Principal curves for lumen center extraction and flow channel width estimation in 3-D arterial networks: theory, algorithm, and validation.

We present an energy-minimization-based framework for locating the centerline and estimating the width of tubelike objects from their structural network with a nonparametric model. The nonparametric representation promotes simple modeling of nested branches and n -way furcations, i.e., structures that abound in an arterial network, e.g., a cerebrovascular circulation. Our method is capable of extracting the entire vascular tree from an angiogram in a single execution with a proper initialization. A succinct initial model from the user with arterial network inlets, outlets, and branching points is sufficient for complex vasculature. The novel method is based upon the theory of principal curves. In this paper, theoretical extension to grayscale angiography is discussed, and an algorithm to find an arterial network as principal curves is also described. Quantitative validation on a number of simulated data sets, synthetic volumes of 19 BrainWeb vascular models, and 32 Rotterdam Coronary Artery volumes was conducted. We compared the algorithm to a state-of-the-art method and further tested it on two clinical data sets. Our algorithmic outputs-lumen centers and flow channel widths-are important to various medical and clinical applications, e.g., vasculature segmentation, registration and visualization, virtual angioscopy, and vascular atlas formation and population study.

K-space reconstruction with anisotropic kernel support (KARAOKE) for ultrafast partially parallel imaging.

PURPOSE: Partially parallel imaging (PPI) greatly accelerates MR imaging by using surface coil arrays and under-sampling k-space. However, the reduction factor (R) in PPI is theoretically constrained by the number of coils (N(C)). A symmetrically shaped kernel is typically used, but this often prevents even the theoretically possible R from being achieved. Here, the authors propose a kernel design method to accelerate PPI faster than R = N(C). METHODS: K-space data demonstrates an anisotropic pattern that is correlated with the object itself and to the asymmetry of the coil sensitivity profile, which is caused by coil placement and B(1) inhomogeneity. From spatial analysis theory, reconstruction of such pattern is best achieved by a signal-dependent anisotropic shape kernel. As a result, the authors propose the use of asymmetric kernels to improve k-space reconstruction. The authors fit a bivariate Gaussian function to the local signal magnitude of each coil, then threshold this function to extract the kernel elements. A perceptual difference model (Case-PDM) was employed to quantitatively evaluate image quality. RESULTS: A MR phantom experiment showed that k-space anisotropy increased as a function of magnetic field strength. The authors tested a K-spAce Reconstruction with AnisOtropic KErnel support ("KARAOKE") algorithm with both MR phantom and in vivo data sets, and compared the reconstructions to those produced by GRAPPA, a popular PPI reconstruction method. By exploiting k-space anisotropy, KARAOKE was able to better preserve edges, which is particularly useful for cardiac imaging and motion correction, while GRAPPA failed at a high R near or exceeding N(C). KARAOKE performed comparably to GRAPPA at low Rs. CONCLUSIONS: As a rule of thumb, KARAOKE reconstruction should always be used for higher quality k-space reconstruction, particularly when PPI data is acquired at high Rs and/or high field strength.

Modeling non-stationarity of kernel weights for k-space reconstruction in partially parallel imaging.

PURPOSE: In partially parallel imaging, most k-space-based reconstruction algorithms such as GRAPPA adopt a single finite-size kernel to approximate the true relationship between sampled and nonsampled signals. However, the estimation of this kernel based on k-space signals is imperfect, and the authors are investigating methods dealing with local variation of k-space signals. METHODS: To model nonstationarity of kernel weights, similar to performing a spatially adaptive regularization, the authors fit a set of linear functions using concepts from geographically weighted regression, a methodology used in geophysical analysis. Instead of a reconstruction with a single set of kernel weights, the authors use multiple sets. A missing signal is reconstructed with its kernel weights set determined by k-space clustering. Simulated and acquired MR data with several different image content and acquisition schemes, including MR tagging, were tested. A perceptual difference model (Case-PDM) was used to quantitatively evaluate the quality of over 1000 test images, and to optimize the parameters of our algorithm. RESULTS: A MOdeling Non-stationarity of KErnel wEightS ("MONKEES") reconstruction with two sets of kernel weights gave reconstructions with significantly better image quality than the original GRAPPA in all test images. Using more sets produced improved image quality but with diminishing returns. As a rule of thumb, at least two sets of kernel weights, one from low- and the other from high frequency k-space, should be used. CONCLUSIONS: The authors conclude that the MONKEES can significantly and robustly improve the image quality in parallel MR imaging, particularly, cardiac imaging.

Reproducible MRI measurement of adipose tissue volumes in genetic and dietary rodent obesity models.

PURPOSE: To develop ratio MRI [lipid/(lipid+water)] methods for assessing lipid depots and compare measurement variability with biological differences among lean controls (spontaneously hypertensive rats [SHRs]), dietary obese rats (SHR-DOs), and genetic/dietary obese rats (SHROBs). MATERIALS AND METHODS: Images with and without chemical shift-selective (CHESS) water suppression were processed using a semiautomatic method that accounts for relaxometry, chemical shift, receive coil sensitivity, and partial volume. RESULTS: Partial volume correction improved results by 10% to 15%. Over six operators, volume variation was reduced to 1.9 mL from 30.6 mL for single-image-analysis with intensity inhomogeneity. For three acquisitions on the same animal, volume reproducibility was <1%. SHROBs had six times more visceral and eight times more subcutaneous adipose tissue than SHRs. SHR-DOs had enlarged visceral depots (three times larger than those in SHRs). SHROBs had significantly more subcutaneous adipose tissue, indicating a strong genetic component to this fat depot. Liver ratios in SHR-DO and SHROB were higher than in SHR, indicating elevated fat content. Among SHROBs, evidence suggested a phenotype SHROB* having elevated liver ratios and visceral adipose tissue volumes. CONCLUSION: Effects of diet and genetics on obesity were significantly larger than variations due to image acquisition and analysis, indicating that these methods can be used to assess accumulation/depletion of lipid depots in animal models of obesity.

A loose-packing approach for coil embolization of giant intracranial aneurysm.

Endoluminal occlusion of giant intracranial aneurysms with coil embolization is a viable endovascular treatment option alternative to surgical clipping. However, due to the relatively large aneurysm size, the use of embolization coils for giant aneurysms could be great. A loose-packing embolization strategy in which the fundus of the aneurysm is loosely packed while the aneurysm base is tightly packed is presented. Such a coiling strategy is best suited to giant aneurysms of elongated configuration and narrow neck as illustrated in the present case. While the use of the loose-packing approach is recommended for elongated aneurysms with a narrow neck, its use is not to be generalized for aneurysms of other configurations.

Probabilistic vessel axis tracing and its application to vessel segmentation with stream surfaces and minimum cost paths.

We propose a novel framework to segment vessels on their cross-sections. It starts with a probabilistic vessel axis tracing in a gray-scale three-dimensional angiogram, followed by vessel boundary delineation on cross-sections derived from the extracted axis. It promotes a more intuitive delineation of vessel boundaries which are mostly round on the cross-sections. The prior probability density function of the axis tracer's formulation permits seamless integration of user guidance to produce continuous traces through regions that contain furcations, diseased portions, kissing vessels (vessels in close proximity to each other) and thin vessels. The contour that outlines the vessel boundary in a 3-D space is determined as the minimum cost path on a weighted directed acyclic graph derived from each cross-section. The user can place anchor points to force the contour to pass through. The contours obtained are tiled to approximate the vessel boundary surface. Since we use stream surfaces generated w.r.t. the traced axis as cross-sections, non-intersecting adjacent cross-sections are guaranteed. Therefore, the tiling can be achieved by joining vertices of adjacent contours. The vessel boundary surface is then deformed under constrained movements on the cross-sections and is voxelized to produce the final vascular segmentation. Experimental results on synthetic and clinical data have shown that the vessel axes extracted by our tracer are continuous and less jittered as compared with the other two trace-based algorithms. Furthermore, the segmentation algorithm with cross-sections are robust to noise and can delineate vessel boundaries that have level of variability similar to those obtained manually.

Does endoluminal coil embolization cause distension of intracranial aneurysms?

INTRODUCTION: The aim of the present study was to determine whether intracranial aneurysms are distended after coil embolization and to evaluate the distensibility of ruptured aneurysms treated with endovascular coiling. METHODS: This was a prospective study of 20 consecutive patients with 22 aneurysms, who presented with a ruptured cerebral aneurysm and were treated with endovascular coiling of the aneurysm in a single institution. A diagnostic digital subtraction angiography (DSA) and a three-dimensional radiographic angiography (3DRA) were performed with bi-plane angiography equipment (Philips V5000) immediately before and after the embolization procedure to detect volume enlargement of the aneurysm after embolization, and the extent of the enlargement. A simulation study with steel spheres was carried out to study the possible error of over-estimation of the postembolization volume due to the beam-hardening artifact. RESULTS: There was no procedure-related rupture of the aneurysms. The percentage by volume of solid coil within the coil mass ranged from 15.78% to 82.01% in the present series. All aneurysms showed distension which ranged from 0.09% to 34.23%. The distensibility of the aneurysms was 34.23%. Error due to the beam-hardening artifact was negligible. CONCLUSION: Endoluminal packing of intracranial saccular aneurysms with embolization coils could cause a certain degree of distension in aneurysms treated with coil embolization, with the degree of distension up to 34.2%. Intracranial aneurysms were able to tolerate a certain degree of endoluminal distension without a risk of immediate rupture, even those that had ruptured recently.

Augmented vessels for quantitative analysis of vascular abnormalities and endovascular treatment planning.

Endovascular treatment plays an important role in the minimally invasive treatment of patients with vascular diseases, a major cause of morbidity and mortality worldwide. Given a segmentation of an angiography, quantitative analysis of abnormal structures can aid radiologists in choosing appropriate treatments and apparatuses. However, effective quantitation is only attainable if the abnormalities are identified from the vasculature. To achieve this, a novel method is developed, which works on the simpler shape of normal vessels to identify different vascular abnormalities (viz. stenotic atherosclerotic plaque, and saccular and fusiform aneurysmal lumens) in an indirect fashion, instead of directly manipulating the complex-shaped abnormalities. The proposed method has been tested on three synthetic and 17 clinical data sets. Comparisons with two related works are also conducted. Experimental results show that our method can produce satisfactory identification of the abnormalities and approximations of the ideal post-treatment vessel lumens. In addition, it can help increase the repeatability of the measurement of clinical parameters significantly.

Toward interactive user guiding vessel axis extraction from gray-scale angiograms: an optimization framework.

We propose a novel trace-based method to extract vessel axes from gray-scale angiograms without preliminary segmentations. Our method traces the axes on an optimization framework with the bounded spherical projection images and the sum of squared difference metric. It does not take alternate steps to search the next axial point and its tangent as in other trace-based algorithms, instead the novel method finds the solution simultaneously. This helps avoid U-turns of the trace and large spatial discontinuity of the axial points. Another advantage of the method is that it enables interactive user guidance to produce continuous tracing through regions that contain furcations, disease portions, kissing vessels (vessels in close proximity to each other) and thin vessels, which pose difficulties for the other algorithms and make re-initialization inevitable as illustrated on synthetic and clinical data sets.

Statistical cerebrovascular segmentation in three-dimensional rotational angiography based on maximum intensity projections.

Segmentation of three-dimensional rotational angiography (3D-RA) can provide quantitative 3D morphological information of vasculature. The expectation maximization-(EM-) based segmentation techniques have been widely used in the medical image processing community, because of the implementation simplicity, and computational efficiency of the approach. In a brain 3D-RA, vascular regions usually occupy a very small proportion (around 1%) inside an entire image volume. This severe imbalance between the intensity distributions of vessels and background can lead to inaccurate statistical modeling in the EM-based segmentation methods, and thus adversely affect the segmentation quality for 3D-RA. In this paper we present a new method for the extraction of vasculature in 3D-RA images. The new method is fully automatic and computationally efficient. As compared with the original 3D-RA volume, there is a larger proportion (around 20%) of vessels in its corresponding maximum intensity projection (MIP) image. The proposed method exploits this property to increase the accuracy of statistical modeling with the EM algorithm. The algorithm takes an iterative approach to compiling the 3D vascular segmentation progressively with the segmentation of MIP images along the three principal axes, and use a winner-takes-all strategy to combine the results obtained along individual axes. Experimental results on 12 3D-RA clinical datasets indicate that the segmentations obtained by the new method exhibit a high degree of agreement to the ground truth segmentations and are comparable to those produced by the manual optimal global thresholding method.

Bayesian image segmentation using local iso-intensity structural orientation.

Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model.