Faculty Bibliography

The educational objectives for this self-assessment module are for the participant to exercise, self-assess, and improve his or her understanding of the imaging evaluation of the solitary pulmonary nodule

A pulmonary fissure is a boundary between the lobes in the lungs. Its segmentation is of clinical interest as it facilitates the assessment of lung disease on a lobar level. This paper describes a new approach for segmenting the major fissures in both lungs on thin-section computed tomography (CT). An image transformation called 'ridge map' is proposed for enhancing the appearance of fissures on CT. A curve-growing process, modeled by a Bayesian network, is described that is influenced by both the features of the ridge map and prior knowledge of the shape of the fissure. The process is implemented in an adaptive regularization framework that balances these influences and reflects the causal dependencies in the Bayesian network using an entropy measure. The method effectively alleviates the problem of inappropriate weights of regularization terms, an effect that can occur with static regularization methods. The method was applied to segment and visualize the lobes of the lungs on chest CT of 10 patients with pulmonary nodules. Only 78 out of 3286 left or right lung regions with fissures (2.4%) required manual correction. The average distance between the automatically segmented and the manually delineated 'ground-truth' fissures was 1.01mm, which was similar to the average distance of 1.03mm between two sets of manually segmented fissures. The method has a linear-time worst-case complexity and segments the upper lung from the lower lung on a standard computer in less than 5min

PURPOSE: To identify, by using a chest phantom, whether vessels that contact lung nodules measuring less than 5 mm in diameter will affect nodule volume assessment. MATERIALS AND METHODS: Forty synthetic nodules (20 with ground-glass attenuation and 20 with solid attenuation) that measured less than 5 mm in diameter were placed into a chest phantom either adjacent to (n = 30) or isolated from (n = 10) synthetic vessels. Nodules were imaged by using low-dose (20 mAs) and diagnostic (120 mAs) multi-detector row computed tomography (CT). Nodules that were known to lie in direct contact with vessels were confirmed by visual inspection. Nontargeted 1.25 x 1.00-mm sections were analyzed with a three-dimensional computer-assisted method for measuring nodule volume. A mixed-model analysis of variance was used to examine the influence of several factors (eg, the presence of adjacent vessels; tube current-time product; and nodule attenuation, diameter, and location) on measurement error. RESULTS: The mean absolute error (MAE) for all nodules adjacent to vessels was 2.3 mm(3), which was higher than the MAE for isolated nodules (1.9 mm(3)) (P < .001). This difference proved significant only for diagnostic CT (2.2 mm(3) for nodules adjacent to vessels vs 1.3 mm(3) for nodules isolated from vessels) (P < .05). A larger MAE was noted for nodules with ground-glass attenuation (2.3 mm(3)) versus those with solid attenuation (2.0 mm(3)), for increasing nodule volume (1.66 mm(3) for nodules smaller than 20 mm(3) vs 2.83 mm(3) for nodules larger than 40 mm(3)), and for posterior nodule location (P < .05). CONCLUSION: The presence of a vessel led to a small yet significant increase in volume error on diagnostic-quality images. This represents less than one-third of the overall error, even for nodules larger than 40 mm(3) or approximately 4 mm in diameter. This increase, however, may be more important for smaller nodules with errors of less than 3 mm(3)

Pulmonary embolism (PE) is a life-threatening disease, requiring rapid diagnosis and treatment. Contrast enhanced computed tomographic (CT) images of the lungs allow physicians to confirm or rule out PE, but the large number of images per study and the complexity of lung anatomy may cause some emboli to be overlooked. We evaluated a novel three-dimensional (3D) visualization technique for detecting PE, and compared it with traditional 2D axial interpretation. Three readers independently marked 10 cases using the 3D method, and a separate interpretation was performed at a later date using only source axial images. An experienced thoracic radiologist adjudicated all marks, classifying clots according to location and confidence. There were a total of 8 positive examinations with 69 validated emboli. 44 (64%) of the clots were segmental while 12 (17%) proved subsegmental. Using the traditional 2D method for examination, readers detected a mean of 45 PE for 66% sensitivity. Using the 3D method, readers detected a mean of 35 PE (50% sensitivity). Combining both methods, readers detected a mean of 51 PE (74% sensitivity), significantly higher than either single method (p < 0.001). Considered by arterial level, significant improvement was observed for detection of segmental and subsegmental clots (p < 0.001) when comparing combined reading with either single method. The mean number of false positives per patient was 0.23 for both 2D and 3D readings and 0.4 for combined reading. 3D visualization of pulmonary arteries allowed readers to detect a significant number of additional emboli not detected during 2D axial interpretations and thus may lead to a more accurate diagnosis of PE

PURPOSE: To evaluate the effect of two-dimensional wavelet-based computed tomographic (CT) image compression according to the Joint Photographic Experts Group (JPEG) 2000 standard on computer-assisted assessment of nodule volume. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the research board at the authors' institution; patients' informed consent was not required. Fifty-one nodules in 23 patients (seven men, 16 women; mean age, 59 years; age range, 39-75 years) were selected on low-dose CT scans that were compressed to levels of 10:1, 20:1, 30:1, and 40:1 by using a two-dimensional JPEG 2000 wavelet-based image compression method. Nodules were classified according to size (< or = 5 mm or > 5 mm in diameter), location (central, peripheral, or abutting pleura or fissures), and attenuation (solid, calcified, or subsolid). Regions of interest were placed on the original images and transposed onto compressed images. Nodule volumes on original (noncompressed) and compressed images were measured by using a computer-assisted method. A mixed-model analysis of variance was conducted for statistical evaluation. RESULTS: Nodule volumes averaged 388.1 mm3 (range, 34-3474 mm3). There were three calcified, 33 solid noncalcified, and 15 subsolid nodules (13 with ground-glass attenuation). Average volume decreased with increasing compression level, to 383 mm3 (10:1), 370 mm3 (20:1), 360 mm3 (30:1), and 354 mm3 (40:1). No significant difference was identified between measurements obtained on original images and those compressed to a level of 10:1. Significant differences were noted, however, between original images and those compressed to a level of 20:1 or greater (P < .05). Compression level significantly interacted with nodule size, location, and attenuation (P < .001). The effect of compression was greater for nodules with ground-glass attenuation than for those with higher attenuation values. The difference in mean volumes between original images and those compressed to a level of 20:1 was 34.9 mm3 for nodules with ground-glass attenuation, compared with 8.3 mm3 for higher-attenuation nodules, a 4.2-fold difference. CONCLUSION: Nodule volumes measured on images compressed to a level of 20:1 differed significantly from those measured on noncompressed images, especially for nodules with ground-glass attenuation. This difference could affect the assessment of nodule change in size as measured with computer-assisted methods

The influence of MSCT on nodule detection and characterization will be discussed. The objective is to improve understanding of the clinical issues involved in nodule detection, characterization, and management in light of technological advances. Topics to be covered are noninvasive characterization techniques, such as morphologic and density inspection on CT, nodule enhancement techniques, CT-PET, temporal nodule size assessment, and computer aided diagnosis for both detection and characterization

This paper presents a shape-based curve-growing algorithm for object recognition in the field of medical imaging. The proposed curve growing process, modeled by a Bayesian network, is influenced by both image data and prior knowledge of the shape of the curve. A maximum a posteriori (MAP) solution is derived using an energy-minimizing mechanism. It is implemented in an adaptive regularization framework that balances the influence of image data and shape prior in estimating the curve, and reflects the causal dependencies in the Bayesian network. The method effectively alleviates over-smoothing, an effect that can occur with other regularization methods. Moreover, the proposed framework also addresses initialization and local minima problems. Robustness and performance of the proposed method are demonstrated by segmentation of pulmonary fissures in computed tomography (CT) images

Several segmentation methods to evaluate growth of small isolated pulmonary nodules on chest computed tomography (CT) are presented. The segmentation methods are based on adaptively thresholding attenuation levels and use measures of nodule shape. The segmentation methods were first tested on a realistic chest phantom to evaluate their performance with respect to specific nodule characteristics. The segmentation methods were also tested on sequential CT scans of patients. The methods' estimation of nodule growth were compared to the volume change calculated by a chest radiologist. The best method segmented nodules on average 43% smaller or larger than the actual nodule when errors were computed across all nodule variations on the phantom. Some methods achieved smaller errors when examined with respect to certain nodule properties. In particular, on the phantom individual methods segmented solid nodules to within 23% of their actual size and nodules with 60.7 mm3 volumes to within 14%. On the clinical data, none of the methods examined showed a statistically significant difference in growth estimation from the radiologist