Vessel enhancement filtering in three-dimensional MR angiography

Inspection of Transparent Objects with Varying Light Scattering Using a Frangi Filter

This paper proposes a new machine vision method to test the quality of a semi-transparent automotive illuminant component. Difference images of Frangi filtered surface images are used to enhance defect-like image structures. In order to distinguish allowed structures from defective structures, morphological features are extracted and used for a nearest-neighbor-based anomaly score. In this way, it could be demonstrated that a segmentation of occurring defects is possible on transparent illuminant parts. The method turned out to be fast and accurate and is therefore also suited for in-production testing.

Quality improvement of OCT angiograms with elliptical directional filtering

We present a method of OCT angiography (OCTA) data filtering for noise suppression and improved visualization of the retinal vascular networks in en face projection images. In our approach, we use a set of filters applied in three orthogonal axes in the three-dimensional (3-D) data sets. Minimization of artifacts generated in B-scan-wise data processing is accomplished by filtering the cross-sections along the slow scanning axis. A-scans are de-noised by axial filtering. The core of the method is the application of directional filtering to the C-scans, i.e. one-pixel thick sections of the 3-D data set, perpendicular to the direction of the scanning OCT beam. The method uses a concept of structuring, directional kernels of shapes matching the geometry of the image features. We use rotating ellipses to find the most likely local orientation of the vessels and use the best matching ellipses for median filtering of the C-scans. We demonstrate our approach in the imaging of a normal human eye with laboratory-grade spectral-domain OCT setup. The "field performance" is demonstrated in imaging of diabetic retinopathy cases with a commercial OCT device. The absolute complex differences method is used for the generation of OCTA images from the data collected in the most noise-wise unfavorable OCTA scanning regime-two frame scanning.

Curvilinear Structure Analysis by Ranking the Orientation Responses of Path Operators

The analysis of thin curvilinear objects in 3D images is a complex and challenging task. In this article, we introduce a new, non-linear operator, called RORPO (Ranking the Orientation Responses of Path Operators). Inspired by the multidirectional paradigm currently used in linear filtering for thin structure analysis, RORPO is built upon the notion of path operator from mathematical morphology. This operator, unlike most operators commonly used for 3D curvilinear structure analysis, is discrete, non-linear and non-local. From this new operator, two main curvilinear structure characteristics can be estimated: an intensity feature, that can be assimilated to a quantitative measure of curvilinearity; and a directional feature, providing a quantitative measure of the structure's orientation. We provide a full description of the structural and algorithmic details for computing these two features from RORPO, and we discuss computational issues. We experimentally assess RORPO by comparison with three of the most popular curvilinear structure analysis filters, namely Frangi Vesselness, Optimally Oriented Flux, and Hybrid Diffusion with Continuous Switch. In particular, we show that our method provides up to 8 percent more true positive and 50 percent less false positives than the next best method, on synthetic and real 3D images.

MR venography of the brain with enhanced vessel contrast using image-domain high-pass filtering of the susceptibility phase shift

PURPOSE

To develop a postprocessing algorithm that enhances the visibility of intracranial venous vasculature and reduces the artifacts in the display of susceptibility-weighted images (SWI).

MATERIALS AND METHODS

Image-domain high-pass filters based on second-order phase difference were applied to the complex 3D SWI data to enhance the susceptibility phase shift of the veins and suppress background signal in SWI. A multivariant statistical parameter was used to suppress the noise in air.

RESULTS

Magnetic resonance (MR) venography with enhanced susceptibility phase shift and reduced off-resonance artifacts was obtained using the proposed filters. The background signal in the 3D MR venography data was well suppressed. Venous vasculature in the peripheral regions of the brain was well depicted and the adverse effect of noise in air in the maximum-intensity projection display of the 3D SWI data was well suppressed.

CONCLUSION

Image-domain high-pass filtering with second-order phase difference provides an alternative display of 3D SWI data with enhanced visibility of the venous vasculature and effective suppression of artifacts.

Segmentation of arteries in MPRAGE images of the ventral medial prefrontal cortex

A method for removing arteries that appear bright with intensities similar to white matter in Magnetized Prepared Rapid Gradient Echo images of the ventral medial prefrontal cortex is described. The Fast Marching method is used to generate a curve within the artery. Then, the largest connected component is selected to segment the artery which is used to mask the image. The surface reconstructed from the masked image yielded cortical thickness maps similar to those generated by manually pruning the arteries from surfaces reconstructed from the original image. The method may be useful in masking vasculature in other cortical regions.

Intracranial vessel segmentation from time-of-flight MRA using pre-processing of the MIP Z-buffer: accuracy of the ZBS algorithm

We evaluate the accuracy of a vascular segmentation algorithm which uses continuity in the maximum intensity projection (MIP) depth Z-buffer as a pre-processing step to generate a list of 3D seed points for further segmentation. We refer to the algorithm as Z-buffer segmentation (ZBS). The pre-processing of the MIP Z-buffer is based on smoothness measured using the minimum chi-square value of a least square fit. Points in the Z-buffer with chi-square values below a selected threshold are used as seed points for 3D region growing. The ZBS algorithm couples spatial continuity information with intensity information to create a simple yet accurate segmentation algorithm. We examine the dependence of the segmentation on various parameters of the algorithm. Performance is assessed in terms of the inclusion/exclusion of vessel/background voxels in the segmentation of intracranial time-of-flight MRA images. The evaluation is based on 490,256 voxels from 14 patients which were classified by an observer. ZBS performance was compared to simple thresholding and to segmentation based on vessel enhancement filtering. The ZBS segmentation was only weakly dependent on the parameters of the initial MIP image generation, indicating the robustness of this approach. Region growing based on Z-buffer generated seeds was advantageous compared to simple thresholding. The ZBS algorithm provided segmentation accuracies similar to that obtained with the vessel enhancement filter. The ZBS performance was notably better than the filter based segmentation for aneurysms where the assumptions of the filter were violated. As currently implemented the algorithm slightly under-segments the intracranial vasculature.

Diagnostic fidelity of the Z-buffer segmentation algorithm: preliminary assessment based on intracranial aneurysm detection

We have developed an algorithm known as the Z-buffer segmentation (ZBS) algorithm for segmenting vascular structures from 3D MRA images. Previously we evaluated the accuracy of the ZBS algorithm on a voxel level in terms of inclusion and exclusion of vascular and background voxels. In this paper we evaluate the diagnostic fidelity of the ZBS algorithm. By diagnostic fidelity we mean that the data preserves the structural information necessary for diagnostic evaluation. This evaluation is necessary to establish the potential usefulness of the segmentation for improved image display, or whether the segmented data could form the basis of a computerized analysis tool. We assessed diagnostic fidelity by measuring how well human observers could detect aneurysms in the segmented data sets. ZBS segmentation of 30 MRA cases containing 29 aneurysms was performed. Image display used densitometric reprojections with shaded surface highlighting that were generated from the segmented data. Three neuroradiologists independently reviewed the generated ZBS images for aneurysms. The observers had 80% sensitivity (90% for aneurysms larger than 2mm) with 0.13 false positives per image. Good agreement with the gold standard for describing aneurysm size and orientation was shown. These preliminary results suggest that the segmentation has diagnostic fidelity with the original data and may be useful for improved visualization or automated analysis of the vasculature.

Automated segmentation and analysis of vascular structures in magnetic resonance angiographic images

The accurate assessment of the presence and extent of vascular disease, and planning of vascular interventions based on MRA requires the determination of vessel dimensions. The current standard is based on measuring vessel diameters on maximum intensity projections (MIPs) using calipers. In order to increase the accuracy and reproducibility of the method, automated analysis of the 3D MR data is required. A novel method for automatically determining the trajectory of the vessel of interest, the luminal boundaries, and subsequent the vessel dimensions is presented. The automated segmentation in 3D uses deformable models, combined with knowledge of the acquisition protocol. The trajectory determination was tested on 20 in vivo studies of the abdomen and legs. In 93% the detected trajectory followed the vessel. The luminal boundary detection was validated on contrast-enhanced (CE) MRA images of five stenotic phantoms. The results from the automated analysis correlated very well with the true diameters of the phantoms used in the in vitro study (r = 0.999, P < 0.001). MRA and x-ray angiography (XA) of the phantoms also correlated well (r = 0.895, P < 0.001). The average unsigned difference between the MRA and XA measurements was 0.08 +/- 0.05 mm. In conclusion, the automated approach allows the accurate assessment of vessel dimensions in MRA images.

Diffusion-enhanced visualization and quantification of vascular anomalies in three-dimensional rotational angiography: Results of an in-vitro evaluation

Three-dimensional rotational angiography (3DRA) is a new and promising technique for obtaining high-resolution isotropic 3D images of vascular structures. However, due to the relatively high noise level and the presence of other background structures in clinical 3DRA images, noise reduction is inevitable. In this paper, we evaluate a number of linear and nonlinear diffusion techniques for this purpose. Specifically, we analyze the effects of these techniques on the threshold-based visualization and quantification of vascular anomalies in 3DRA images. The results of in-vitro experiments indicate that edge-enhancing anisotropic diffusion filtering is most suitable: the increase in the user-dependency of visualizations and quantifications is considerably less with this technique compared to linear filtering techniques, and it is better at reducing noise near edges than isotropic nonlinear diffusion. However, in view of the memory and computation-time requirements of this technique, the latter scheme may be considered a useful alternative.

Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction

The extraction of the centerlines of tubular objects in two and three-dimensional images is a part of many clinical image analysis tasks. One common approach to tubular object centerline extraction is based on intensity ridge traversal. In this paper, we evaluate the effects of initialization, noise, and singularities on intensity ridge traversal and present multiscale heuristics and optimal-scale measures that minimize these effects. Monte Carlo experiments using simulated and clinical data are used to quantify how these "dynamic-scale" enhancements address clinical needs regarding speed, accuracy, and automation. In particular, we show that dynamic-scale ridge traversal is insensitive to its initial parameter settings, operates with little additional computational overhead, tracks centerlines with subvoxel accuracy, passes branch points, and handles significant image noise. We also illustrate the capabilities of the method for medical applications involving a variety of tubular structures in clinical data from different organs, patients, and imaging modalities.

Three-dimensional adaptive filtering in magnetic resonance angiography

In order to enhance 3D image data from magnetic resonance angiography (MRA), a novel method based on the theory of multidimensional adaptive filtering has been developed. The purpose of the technique is to suppress image noise while enhancing important structures. The method is based on local structure estimation using six 3D orientation selective filters, followed by an adaptive filtering step controlled by the local structure information. The complete filtering procedure requires approximately 3 minutes of computational time on a standard workstation for a 256 x 256 x 64 data set. The method has been evaluated using a mathematical vessel model and in vivo MRA data (both phase contrast and time of flight (TOF)). 3D adaptive filtering results in a better delineation of small blood vessels and efficiently reduces the high-frequency noise. Depending on the data acquisition and the original data type, contrast-to-noise ratio (CNR) improvements of up to 179% (8.9 dB) were observed. 3D adaptive filtering may provide an alternative to prolonging the scan time or using contrast agents in MRA when the CNR is low.

Hepatic MR angiography: a multiobserver comparison of visualization methods

OBJECTIVE

MR angiography (MRA) is an established diagnostic method; however, controversy remains over the best technique for display. In this study, we compared five methods of depicting hepatic MRA, including a novel skeletonization approach, using receiver operator characteristic (ROC) curves, interobserver variability (kappa values), and speed of interpretation.

SUBJECTS AND METHODS

Twenty-one patients scheduled for isolated liver perfusion therapy for metastatic disease underwent contrast-enhanced three-dimensional MRA to determine vascular anatomy. Vascular anatomy was validated at the time of surgery. We displayed the image data, using five techniques: maximum intensity projection, targeted maximum intensity projection, isointensity surface (isosurface), connected isointensity surface (connected isosurface), and ordered region growing skeleton (skeleton). Four observers, blinded to the surgical results, interpreted each technique in random order without patient identifiers. Areas under the ROC curves, kappa values of interobserver variability, and time to interpret each display were compared.

RESULTS

Skeletonized MRA had the highest area under the ROC curve (A(z), 0.90 +/- 0.04) compared with the other techniques (p < 0.013). Kappa scores of agreement were also highest for skeletonized MRA (0.75 +/- 0.04) and had no overlap at the 95% confidence level compared with other techniques. Compared with source images, all visualization methods were faster to interpret, but the skeleton technique was more quickly (p = 0.04) interpreted than the other techniques.

CONCLUSION

Skeletonized MRA with the skeleton connectivity algorithm is a semi-automated method of displaying complex arterial anatomy. Compared with other techniques, it is more accurate, more consistent among observers, and slightly faster to interpret. Skeletonization should be applicable to CT angiography and MRA.

Enhanced image detail using continuity in the MIP Z-buffer: applications to magnetic resonance angiography

In this paper a new algorithm is presented for the segmentation and display of blood vessels from images obtained with magnetic resonance angiography (MRA) and other three-dimensional (3D) angiographic imaging techniques. The algorithm developed is based on the observation that vessels are strongly evident in the maximum intensity projection (MIP) Z-buffer as regions of high continuity and low local roughness. Roughness is measured here by the minimum chi2 value of a low-order local least-squares fit in the principal directions through each point in the MIP Z-buffer. Although some background pixels in the Z-buffer exhibit low local roughness, the size of the connected region is nearly always much smaller than even the very smallest vessels that appear in the MIP image. It is shown that by applying connectivity to the regions of low roughness, there is nearly complete separation between vascular detail and background. When connectivity is further applied in the original 3D image, vascular bed segmentation becomes nearly complete. The algorithm consists of three basic steps: a) determination of the minimum local roughness at each point in the MIP Z-buffer; b) connection of all neighboring points of low local roughness; and c) connection of all points in the original 3D image matrix that are connected to the points determined in the MIP Z-buffer and that are above an intensity threshold. The algorithm as presented is not optimized but demonstrates a very strong potential for improved portrayal of vascular detail.

Optimized visualization of vessels in contrast enhanced intracranial MR angiography

In this study, the problem of small vessel visualization in magnetic resonance angiography is addressed. The loss of vessel contrast due to slow flow-related signal saturation can be compensated by the T1 reduction obtained from the use of an MR contrast agent, such as Gd-DTPA. The vessel/background signal-difference-to-noise ratio (SDNR) is shown to strongly depend on the imaging parameters, as well as on the time course of the blood T1 values obtained from the contrast injection. Specifically, it was found that vessel SDNR increases almost linearly with TR, if the sampling bandwidth is reduced proportionately.

Contrast-enhanced magnetic resonance angiography of cerebral arteries. A review

The loss of blood vessel visibility due to the signal saturation of slow flow can be partially overcome by the T1 reduction that occurs with the use of contrast agents such as Gd-DTPA during magnetic resonance angiography (MRA) studies. Dynamic-imaging techniques that have been applied successfully in abdominal imaging may also be useful for intracranial applications. However, the time between arterial and venous enhancement is very short during intracranial circulation. This limits the spatial resolution that can be obtained between arterial and venous enhancement. Fortunately, the blood-brain barrier and the relatively long duration of significant decrease in blood T1 has led to the development of very high resolution intracranial MRA techniques. Knowledge of the contrast-agent dilution factors and the ultimate resulting relaxation rates can be used to optimize the imaging parameters to maximize vessel signal relative to the background signal (the signal-difference-to-noise ratio). The additional venous vascular detail in the contrast-enhanced study can be spatially resolved in the 3D image data and determined by incorporating information from both high-resolution precontrast and postcontrast studies. In this article, the history, development and application of contrast agents in MRA are presented.

[Stereotactic localization in medical imaging. Technical and methodologic aspects]

Stereotactic neurosurgery and stereotactic radiation therapy require the three-dimensional localization of lesions for biopsy or for treatment planning. The aim of this paper is the description of methods used in the different imaging modalities: x-ray teleradiography, digital subtracted angiography, computed tomography, and nuclear magnetic resonance imaging. The simple pin-target locating techniques are distinguished from those serving to the definition of volumes target necessary to treatment planning. Performances and difficulties of these techniques are emphasized. The specific methodology developed in Lille is described as an example. Organizational aspects and necessary quality controls for a good progress of the entire procedure, from imaging to treatment, are also discussed.

Multiresolution, model-based segmentation of MR angiograms

During the last decade, the quality of MR angiograms has risen substantially and their clinical utility has been demonstrated progressively. This acceptance has created a need for tools with which to summarize and display the information available. We have used a model-based segmentation technique to extract vascular morphology and local flow parameters from phase contrast MR angiograms. A multiresolution data structure is used as the basis of recursive decision-making to identify regions of blood flow. The resulting data representation allows more efficient data handling in subsequent processing and visualization and is directly applicable to the creation of a connected graph model of vascular regions. We describe this flow feature extraction algorithm and demonstrate the utility of the results.

Vessel enhancement filtering in three-dimensional MR angiograms using long-range signal correlation

Small vessels in three-dimensional MR angiograms have low visibility in maximum-intensity projection images because of their low contrast. In a previous study, we had two nonlinear filters that appeared to give significant improvement in small vessel detail. In this paper, we report on a generalization of this filter that allows a more general modeling of the vessels and a more complete suppression of background. One implementation of the general filter gave a vessel mean contrast-to-noise ratio that is 2.52 and 3.51 times higher than the vessel mean contrast-to-noise ratio obtained using our previously reported maximum-minimum (max-min) filter and cross-section filter, respectively.

The effect of image resolution on vessel signal in high-resolution magnetic resonance angiography

In this paper, the theoretical dependence of the signal of flowing fluids is considered in terms of the imaging parameters and the physical condition of the flowing fluid. For the vessel sizes, flow ranges, and imaging conditions studied, it is demonstrated that the fluid signal can be predicted from the solution of Bloch equations. For vessels on the order of the imaging resolution or smaller, the effects of the image resolution must be included