Bronchial wall measurements differ between patients who have COPD with CB and those who have COPD without CB. The correlation between airway dimensions and indexes of airway obstruction in patients with COPD and CB indicates that the bronchial tree is the site of anatomic-functional alterations in this patient group.

Diffusion-weighted (Dw) imaging has for a number of years been a diagnostic tool in the field of neuroradiology, yet only since the end of the 1990s, with the introduction of echoplanar imaging (EPI) and the use of sequences capable of performing diffusion studies during a single breath hold, has it found diagnostic applications at the level of the abdomen. The inherent sensitivity to motion and the magnetic susceptibility of Dw sequences nonetheless still create problems in the study of the abdomen due to artefacts caused by the heartbeat and intestinal peristalsis, as well as the presence of various parenchymal-gas interfaces. With regard to focal liver lesions, a review of the literature reveals that Dw imaging is able to differentiate lesions with high water content (cysts and angiomas) from solid lesions. With regard to the latter, although there are differences between benign forms [focal nodular hyperplasia (FNH), adenoma] and malignant forms [metastasis, hepatocellular carcinoma (HCC)] in their apparent diffusion coefficient (ADC) in the average values for histological type, there is a significant overlap in values when lesions are assessed individually, with the consequent problem of their correct identification. One promising aspect is the possibility of quantifying the degree of fibrosis in patients with chronic liver disease and cirrhosis given that the deposit of collagen fibres "restricts" the motion of water molecules and therefore reduces ADC values. However, even in this field, studies can only be considered preliminary and far from real clinical applications. The retroperitoneum is less affected by motion artefacts and similarly deserves the attention of Dw imaging. Here it is possible to differentiate mucin-producing tumours of the pancreas from pseudocystic forms on the basis of ADC values even though the limited spatial resolution of Dw imaging does not enable the identification of small lesions. Dw imaging may be applied to the study of the kidney to differentiate hydronephrosis from pyonephrosis and with regard to tumours, solid from pseudocystic forms. In addition, given that renal parenchyma has significantly variable ADC values on the basis of the anatomic section and physiological conditions, the possibility of assessing functional alterations is currently being studied. Indeed, a good correlation has been found between ADC values and glomerular filtration rate. With regard to musculoskeletal applications, the absence of motion artefacts in the regions studied has enabled the development of sequences less sensitive to magnetic susceptibility and with greater spatial resolution than EPI. Attempts have therefore been made to use Dw imaging in the characterization of soft-tissue tumours although the findings so far have been disputed. Greater agreement has been found regarding sensitivity of the technique in assessing response of these tumours to chemotherapy: tumour necrosis is thought to increase ADC whereas the persistence of vital neoplastic tissue tends to lower it. One of the most pr...

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Abstract-Computed tomography (CT) is the most sensitive imaging technique for detecting lung nodules, and is now being evaluated as a screening tool for lung cancer in several large samples studies all over the world. In this report, we describe a semiautomatic method for 3-D segmentation of lung nodules in CT images for subsequent volume assessment. The distinguishing features of our algorithm are the following. 1) The user interaction process. It allows the introduction of the knowledge of the expert in a simple and reproducible manner. 2) The adoption of the geodesic distance in a multithreshold image representation. It allows the definition of a fusion-segregation process based on both gray-level similarity and objects shape. The algorithm was validated on low-dose CT scans of small nodule phantoms (mean diameter 5.3-11 mm) and in vivo lung nodules (mean diameter 5-9.8 mm) detected in the Italung-CT screening program for lung cancer. A further test on small lung nodules of Lung Image Database Consortium (LIDC) first data set was also performed. We observed a RMS error less than 6.6% in phantoms, and the correct outlining of the nodule contour was obtained in 82/95 lung nodules of Italung-CT and in 10/12 lung nodules of LIDC first data set. The achieved results support the use of the proposed algorithm for volume measurements of lung nodules examined with low-dose CT scanning technique.Index Terms-Computer-aided diagnosis, computer vision, lung cancer, lung nodules segmentation, medical imaging, multiscale processing, spiral computed tomography (CT).

The paper describes a neural-network-based system for the computer aided detection of lung nodules in chest radiograms. Our approach is based on multiscale processing and artificial neural networks (ANNs). The problem of nodule detection is faced by using a two-stage architecture including: 1) an attention focusing subsystem that processes whole radiographs to locate possible nodular regions ensuring high sensitivity; 2) a validation subsystem that processes regions of interest to evaluate the likelihood of the presence of a nodule, so as to reduce false alarms and increase detection specificity. Biologically inspired filters (both LoG and Gabor kernels) are used to enhance salient image features. ANNs of the feedforward type are employed, which allow an efficient use of a priori knowledge about the shape of nodules, and the background structure. The images from the public JSRT database, including 247 radiograms, were used to build and test the system. We performed a further test by using a second private database with 65 radiograms collected and annotated at the Radiology Department of the University of Florence. Both data sets include nodule and nonnodule radiographs. The use of a public data set along with independent testing with a different image set makes the comparison with other systems easier and allows a deeper understanding of system behavior. Experimental results are described by ROC/FROC analysis. For the JSRT database, we observed that by varying sensitivity from 60 to 75% the number of false alarms per image lies in the range 4-10, while accuracy is in the range 95.7-98.0%. When the second data set was used comparable results were obtained. The observed system performances support the undertaking of system validation in clinical settings.