We report the lasing performance and photobleaching of gain material containing a water solution of Rhodamine 6G dye and gold nanoparticles (NPs). In comparison to a pure dye solution, the investigated material demonstrated both enhancement and quenching of the lasing output, depending on the relative concentration of the gold NPs. Although the presence of NPs with an optimized concentration looks preferable in terms of the lasing output enhancement, such additives deteriorate the operational resource of the gain material; i.e., the photobleaching rate speeds up.

RATIONAL AND OBJECTIVES: To evaluate our prototype method for segmentation and tracking of the coronary arterial tree, which is the foundation for a computer-aided detection (CADe) system to be developed to assist radiologists in detecting non-calcified plaques in coronary CT angiography (cCTA) scans. MATERIALS AND METHODS: The heart region was first extracted by a morphological operation and an adaptive thresholding method based on expectation-maximization (EM) estimation. The vascular structures within the heart region were enhanced and segmented using a multiscale coronary response (MSCAR) method that combined 3D multiscale filtering, analysis of the eigenvalues of Hessian matrices and EM estimation segmentation. After the segmentation of vascular structures, the coronary arteries were tracked by a 3D dynamic balloon tracking (DBT) method. The DBT method started at two manually identified seed points located at the origins of the left and right coronary arteries (LCA and RCA) for extraction of the arterial trees. The coronary arterial trees of a data set containing 20 ECG-gated contrast-enhanced cCTA scans were extracted by our MSCAR-DBT method and a clinical GE Advantage workstation. Two experienced thoracic radiologists visually examined the coronary arteries on the original cCTA scans and the rendered volume of segmented vessels to count the untracked false-negative (FN) segments and false positives (FPs) for both methods. RESULTS: For the visible coronary arterial segments in the 20 cases, the radiologists identified that 25 segments were missed by our MSCAR-DBT method, ranging from 0 to 5 FN segments in individual cases, and that 55 artery segments were missed by the GE software, ranging from 0 to 7 FN segments in individual cases. 19 and 15 FPs were identified in our and the GE coronary trees, ranging from 0 to 4 FPs for both methods in individual cases, respectively. CONCLUSION: The preliminary study demonstrates the feasibility of our MSCAR-DBT method for segmentation and tracking coronary artery trees. The results indicated that both our method and GE software can extract coronary artery trees reasonably well and the performance of our method is superior to that of GE software in this small data set. Further studies are underway to develop methods for improvement of the segmentation and tracking accuracy.

Advances in computed tomography (CT) scanner technology have made isotropic volumetric, multiplanar high-resolution lung imaging possible in a single breath-hold, a significant advance over the incremental high-resolution CT (HRCT) technique in which noncontiguous images sampled the lung, but lacked anatomic continuity. HRCT of the lungs is an established imaging technique for the diagnosis and management of interstitial lung disease, emphysema, and small airway disease, providing a noninvasive detailed evaluation of the lung parenchyma, and providing information about the lungs as a whole and focally. In addition to having a high degree of specificity for diagnosing conditions such as emphysema, sarcoidosis, usual interstitial pneumonitis, Langerhans cell histiocytosis, and small airway disease, there is a growing body of medical evidence to support the use of HRCT findings or diagnosis to predict patient prognosis. In this article, we review the technique, advantages, and clinical applications of the current HRCT technique.

Cardiac toxicity is an important sequela of breast radiotherapy. However, the relationship between dose to cardiac structures and subsequent toxicity has not been well defined, partially due to variations in substructure delineation, which can lead to inconsistent dose reporting and the failure to detect potential correlations. Here we have developed a heart atlas and evaluated its effect on contour accuracy and concordance. A detailed cardiac computed tomography scan atlas was developed jointly by cardiology, cardiac radiology, and radiation oncology. Seven radiation oncologists were recruited to delineate the whole heart, left main and left anterior descending interventricular branches, and right coronary arteries on four cases before and after studying the atlas. Contour accuracy was assessed by percent overlap with gold standard atlas volumes. The concordance index was also calculated. Standard radiation fields were applied. Doses to observer-contoured cardiac structures were calculated and compared with gold standard contour doses. Pre- and post-atlas values were analyzed using a paired t test. The cardiac atlas significantly improved contour accuracy and concordance. Percent overlap and concordance index of observer-contoured cardiac and gold standard volumes were 2.3-fold improved for all structures (p < 0.002). After application of the atlas, reported mean doses to the whole heart, left main artery, left anterior descending interventricular branch, and right coronary artery were within 0.1, 0.9, 2.6, and 0.6 Gy, respectively, of gold standard doses. This validated University of Michigan cardiac atlas may serve as a useful tool in future studies assessing cardiac toxicity and in clinical trials which include dose volume constraints to the heart.

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.

OBJECTIVE: The purpose of this study was to determine whether the view used, multiplanar or axial, for image interpretation at pulmonary CT angiography for suspected acute pulmonary embolism alters the diagnostic confidence, accuracy, and interpretation time of cardiothoracic radiology specialists and radiology residents. MATERIALS AND METHODS: Patients who underwent 50 consecutive pulmonary 64-MDCT angiographic examinations formed the study group (18 men, 32 women; mean age, 53 years; range, 19-93 years). Three blinded cardiothoracic faculty radiologists and three blinded radiology residents reviewed each case independently initially using only axial display mode and later using multiplanar reformation (MPR) in any x-, y-, or z-axis. The presence of pulmonary embolism in the main through subsegmental pulmonary arteries was scored on a 5-point scale; diagnostic confidence for the overall examination was scored on a 3-point scale; and interpretation time was recorded. A surrogate reference standard consisted of either faculty agreement or, in cases of disagreement, adjudication by another, senior faculty member. Statistical analysis included the Kendall coefficient (W), receiver operating characteristics curves, and a univariate repeated measures model. RESULTS: Interobserver agreement between specialists on the diagnosis of pulmonary embolism was good for axial viewing (W=0.72) and for MPR viewing (W=0.79). Interobserver agreement between residents was good for axial viewing (W=0.62) and for MPR viewing (W=0.70). Reader confidence improved among all readers with MPR viewing, but the difference did not reach statistical significance. Interpretation time with MPR was significantly longer for two of the three specialists and significantly shorter for two of the three residents. CONCLUSION: Use of MPR for viewing increased the reader agreement and interpretation time of cardiothoracic specialists but increased reader agreement between residents and might have decreased interpretation time. All readers had a trend toward increased confidence.

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.

OBJECTIVE: The purpose of our study was to determine whether CT can accurately evaluate mechanical heart valve size and function. MATERIALS AND METHODS: Sixty-two patients with mechanical valves (37 single-disc, 27 bileaflet; 59 aortic, 5 mitral) were evaluated with ECG-gated 64-MDCT and transthoracic echocardiography; a subset of 10 patients underwent cinefluoroscopy. Two readers independently interpreted each study. RESULTS: The mean age of the patients was 46.4 +/- 14.4 years; 50 were men and 12 were women. There was excellent correlation, and differences between CT readers were absent to small in measuring the opening angle (r = 0.96, p < 0.001; 76.7 +/- 9.0 degrees vs 76.8 +/- 9.6 degrees , p = 0.73), annulus diameter (r = 0.96, p < 0.001; 25.9 +/- 3.3 vs 25.9 +/- 3.2 mm, p = 0.62), and geometric orifice area (r = 0.98, p < 0.001; 3.8 +/- 0.9 vs 3.6 +/- 0.8 cm(2), p < 0.001). There was strong correlation without difference in opening angle between CT and cinefluoroscopy (r = 0.77, p < 0.001; 79.2 degrees +/- 9.8 degrees vs 77.2 degrees +/- 15.5 degrees , p = 0.45). Compared with manufacturer specifications, CT reported opening angles that were smaller for single-disc valves (n = 36, 67.4 degrees +/- 5.7 degrees vs 75 degrees , p < 0.001) and similar for bileaflet valves (n = 42 for 21 valves, 83.8 degrees +/- 3.9 degrees vs 85 degrees , p = 0.05), valves, with small underestimation with CT versus specifications in annulus diameter (n = 41; r = 0.75, p < 0.001; 26.4 +/- 3.0 vs 27.5 +/- 3.3 mm, p = 0.003), and geometric orifice area (n = 35; r = 0.90, p < 0.001; 3.7 +/- 0.7 vs 3.8 +/- 0.8 cm(2), p = 0.04). Each disc closed fully on CT; none had more than mild regurgitation on echocardiography. CONCLUSION: CT can measure the size and function of mechanical valves with high interobserver agreement and results similar to specifications. The opening angle with CT strongly correlates with cinefluoroscopy. CT is promising for the assessment of mechanical valves.

The authors are developing a computer-aided detection system for pulmonary emboli (PE) in computed tomographic pulmonary angiography (CTPA) scans. The pulmonary vessel tree is extracted using a 3D expectation-maximization segmentation method based on the analysis of eigen-values of Hessian matrices at multiple scales. A parallel multiprescreening method is applied to the segmented vessels to identify volume of interests (VOIs) that contained suspicious PE. A linear discriminant analysis (LDA) classifier with feature selection is designed to reduce false positives (FPs). Features that characterize the contrast, gray level, and size of PE are extracted as input predictor variables to the LDA classifier. With the IRB approval, 59 CTPA PE cases were collected retrospectively from the patient files (UM cases). With access permission, 69 CTPA PE cases were randomly selected from the data set of the prospective investigation of pulmonary embolism diagnosis (PIOPED) II clinical trial. Extensive lung parenchymal or pleural diseases were present in 22/59 UM and 26/69 PIOPED cases. Experienced thoracic radiologists manually marked 595 and 800 PE as the reference standards in the UM and PIOPED data sets, respectively. PE occlusion of arteries ranged from 5% to 100%, with PE located from the main pulmonary artery to the subsegmental artery levels. Of the 595 PE identified in the UM cases, 245 and 350 PE were located in the subsegmental arteries and the more proximal arteries, respectively. The detection performance was assessed by free response ROC (FROC) analysis. The FROC analysis indicated that the PE detection system could achieve an overall sensitivity of 80% at 18.9 FPs/case for the PIOPED cases when the LDA classifier was trained with the UM cases. The test sensitivity with the UM cases was 80% at 22.6 FPs/cases when the LDA classifier was trained with the PIOPED cases. The detection performance depended on the arterial level where the PE was located and on the percentage of occlusion. The sensitivity was lower for PE in the subsegmental arteries than in more proximal arteries and was lower for PE with less than 20% occlusion. The results indicate that the PE detection system achieves high sensitivity for PE detection on independent CTPA scans for both the PIOPED and UM data sets and demonstrate the potential that the automated PE detection approach can be generalized to unknown cases.

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.