Motivation: Quantification of DCE-MRI in the lungs is vulnerable to respiratory motion. In previous work, timepoints with residual motion were selected manually. Goal(s): To develop automatic methods for breathing detection and bolus characterization in lung DCE-MRI. Approach: We developed a breathing motion detection CNN and algorithms for bolus classification in lung DCE-MRI. Quantitative perfusion parameters were calculated with and without removing frames with breathing motion. Results: Breathing motion was robustly detected independent of contrast agent presence. Also, artefacts in PBF maps were reduced. Impact: The developed CNN for breathing detection in DCE-MRI allows the robust, automatic identification of breathing motion. After identification, breathing correction methods can be applied to the affected frames, which might improve the comparability of quantitative perfusion metrics.
Grolig et al. (Tue,) studied this question.