Faculty Bibliography

Endovascular techniques have emerged as a minimally invasive alternative for the repair of the descending thoracic aorta, especially in high-risk patients. Multidetector computed tomography has a pivotal role, specifically in determining the candidacy or exclusion of patients for thoracic endovascular aortic repair and preoperative planning. In addition, multidetector computed tomography is used for follow-up assessment of the postsurgical aorta, so that potentially fatal complications can be correctly diagnosed and treated in a timely manner. In this pictorial review, we focus on the preoperative assessment of the pathologic aorta and evaluation after thoracic endovascular aortic repair

Purpose: Many potential lung cancers start out as small pulmonary nodules showing up as incidental findings on chest radiograph or computed tomography (CT) scans. Diagnosis is confirmed via biopsy, usually involving broncoscopy or CT-guided biopsy. However, these are invasive procedures that expose patients to additional risks. An alternative mode of tumor detection lies in administering successive chest CT scans. This has the advantage of avoiding those risks associated with biopsy. Overall, there is growing evidence for the effectiveness of low-dose CT for lung cancer screening. Morphology is an important indicator of malignant potential for lung nodules detected at CT. Automated methods for morphology assessment have previously been described for breast cancer visualized on mammography [1]. The most common measure of nodule shape is area-to-perimeter-length ratio (APR), low APR values being associated with spiculated or lobulated shape. APR is a static measure and thus highly susceptible to alterations by random noise and artifacts in image acquisition. We introduce and analyze the self-overlap (SO) method as a dynamic automated morphological detection scheme. SO measures the rate of change of nodule masks as a function of the radius of the blurring kernel. In other words, SO measures the degree to which a nodule's shape changes or stays intact upon successive pixel averaging that blurs the original image. Irregularities at the surface mean that a significant number of high-attenuation pixels (representing solid nodular tissue) are surrounded by low-attenuation pixels (representing air). Averaging each pixel with its neighboring pixels thus serves to trim back lobulations and spiculations from a nodule image. Hence, comparedto smooth nodules, lobulated and spiculated nodules are subject to more of this trimming upon successive averaging, so that their shape changes more, resulting in lower SO values. Due to its dynamical nature, we hypothesized that SO is more resilient to random image noise than APR. Methods: In experiment 1 we compare our algorithm with APR for nodules simulated using a spherical harmonic model (degree = 0-7) rasterized and contaminated with random noise. In experiment 2 we compare the new measure with a consensus of two expert morphology ratings of 119 nodules from clinical CT exams. Results: Experiment 1 shows that both methods display the desired trend in that APR and SO both decrease with increasing spherical harmonic degree-meaning more lobulations. As such both methods serve as measures of surface smoothness. However, SO displays significantly greater robustness to CT image noise; for both methods, we calculate variability as standard deviation over mean. We find that APR's variability in the face of random noise is on the order of ten times that of SO. This finding suggests that SO is much more robust than APR to the effects of random noise. Using a logistic regression model, in experiment 2 we achieved 89.9% agreement with the consensus assessment of two expert radiologists, versus 87.4% for APR. Conclusion: Simulation nodules show that both our dynamic method (SO) and a representative static method (APR) for automated lung nodule surface morphology determination yield clear trends as functions of surface smoothness. Hence both methods can, with proper fitting and cutoff selection, yield faithful predictions that have over 80% agreement with expert assessment. However, when the simulation nodules are subjected to random noise, SO yields much more consistent and reproducible results than APR. Overall, we conclude that our method, due to its robustness to the random noise and CT artifacts that can plague nodule images, is well suited for clinical application

RATIONALE AND OBJECTIVES: The aim of this study was to retrospectively evaluate an automated global scoring system for evaluating the extent and severity of disease in a known cohort of patients with documented bronchiectasis. On the basis of a combination of validated three-dimensional automated algorithms for bronchial tree extraction and quantitative airway measurements, global scoring combines the evaluation of bronchial lumen-to-artery ratios and bronchial wall-to-artery ratios, as well as the detection of mucoid-impacted airways. The result is an automatically generated global computed tomographic (CT) score designed to simplify and standardize the interpretation of scans in patients with chronic airway infections. MATERIALS AND METHODS: Twenty high-resolution CT data sets were used to evaluate an automated CT scoring method that combines algorithms for airway quantitative analysis that have been individually tested and validated. Patients with clinically documented atypical mycobacterial infections with visually assessed CT evidence of bronchiectasis varying from mild to severe were retrospectively selected. These data sets were evaluated by two independent experienced radiologists and by computer scoring, with the results compared statistically, including Spearman's rank correlation. RESULTS: Computer evaluation required 3 to 5 minutes per data set, compared to 12 to 15 minutes for manual scoring. Initial Spearman's rank tests showed positive correlations between automated and readers' global scores (r = 0.609, P = .01), extent of bronchiectasis (r = 0.69, P = .0004), and severity of bronchiectasis (r = 0.61, P = .01), while mucus plug detection showed a lesser extent of positive correlation between the scoring methods (r = 0.42, P = .07) and wall thickness a negative weak correlation (r = -0.10, P = .40). Further retrospective review of 24 lobes in which wall thickness scores showed the highest discrepancy between manual and automated methods was then performed, using electronic calipers and perpendicular cross-sections to reassess airway measurements. This resulted in an improved Spearman's rank correlation to r = 0.62 (P = .009), for a global score of r = 0.67 (P = .001). CONCLUSION: Automated computerized scoring shows considerable promise for providing a standardized, quantitative method, demonstrating overall good correlation with the results of experienced readers' evaluation of the extent and severity of bronchiectasis. It is speculated that this technique may also be applicable to a wide range of other conditions associated with chronic bronchial inflammation, as well as of potential value for monitoring response to therapy in these same populations

Chest radiograph interpretation requires an understanding of the mediastinal reflections and anatomical structures. Computed tomography (CT) improves the learning of three-dimensional (3D) anatomy, and more recently multidetector CT (MDCT) technology has enabled the creation of high-quality reformations in varying projections. Multiplanar reformations (MPRs) of varying thickness in the coronal and sagittal projections can be created for direct correlation with findings on frontal and lateral chest radiographs, respectively. MPRs enable simultaneous visualization of the craniocaudal extent of thoracic structures while providing the anatomic detail that has been previously illustrated using cadaveric specimens. Emphasis will be placed on improving knowledge of mediastinal anatomy and reflections including edges, lines, and stripes that are visible on chest radiographs

With the increasing use of MDCT, more solitary pulmonary nodules are being detected. Although the majority of these lesions are benign, lung cancer constitutes an important consideration in the differential diagnosis of solitary pulmonary nodules. The goal of management is to correctly differentiate malignant from benign nodules to ensure appropriate treatment. Stratifying patients' risk factors for malignancy, including patient age, smoking history, and history of malignancy, is essential in the management of solitary pulmonary nodules. In terms of radiologic evaluation, obtaining prior films is important to assess for nodule growth. The detection of certain patterns of calcification and stability for 2 years or more have historically been the only useful findings for determining whether a nodule is or is not benign. However, recent technological advances in imaging, including MDCT and PET/CT, have improved nodule characterization and surveillance. For solid nodules, CT enhancement of less than 15 HU and hypometabolism on PET (SUVmax <2.5) favor a benign etiology. Potential pitfalls in nodule enhancement and PET evaluation of solitary pulmonary nodules include infectious and inflammatory conditions. Stratified according to patient risk factors for malignancy and nodule size, recent guidelines for the management of incidentally detected small pulmonary nodules have been useful in decision analysis. An important exception to these guidelines is the evaluation and management of the subsolid nodule. These lesions are not suitable for CT enhancement studies and may show low metabolic activity on PET imaging. Due to their association with bronchioloalveolar carcinoma and adenocarcinoma, subsolid nodules require a more aggressive approach in terms of reassessing serial imaging and/or obtaining tissue diagnosis. As data from the low-dose CT lung cancer screening trials are analyzed and further studies with new imaging techniques are performed, management strategies for the imaging evaluation of the solitary pulmonary nodule will continue to evolve

With the increasing use of multidetector CT, small nodules are being detected more often. Although most incidentally discovered nodules are benign, usually the sequelae of pulmonary infection and malignancy, either primary or secondary, remains an important consideration in the differential diagnosis of solitary pulmonary nodules. This article reviews the role of imaging in the detection and characterization of solitary pulmonary nodules. Strategies for evaluating and managing solitary pulmonary nodules are also discussed