Quantification of Lung PET Images: Challenges and Opportunities.
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Quantification of Lung PET Images: Challenges and Opportunities.
J Nucl Med. 2017 Jan 12;:
Authors: Chen DL, Cheriyan J, Chilvers E, Choudoury G, Coello C, Connell M, Fisk M, Groves AM, Gunn RN, Holman BF, Hutton BF, Lee S, MacNee W, Mohan D, Parr D, Subramanian D, Tal-Singer R, Thielemans K, van Beek EJ, Vass L, Wellen JW, Wilkinson I, Wilson FJ
Abstract
Millions of people are affected by respiratory diseases, leading to a significant health burden globally. Due to the current insufficient knowledge of the underlying mechanisms that lead to the development and progression of respiratory diseases, treatment options remain limited. To overcome this limitation and understand the associated molecular changes, non-invasive imaging techniques such as positron emission tomography (PET) and single photon emission computed tomography have been explored for biomarker development, with (18)F-fluorodeoxyglucose ((18)F-FDG) PET imaging being the most studied. The quantification of pulmonary molecular imaging data remains challenging due to variations in tissue, air, blood and water fractions within the lungs. The proportions of these components further differ depending on the lung disease. Therefore, different quantification approaches have been proposed to address these variabilities. However, no standardized approach has been developed to date. This article reviews the data evaluating (18)F-FDG PET quantification approaches in lung diseases, focusing on methods to account for variations in lung components and the interpretation of the derived parameters. The diseases reviewed include acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD) and interstitial lung disease such as idiopathic pulmonary fibrosis (IPF). Based on review of prior literature, ongoing research and discussions amongst the authors, suggested considerations are presented to assist with the interpretation of the derived parameters from these approaches and the design of future studies.
PMID: 28082432 [PubMed - as supplied by publisher]