet (http://www.isi.uu.nl/Research/Databases/).

A new method for MR to X-ray registration is presented. Based on training data, consisting of registered multispectral MR and CT data, a function is defined that maps multispectral MR data to CT-like data. For new subjects for which multispectral MR data have been acquired, the mapping function is used to generate a corresponding CT-like dataset. The CT-like image is subsequently used for registration to X-ray data, using gradient-based registration. Preliminary experiments indicate that MR to X-ray registration using this method is more accurate and has a larger capture range than gradient-based registration applied directly to MR data.

Using three-dimensional rotational X-ray angiography (3DRA), three-dimensional (3-D) information of the vasculature can be obtained prior to endovascular interventions. However, during interventions, the radiologist has to rely on fluoroscopy images to manipulate the guide wire. In order to take full advantage of the 3-D information from 3DRA data during endovascular interventions, a method is presented that yields an integrated display of the position of the guide wire and vasculature in 3-D. The method relies on an automated method that tracks the guide wire simultaneously in biplane fluoroscopy images. Based on the calibrated geometry of the C-arm, the 3-D guide-wire position is determined and visualized in the 3-D coordinate system of the vasculature. The method is evaluated in an intracranial anthropomorphic vascular phantom. The influence of the angle between projections, distortion correction of the projection images, and accuracy of geometry knowledge on the accuracy of 3-D guide-wire reconstruction from biplane images is determined. If the calibrated geometry information is used and the images are corrected for distortion, a mean distance to the reference standard of 0.42 mm and a tip distance of 0.65 mm is found, which means that accurate guide-wire reconstruction from biplane images can be performed.

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. [ABSTRACT FROM AUTHOR]