Development of quantitative computed tomography lung protocols

• Published in: Journal of thoracic imaging Vol. 28; no. 5; p. 266
• Main Authors: Newell, Jr, John D, Sieren, Jered, Hoffman, Eric A
• Format: Journal Article
• Language: English
• Published: United States 01.09.2013
• Subjects: Asthma - diagnostic imaging; Asthma - pathology; Clinical Protocols; Genomics; Humans; Lung Diseases - diagnostic imaging; Lung Diseases - pathology; Phantoms, Imaging; Pulmonary Disease, Chronic Obstructive - diagnostic imaging; Pulmonary Disease, Chronic Obstructive - pathology; Quality Control; Radiographic Image Interpretation, Computer-Assisted; Respiratory Function Tests; Software; Surveys and Questionnaires; Tomography, X-Ray Computed - methods
• ISSN: 1536-0237
• DOI: 10.1097/RTI.0b013e31829f6796

The purpose of this review article is to review the process of developing optimal computed tomography (CT) protocols for quantitative lung CT (QCT). In this review, we discuss the following important topics: QCT-derived metrics of lung disease; QCT scanning protocols; quality control; and QCT image processing software. We will briefly discuss several QCT-derived metrics of lung disease that have been developed for the assessment of emphysema, small airway disease, and large airway disease. The CT scanning protocol is one of the most important elements in a successful QCT. We will provide a detailed description of the current move toward optimizing the QCT protocol for the assessment of chronic obstructive pulmonary disorder and asthma. Quality control of CT images is also a very important part of the QCT process. We will discuss why it is necessary to use CT scanner test objects (phantoms) to provide frequent periodic checks on the CT scanner calibration to ensure precise and accurate CT numbers. We will discuss the use of QCT image processing software to segment the lung and extract the desired QCT metrics of lung disease. We will discuss the practical issues of using this software. The data obtained from the image processing software are then combined with those from other clinical examinations, health status questionnaires, pulmonary physiology, and genomics to increase our understanding of obstructive lung disease and improve our ability to design new therapies for these diseases.