Background: Four-dimensional computed tomography (4DCT) provides an important physiological information for diagnosis and treatment. On the other hand, its acquisition could be challenged by artifacts due to motion sorting/binning, time and effort bandwidth in image quality QA, and dose considerations. A 4D synthesis development would significantly augment the available data, addressing quality and consistency issues. Furthermore, the high-quality synthesis can serve as an essential backbone to establish a feasible physiological manifold to support online reconstruction, registration, and downstream analysis from real-time x-ray imaging.
Purpose: Our study aims to synthesize continuous 4D respiratory motion from two extreme respiration phases.
Methods: A conditional image registration network is trained to take the end-inhalation (EI) and end-exhalation (EE) as input, and output arbitrary breathing phases by varying the conditional variable. A volume compensation and calibration post-processing is further introduced to improve intensity synthesis accuracy. The method was tested on 20 4DCT scans with a four-fold cross-testing scheme and compared against two linear scaling methods and an image translation network.
Results: Our method generated realistic 4D respiratory motion fields that were spatiotemporally smooth, achieving a root-mean-square error of (70.1 ± 33.0) HU and structural similarity index of (0.926 ± 0.044), compared to the ground-truth 4DCT. A 10-phase synthesis takes about 2.85 s.
Conclusions: We have presented a novel paradigm to synthesize continuous 4D respiratory motion from end-inhale and end-exhale image pair. By varying the conditional variable, the network can generate the motion field for an arbitrary intermediate breathing phase with precise control.
Keywords: deep learning; image registration; image synthesis.
© 2023 American Association of Physicists in Medicine.