Artificial intelligence in functional imaging of the lung

Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung performance, pushing the boundary in pulmonary functional imaging for patients suffering from respiratory conditions. In this review, we overview the current developments and expound on some of the encouraging new frontiers. We focus on the recent advances in machine learning and deep learning that enable reconstructing images, quantitating, and predicting functional responses of the lung. Finally, we shed light on the potential opportunities and challenges ahead in adopting AI for functional lung imaging in clinical settings.

Analysis of different image-registration algorithms for Fourier decomposition MRI in functional lung imaging

Background Motion correction is mandatory for the functional Fourier decomposition magnetic resonance imaging (FD-MRI) of the lungs. Therefore, it is important to evaluate the quality of various image-registration algorithms for pulmonary FD-MRI and to determine their impact on FD-MRI outcome. Purpose To evaluate different image-registration algorithms for FD-MRI in functional lung imaging. Material and Methods Fifteen healthy volunteers were examined in a 1.5-T whole-body MR scanner (Magnetom Avanto, Siemens AG) with a non-contrast enhanced 2D TrueFISP pulse sequence in coronal view and free-breathing (acquisition time 45 s, 250 images). Three image-registration algorithms were used to compensate the spatial variation of the lungs (fMRLung 3.0, ANTs, and Elastix). Quality control for image registration was performed by edge detection (ED), quotient image criterion (QI), and dice similarity coefficient (DSC). Ventilation, perfusion, and a ventilation/perfusion quotient (V/Q) were calculated using the three registered datasets. Results Average computing times for the three image-registration algorithms were 1.0 ± 1.6 min, 38.0 ± 13.5 min, and 354 ± 78 min for fMRLung, ANTs, and Elastix, respectively. No significant difference in the quality of motion correction provided by different image-registration algorithms occurred. Significant differences were observed between fMRLung- and Elastix-based perfusion values ​​of the left lung as well as fMRLung- and ANTs-based V/Q quotient of the right and the entire lung ( P < 0.05). Other ventilation and perfusion values were not significantly different. Conclusion The mandatory motion correction for functional FD-MRI of the lung can be achieved through different image-registration algorithms with consistent quality. However, a significantly difference in computing time between the image-registration algorithms still requires an optimization.