Functional analysis of arteriovenous fistulae in non-contrast magnetic resonance images.

BACKGROUND AND OBJECTIVE: Arteriovenous fistulae (AVF) are the preferred mode of hemodialysis vascular access and their successful maturation is critical to reduce patient morbidity, mortality, cost, and improve quality of life. Peri-anastomotic venous segment stenosis is the primary cause of AVF maturation failure. The objective is to develop a software protocol for the functional analysis of arteriovenous fistula. METHOD: We have developed a standard protocol for the anatomical analysis of the AVF to better understand the mechanisms involved in AVF stenosis and to identify future imaging biomarkers for AVF success or failure using non-contrast magnetic resonance imaging (MRI). The 3D model of the AVF is created using a polar dynamic programming technique. Analysis has been performed on six Yorkshire cross domestic swine, but techniques can be applied into clinical settings. RESULTS: Differences in AVF angles and vein curvature are associated with significant variability of venous cross-sectional area. This suggests that the pattern of stenosis is likely to be dependent upon hemodynamic profiles which are largely determined by AVF anatomical features and could play an important role in AVF maturation. CONCLUSIONS: This protocol enables us to visualize and study the hemodynamic profiles indirectly allowing early stratification of patients into high and low risk groups for AVF maturation failure. High risk patients could then be targeted with an enhanced process of care or future maturation enhancing therapies resulting in a much-needed precision-medicine approach to dialysis vascular access.

Segmentation of the right ventricle in four chamber cine cardiac MR images using polar dynamic programming.

The four chamber plane is currently underutilized in the right ventricular segmentation community. Four chamber information can be useful to determine ventricular short axis stacks and provide a rough estimate of the right ventricle in short axis stacks. In this study, we develop and test a semi-automated technique for segmenting the right ventricle in four chamber cine cardiac magnetic resonance images. The three techniques that use minimum cost path algorithms were used. The algorithms are: Dijkstra's shortest path algorithm (Dijkstra), an A* algorithm that uses length, curvature and torsion into an active contour model (ALCT), and a variation of polar dynamic programming (PDP). The techniques are evaluated against the expert traces using 175 cardiac images from 7 patients. The evaluation first looks at mutual overlap metrics and then focuses on clinical measures such as fractional area change (FAC). The mean mutual overlap between the physician's traces ranged from 0.85 to 0.88. Using as reference physician 1's landmarks and traces (i.e., comparing the traces from physician 1 to the semi-automated segmentation using physician 1's landmarks), the PDP algorithm has a mean mutual overlap of 0.8970 compared to 0.8912 for ALCT and 0.8879 for Dijkstra. The mean mutual overlap between the BP regions generated by physician 1 and physician 2 landmarks are 0.9674, 0.9605 and 0.9531 for PDP, ALCT and Dijkstra, respectively. The FAC correlation coefficient between the physician's traces ranged from 0.73 to 0.93.

Dynamic Programming Using Polar Variance for Image Segmentation.

When using polar dynamic programming (PDP) for image segmentation, the object size is one of the main features used. This is because if size is left unconstrained the final segmentation may include high-gradient regions that are not associated with the object. In this paper, we propose a new feature, polar variance, which allows the algorithm to segment the objects of different sizes without the need for training data. The polar variance is the variance in a polar region between a user-selected origin and a pixel we want to analyze. We also incorporate a new technique that allows PDP to segment complex shapes by finding low-gradient regions and growing them. The experimental analysis consisted on comparing our technique with different active contour segmentation techniques on a series of tests. The tests consisted on robustness to additive Gaussian noise, segmentation accuracy with different grayscale images and finally robustness to algorithm-specific parameters. Experimental results show that our technique performs favorably when compared with other segmentation techniques.

Automated breast segmentation of fat and water MR images using dynamic programming.

RATIONALE AND OBJECTIVES: To develop and test an algorithm that outlines the breast boundaries using information from fat and water magnetic resonance images. MATERIALS AND METHODS: Three algorithms were implemented and tested using registered fat and water magnetic resonance images. Two of the segmentation algorithms are simple extensions of the techniques used for contrast-enhanced images: one algorithm uses clustering and local gradient (CLG) analysis and the other algorithm uses a Hessian-based sheetness filter (HSF). The third segmentation algorithm uses k-means++ and dynamic programming (KDP) for finding the breast pixels. All three algorithms separate the left and right breasts using either a fixed region or a morphological method. The performance is quantified using a mutual overlap (Dice) metric and a pectoral muscle boundary error. The algorithms are evaluated against three manual tracers using 266 breast images from 14 female subjects. RESULTS: The KDP algorithm has a mean overlap percentage improvement that is statistically significant relative to the HSF and CLG algorithms. When using a fixed region to remove the tissue between breasts with tracer 1 as a reference, the KDP algorithm has a mean overlap of 0.922 compared to 0.864 (P < .01) for HSF and 0.843 (P < .01) for CLG. The performance of KDP is very similar to tracers 2 (0.926 overlap) and 3 (0.929 overlap). The performance analysis in terms of pectoral muscle boundary error showed that the fraction of the muscle boundary within three pixels of reference tracer 1 is 0.87 using KDP compared to 0.578 for HSF and 0.617 for CLG. Our results show that the performance of the KDP algorithm is independent of breast density. CONCLUSIONS: We developed a new automated segmentation algorithm (KDP) to isolate breast tissue from magnetic resonance fat and water images. KDP outperforms the other techniques that focus on local analysis (CLG and HSF) and yields a performance similar to human tracers.