| Respiratory diseases including chronic obstructive pulmonary disease (COPD), asthma, and bronchitis, collectively categorized as obstructive lung diseases (OLD), pose an immense burden on the health care community. Currently, OLD is among only a handful of afflictions whose rates of mortality and morbidity have increased or remained constant, even with innovations in drug treatment and intervention. This may be due to current disease assessment which lack specificity of the mechanisms causing the regional obstruction within the lung. With the latest advancements in multislice detector computed tomography (MDCT), quantitative lung CT measurements have demonstrated potential for accurate characterization of regional lung disease, which may lead to better interventional procedures to treat OLD before lung function is severely compromised. Innovations in reconstruction algorithms and CT scanner design provide a myriad of imaging parameters which could provide better accuracy of quantitative CT (qCT) in the lung. Density and airway measures are emerging biomarkers of OLD progression and severity used in ongoing clinical trials. The purpose of this work is to improve qCT measures, including densitometry and airway measures, while reducing overall dose to the patient using commercially available clinical acquisition/reconstruction parameters. For airway measures, this includes strategies to improve spatial resolution using higher resolution kernels, dose mitigation via iterative reconstruction techniques, and reduced display field of view (DFOV) to improve sampling resolution. For density measures, automatic exposure control techniques are used to obtain consistent noise performance to guide low CT dose protocol design for lung imaging studies across vendor platforms. |
