Editorial: Functional and quantitative imaging of the lung

Pulmonary functional imaging has been providing important functional and quantitative metrics in a wide range of pathological conditions. Functional lung imaging research is based on two main paradigms: 1. In the lungs, structural alterations cause inevitable loss in lung function, ultimately leading to perfusion and ventilation abnormalities and subsequent reduction of blood oxygenation. Several different structural compartments can be affected in various combinations, that are airways, conducting pulmonary arterial and bronchial arterial as well as venous vessels, capillaries, and the tissue barrier allowing diffusion of oxygen from the alveoli to the red blood cells. 2. Traditional pulmonary function testing such as spirometry and full body plethymography is insensitive to early lung disease since healthy areas may compensate for inhomogeneously distributed function loss. Also, spirometry is a global total lung and airways assessment, which cannot differentiate a regional pattern of tissue destruction, Mummy and colleagues explored the impact of anthropomorphic details such as age, sex and body mass index on MRI-derived measures of pulmonary gas exchange with hyperpolarized 129 Xe-MRI (HYPERLINK). Since this technique holds great potential to regionally assess impairments in the air-to-blood diffusion barrier, such work is of great importance for implementing this advanced technique in clinical routine at specialized centers (5). Doellinger and colleagues used a completely contrast agent-free approach to assess pulmonary ventilation and perfusion solely from time-resolved acquisitions in combination with registration and a matric-pencil decomposition, which separates cyclic changes of MRI signal intensity into contributions of pulsatile blood inflow and respiration (HYPERLINK) (6). The authors could demonstrate a good correlation with the more widely established contrast-enhanced 4D perfusion technique and score, opening a perspective for contrast-free assessments of cystic fibrosis lung disease.Ringwald and colleagues used a large MRI dataset incl. 4D perfusion data to train a convolutional neural network to segment the lungs in children with cystic fibrosis. This is one step in the effort to make MRI evaluation less user-dependent (INSERT HYPERLINK). Triphan and colleagues used another approach by directly quantifying the T1-relaxation times in an echo-time dependent manner in COPD patients (HYPERLINK). Apparently, shorter T1 at ultra-short echo times is more correlated with tissue abnormalities, whereas at longer echo times it is more correlated with perfusion (7). Thus, this technique allows for a sub-resolution assessment of tissue composition.