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Purpose: Pulmonary arterial hypertension (PAH) is a progressive vascular disease that results in high mortality and morbidity in sickle cell disease (SCD) patients. PAH diagnosis is invasive via right heart catheterization, but manual measurements of the main pulmonary artery (PA) diameters from computed tomography (CT) have shown promise as noninvasive surrogate marker of PAH. The authors propose a semiautomated computer-assisted diagnostic (CAD) tool to quantify the main PA size from pulmonary CT angiography (CTA). Methods: A follow-up retrospective study investigated the potential of CT and image analysis to quantify the presence of PAH secondary to SCD based on PA size. The authors segmented the main pulmonary arteries using a combination of fast marching level sets and geodesic active contours from smoothed pulmonary CTA images of 20 SCD patients with proven PAH by right heart catheterization and 20 matched negative controls. From the PA segmentation, a Euclidean distance map was calculated and an algorithm based on fast marching methods was used to compute subvoxel precise centerlines of the PA trunk (PT) and main left/right PA (PM). Maximum distentions of PT and PM were automatically quantified using the centerline and validated with manual measurements from two observers. Results: The pulmonary trunk and main were significantly larger (p<0.001) in PAH/SCD patients (33.73±3.92 mm for PT and 25.17±2.90 for PM) than controls (27.03±2.94 mm for PT and 20.62±3.06 for PM). The discrepancy was qualitatively improved when vessels’ diameters were normalized by body surface area (p<0.001). The validation of the method showed high correlation (mean R=0.9 for PT and R=0.91 for PM) and Bland–Altman agreement (0.4±3.6 mm for PT and 0.5±2.9 mm for PM) between CAD and manual measurements. Quantification errors were comparable to intraobserver and interobserver variability. CAD measurements between two different users were robust and reproducible with correlations of R=0.99 for both PT and PM and Bland–Altman agreements of -0.13±1.33 mm for PT and -0.08±0.84 mm for PM. Conclusion: Results suggest that the semiautomated quantification of pulmonary artery has sufficient accuracy and reproducibility for clinical use. CT with image processing and extraction of PA biomarkers show great potential as a surrogate indicator for diagnosis or quantification of PAH, and could be an important tool for drug discovery and noninvasive clinical surveillance. [ABSTRACT FROM AUTHOR]

Purpose: In computed tomographic colonography (CTC), a patient will be scanned twice—Once supine and once prone—to improve the sensitivity for polyp detection. To assist radiologists in CTC reading, in this paper we propose an automated method for colon registration from supine and prone CTC scans. Methods: We propose a new colon centerline registration method for prone and supine CTC scans using correlation optimized warping (COW) and canonical correlation analysis (CCA) based on the anatomical structure of the colon. Four anatomical salient points on the colon are first automatically distinguished. Then correlation optimized warping is applied to the segments defined by the anatomical landmarks to improve the global registration based on local correlation of segments. The COW method was modified by embedding canonical correlation analysis to allow multiple features along the colon centerline to be used in our implementation. Results: We tested the COW algorithm on a CTC data set of 39 patients with 39 polyps (19 training and 20 test cases) to verify the effectiveness of the proposed COW registration method. Experimental results on the test set show that the COW method significantly reduces the average estimation error in a polyp location between supine and prone scans by 67.6%, from 46.27±52.97 to 14.98 mm±11.41 mm, compared to the normalized distance along the colon centerline algorithm (p<0.01). Conclusions: The proposed COW algorithm is more accurate for the colon centerline registration compared to the normalized distance along the colon centerline method and the dynamic time warping method. Comparison results showed that the feature combination of z-coordinate and curvature achieved lowest registration error compared to the other feature combinations used by COW. The proposed method is tolerant to centerline errors because anatomical landmarks help prevent the propagation of errors across the entire colon centerline. [ABSTRACT FROM AUTHOR]

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6–9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p=7.78×10-5) for the size category of 6–9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p=7.95×10-5) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95%CI [0.75%, 16%], p=0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists. [ABSTRACT FROM AUTHOR]