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Multi–Detector Row CT and Postprocessing Techniques in the Assessment of Diffuse Lung Disease¹

¹ From the Department of Radiology, Pitié-Salpêtrière Hospital, 47/83 Boulevard de l’Hôpital, 75013 Paris, France. Recipient of a Cum Laude award for an education exhibit at the 2004 RSNA Annual Meeting. Received March 4, 2005; revision requested March 29 and received May 16; accepted May 16. All authors have no financial relationships to disclose. Address correspondence to C.B.A. (e-mail: catherine.beigelman{at}psl.ap-hop-paris.fr).

Many acute and chronic lung diseases are characterized by diffuse infiltration of the lung parenchyma. High-resolution computed tomography (CT) has been widely accepted as the imaging standard of reference for the assessment of these diseases. However, only approximately 10% of the lung parenchyma is scanned with high-resolution CT, and characteristic foci of disease may be missed. With use of the established characteristic high-resolution CT patterns, multi–detector row chest CT has revolutionized the evaluation of diffuse lung disease. Multi–detector row CT generates isotropic volumetric high-resolution data, allowing contiguous three-dimensional (3D) visualization of the lung parenchyma, with the capacity to create high-quality two-dimensional (2D) and 3D reformatted images. Minimum intensity projection is the postprocessing technique of choice for the detection and characterization of most patterns of diffuse lung disease. Maximum intensity projection (MIP) allows the detection and characterization of micronodules; the recognition of enlarged pulmonary veins, which is extremely useful in the diagnosis of pulmonary edema and the assessment of mosaic perfusion; and differentiation between perilymphatic, miliary, and centrilobular distribution. MIP can also help differentiate between constrictive bronchiolitis and mixed emphysema. Two-dimensional reformatted images are now of equal importance with the 2D axial images in diagnosing specific diffuse lung diseases. In the future, 3D reformatted images may be used to help quantify these disorders.

© RSNA, 2005