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Rationale and Objectives: The aim of this study was to evaluate the effect of computer-aided diagnosis (CAD) on radiologists' estimates of the likelihood of malignancy of lung nodules on computed tomographic (CT) imaging., Methods and Materials: A total of 256 lung nodules (124 malignant, 132 benign) were retrospectively collected from the thoracic CT scans of 152 patients. An automated CAD system was developed to characterize and provide malignancy ratings for lung nodules on CT volumetric images. An observer study was conducted using receiver-operating characteristic analysis to evaluate the effect of CAD on radiologists' characterization of lung nodules. Six fellowship-trained thoracic radiologists served as readers. The readers rated the likelihood of malignancy on a scale of 0% to 100% and recommended appropriate action first without CAD and then with CAD. The observer ratings were analyzed using the Dorfman-Berbaum-Metz multireader, multicase method., Results: The CAD system achieved a test area under the receiver-operating characteristic curve (A(z)) of 0.857 +/- 0.023 using the perimeter, two nodule radii measures, two texture features, and two gradient field features. All six radiologists obtained improved performance with CAD. The average A(z) of the radiologists improved significantly (P < .01) from 0.833 (range, 0.817-0.847) to 0.853 (range, 0.834-0.887)., Conclusion: CAD has the potential to increase radiologists' accuracy in assessing the likelihood of malignancy of lung nodules on CT imaging.

Rationale and Objectives: To retrospectively investigate the effect of a computer-aided detection (CAD) system on radiologists' performance for detecting small pulmonary nodules in computed tomography (CT) examinations, with a panel of expert radiologists serving as the reference standard., Materials and Methods: Institutional review board approval was obtained. Our dataset contained 52 CT examinations collected by the Lung Image Database Consortium, and 33 from our institution. All CTs were read by multiple expert thoracic radiologists to identify the reference standard for detection. Six other thoracic radiologists read the CT examinations first without and then with CAD. Performance was evaluated using free-response receiver operating characteristics (FROC) and the jackknife FROC analysis methods (JAFROC) for nodules above different diameter thresholds., Results: A total of 241 nodules, ranging in size from 3.0 to 18.6 mm (mean, 5.3 mm) were identified as the reference standard. At diameter thresholds of 3, 4, 5, and 6 mm, the CAD system had a sensitivity of 54%, 64%, 68%, and 76%, respectively, with an average of 5.6 false positives (FPs) per scan. Without CAD, the average figures of merit (FOMs) for the six radiologists, obtained from JAFROC analysis, were 0.661, 0.729, 0.793, and 0.838 for the same nodule diameter thresholds, respectively. With CAD, the corresponding average FOMs improved to 0.705, 0.763, 0.810, and 0.862, respectively. The improvement achieved statistical significance for nodules at the 3 and 4 mm thresholds (P = .002 and .020, respectively), and did not achieve significance at 5 and 6 mm (P = .18 and .13, respectively). At a nodule diameter threshold of 3 mm, the radiologists' average sensitivity and FP rate were 0.56 and 0.67, respectively, without CAD, and 0.67 and 0.78 with CAD., Conclusion: CAD improves thoracic radiologists' performance for detecting pulmonary nodules smaller than 5 mm on CT examinations, which are often overlooked by visual inspection alone.

The purpose of this work is to develop a computer-aided diagnosis (CAD) system to differentiate malignant and benign lung nodules on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a 3D active contour method. The initial contour was obtained as the boundary of a binary object generated by k-means clustering within the VOI and smoothed by morphological opening. A data set of 256 lung nodules (124 malignant and 132 benign) from 152 patients was used in this study. In addition to morphological and texture features, the authors designed new nodule surface features to characterize the lung nodule surface smoothness and shape irregularity. The effects of two demographic features, age and gender, as adjunct to the image features were also investigated. A linear discriminant analysis (LDA) classifier built with features from stepwise feature selection was trained using simplex optimization to select the most effective features. A two-loop leave-one-out resampling scheme was developed to reduce the optimistic bias in estimating the test performance of the CAD system. The area under the receiver operating characteristic curve, A(z), for the test cases improved significantly (p < 0.05) from 0.821 +/- 0.026 to 0.857 +/- 0.023 when the newly developed image features were included with the original morphological and texture features. A similar experiment performed on the data set restricted to primary cancers and benign nodules, excluding the metastatic cancers, also resulted in an improved test A(z), though the improvement did not reach statistical significance (p = 0.07). The two demographic features did not significantly affect the performance of the CAD system (p > 0.05) when they were added to the feature space containing the morphological, texture, and new gradient field and radius features. To investigate if a support vector machine (SVM) classifier can achieve improved performance over the LDA classifier, we compared the performance of the LDA and SVMs with various kernels and parameters. Principal component analysis was used to reduce the dimensionality of the feature space for both the LDA and the SVM classifiers. When the number of selected principal components was varied, the highest test A(z) among the SVMs of various kernels and parameters was slightly higher than that of the LDA in one-loop leave-one-case-out resampling. However, no SVM with fixed architecture consistently performed better than the LDA in the range of principal components selected. This study demonstrated that the authors' proposed segmentation and feature extraction techniques are promising for classifying lung nodules on CT images.

The authors are developing a computerized pulmonary vessel segmentation method for a computer-aided pulmonary embolism (PE) detection system on computed tomographic pulmonary angiography (CTPA) images. Because PE only occurs inside pulmonary arteries, an automatic and accurate segmentation of the pulmonary vessels in 3D CTPA images is an essential step for the PE CAD system. To segment the pulmonary vessels within the lung, the lung regions are first extracted using expectation-maximization (EM) analysis and morphological operations. The authors developed a 3D multiscale filtering technique to enhance the pulmonary vascular structures based on the analysis of eigenvalues of the Hessian matrix at multiple scales. A new response function of the filter was designed to enhance all vascular structures including the vessel bifurcations and suppress nonvessel structures such as the lymphoid tissues surrounding the vessels. An EM estimation is then used to segment the vascular structures by extracting the high response voxels at each scale. The vessel tree is finally reconstructed by integrating the segmented vessels at all scales based on a "connected component" analysis. Two CTPA cases containing PEs were used to evaluate the performance of the system. One of these two cases also contained pleural effusion disease. Two experienced thoracic radiologists provided the gold standard of pulmonary vessels including both arteries and veins by manually tracking the arterial tree and marking the center of the vessels using a computer graphical user interface. The accuracy of vessel tree segmentation was evaluated by the percentage of the "gold standard" vessel center points overlapping with the segmented vessels. The results show that 96.2% (2398/2494) and 96.3% (1910/1984) of the manually marked center points in the arteries overlapped with segmented vessels for the case without and with other lung diseases. For the manually marked center points in all vessels including arteries and veins, the segmentation accuracy are 97.0% (4546/4689) and 93.8% (4439/4732) for the cases without and with other lung diseases, respectively. Because of the lack of ground truth for the vessels, in addition to quantitative evaluation of the vessel segmentation performance, visual inspection was conducted to evaluate the segmentation. The results demonstrate that vessel segmentation using our method can extract the pulmonary vessels accurately and is not degraded by PE occlusion to the vessels in these test cases.

An automated method is being developed in order to identify corresponding nodules in serial thoracic CT scans for interval change analysis. The method uses the rib centerlines as the reference for initial nodule registration. A spatially adaptive rib segmentation method first locates the regions where the ribs join the spine, which define the starting locations for rib tracking. Each rib is tracked and locally segmented by expectation-maximization. The ribs are automatically labeled, and the centerlines are estimated using skeletonization. For a given nodule in the source scan, the closest three ribs are identified. A three-dimensional (3D) rigid affine transformation guided by simplex optimization aligns the centerlines of each of the three rib pairs in the source and target CT volumes. Automatically defined control points along the centerlines of the three ribs in the source scan and the registered ribs in the target scan are used to guide an initial registration using a second 3D rigid affine transformation. A search volume of interest (VOI) is then located in the target scan. Nodule candidate locations within the search VOI are identified as regions with high Hessian responses. The initial registration is refined by searching for the maximum cross-correlation between the nodule template from the source scan and the candidate locations. The method was evaluated on 48 CT scans from 20 patients. Experienced radiologists identified 101 pairs of corresponding nodules. Three metrics were used for performance evaluation. The first metric was the Euclidean distance between the nodule centers identified by the radiologist and the computer registration, the second metric was a volume overlap measure between the nodule VOIs identified by the radiologist and the computer registration, and the third metric was the hit rate, which measures the fraction of nodules whose centroid computed by the computer registration in the target scan falls within the VOI identified by the radiologist. The average Euclidean distance error was 2.7 +/- 3.3 mm. Only two pairs had an error larger than 10 mm. The average volume overlap measure was 0.71 +/- 0.24. Eighty-three of the 101 pairs had ratios larger than 0.5, and only two pairs had no overlap. The final hit rate was 93/101.

Objective: The educational objective of this evidence-based self-assessment module is to use case examples to review the current evidence and the roles of CT and MRI in evaluating and managing patients with both congenital and acquired coronary artery disease., Conclusion: In this educational module, we review the use of CT and MRI in the noninvasive diagnosis and management of patients with coronary artery disease.

We are developing a computer-aided diagnosis (CAD) system to classify malignant and benign lung nodules found on CT scans. A fully automated system was designed to segment the nodule from its surrounding structured background in a local volume of interest (VOI) and to extract image features for classification. Image segmentation was performed with a three-dimensional (3D) active contour (AC) method. A data set of 96 lung nodules (44 malignant, 52 benign) from 58 patients was used in this study. The 3D AC model is based on two-dimensional AC with the addition of three new energy components to take advantage of 3D information: (1) 3D gradient, which guides the active contour to seek the object surface, (2) 3D curvature, which imposes a smoothness constraint in the z direction, and (3) mask energy, which penalizes contours that grow beyond the pleura or thoracic wall. The search for the best energy weights in the 3D AC model was guided by a simplex optimization method. Morphological and gray-level features were extracted from the segmented nodule. The rubber band straightening transform (RBST) was applied to the shell of voxels surrounding the nodule. Texture features based on run-length statistics were extracted from the RBST image. A linear discriminant analysis classifier with stepwise feature selection was designed using a second simplex optimization to select the most effective features. Leave-one-case-out resampling was used to train and test the CAD system. The system achieved a test area under the receiver operating characteristic curve (A(z)) of 0.83 +/- 0.04. Our preliminary results indicate that use of the 3D AC model and the 3D texture features surrounding the nodule is a promising approach to the segmentation and classification of lung nodules with CAD. The segmentation performance of the 3D AC model trained with our data set was evaluated with 23 nodules available in the Lung Image Database Consortium (LIDC). The lung nodule volumes segmented by the 3D AC model for best classification were generally larger than those outlined by the LIDC radiologists using visual judgment of nodule boundaries.

Rationale and Objectives: We sought to develop a computer-aided diagnosis (CAD) system for assisting radiologists in the detection of pulmonary embolism (PE) on computed tomography pulmonary angiographic (CTPA) images., Materials and Methods: An adaptive three-dimensional (3D) voxel clustering method was developed based on expectation-maximization (EM) analysis to extract vessels from their surrounding tissues. Using a connected component analysis, the vessel tree was reconstructed by tracking the vessel and its branches in 3D space. The tracked vessels were prescreened for suspicious PE areas using a second EM analysis. A rule-based false-positive (FP) reduction method was designed to detect true PE based on the features of PE and vessels. In this preliminary study, 14 patients with positive CTPA for PE were studied. CT scans were performed at 1.25-mm collimation using a GE LightSpeed CT scanner; eight of these patients also had extensive lung parenchymal or pleural disease. One hundred sixty-three emboli were identified by two experienced thoracic radiologists. The emboli identified by the radiologists were used as the "gold standard." For each embolus, the percent diameter occlusion (clot) and conspicuity of embolus (rating of 1 to 5, with 5 being the most conspicuous) were visually estimated. One hundred one emboli were identified in the six patients without lung diseases; 57 were proximal to the subsegmental and 44 were subsegmental. For the eight patients with lung diseases, 62 emboli were identified, of which 37 were proximal to the subsegmental and 25 were subsegmental. A computer-detected volume was counted as true-positive when it overlapped with an embolus volume identified by the radiologists., Results: In the cases without lung diseases, if the PE had a conspicuity of >2 and only partially (20%-80%) occluded the vessel, our method detected 92.0% of proximal emboli and 77.8% of subsegmental emboli, with an average of 18.3 FPs/case. In the cases containing extensive lung disease, 66.7% and 40.0% of the PEs were detected with an average of 11.4 FPs/case under the same conditions. For the 14 PE cases, 13 of them were diagnosed as positive PE cases (case sensitivity was 92.9%)., Conclusion: This preliminary study indicates that our automated method is a promising approach to CAD of PE on CTPA. Further study is under way to collect a larger data set and to improve the detection accuracy for PE, especially those with <20% or >80% occlusion, and for very subtle PE. A fully developed CAD system is expected to provide a useful aid for PE detection on CTPA.

Purpose: To compare the frequency of well-visualized pulmonary arteries according to anatomic level by using different collimation with single- and multi-detector row computed tomography (CT) in patients suspected of having acute pulmonary embolism., Materials and Methods: Sixty patients were examined with one of three techniques (20 patients each). Group 1 was examined with single-detector row CT with 3-mm collimation and 1.3-1.6 pitch; groups 2 and 3, with multi-detector row CT with 2.5- and 1.25-mm collimation, respectively. Three thoracic radiologists independently reviewed examination findings to determine if each main, lobar, segmental, and subsegmental artery was well visualized for presence of pulmonary embolism. chi2 tests were performed. For well-visualized vessels, the presence and/or absence of pulmonary embolism was recorded and kappa statistic was determined., Results: Reader 1 scored 95% (114 of 120), 96% (115 of 120), and 99% (119 of 120) of lobar arteries (P >.05); 76% (304 of 400), 86% (346 of 400), and 91% (363 of 400) of segmental arteries (P <.001); and 37% (300 of 800), 56% (448 of 800), and 76% (608 of 800) of subsegmental arteries as well visualized (P <.001) using techniques 1, 2, and 3, respectively. Reader 2 scored 97% (116 of 120), 95% (114 of 120), and 99% (119 of 120) of lobar arteries (P >.05); 77% (308 of 400), 87% (349 of 400), and 93% (371 of 400) of segmental arteries (P <.001); and 39% (310 of 800), 53% (422 of 800), and 78% (621 of 800) of subsegmental arteries (P <.001) as well visualized using techniques 1, 2, and 3, respectively. Reader 3 scored 86% (103 of 120), 82% (98 of 120), and 91% (109 of 120) of lobar arteries (P >.05); 63% (252 of 400), 70% (280 of 400), and 85% (339 of 400) of segmental arteries (P <.001); and 39% (310 of 800), 56% (451 of 800), and 71% (572 of 800) of subsegmental arteries (P <.001) as well visualized using techniques 1, 2, and 3, respectively. Sixteen patients had pulmonary embolism. Interobserver agreement for detection of pulmonary embolism was significantly better for segmental and subsegmental arteries for all readers with technique 3 (segmental, kappa = 0.79-0.80; subsegmental, kappa = 0.71-0.76) than that with technique 1 (segmental, kappa = 0.47-0.75; subsegmental, kappa = 0.28-0.54)., Conclusion: Multi-detector row CT at 1.25-mm collimation significantly improves visualization of segmental and subsegmental arteries and interobserver agreement in detection of pulmonary embolism., (Copyright RSNA, 2003)