الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
The pulmonary nodule is a common finding at computed tomography (CT) that may be due to primary or secondary malignancy, benign conditions, including granulomatous diseases and other infectious processes. The morphologic imaging diagnosis of a pulmonary nodule can be performed, with special attention to the nodule attenuation charteristics, edge, size, shape contrast enhancement, internal structure and to its relationship to surrounding structures.
With conventional computed tomography (CT) images of the thorax are typically obtained with 6-10-mm section thickness. The acquisition of thin-section images of the whole thorax is impractical, because it requires multiple breath-hold sets of contiguous spiral scans to cover the thorax completely.
Multi-detector CT, with its fast scanning speed and superb spatial resolution, allows us to routinely acquire thin-section images of the entire thorax in less than 10 seconds. This improvement in spatial and temporal resolution increases the sensitivity for detection of small pulmonary nodules. Furthermore, in multi-detector CT, during single CT gantry rotation It is available to generate CT images of different section thicknesses.
With MDCT systems, thin section images are obtained, furthermore with thin section reconstructions, volumetric assessment and characterization of pulmonary nodules is done.
The advantages of MDCT include both improved nodule detection and nodule characterization because the entire lung can be scanned with thin slice in a single breath-hold without an intersection gap.
In the evaluation of lung cancer, MDCT will allow improved detection of pleural dissemination and hilar lymph node adenopathy.
Recent advances in MDCT technology and post processing software have brought excellent post processing of axial images. Commonly performed post processing techniques include two dimensional images using multi planar reformation (MPR) and maximum intensity projection (MIP), or three dimensional images using various types of volume rendering (VR).
Reading should be performed on a workstation. To avoid reading of an excessive number of individual axial slices (500-600) image by image.
Detectability of image-processing techniques such as MIP and VR is higher than when viewing thin-section images alone.
Utilization of computer aided nodule detection in distinct clinical situations is recently available with computer-aided detection (CAD) software.
Several studies on sensitivity of different CAD systems for the detection of pulmonary nodules demonstrated an increased sensitivity of radiologists for the detection of pulmonary nodules in chest CT scans when using CAD.