In Medical physics ; h5-index 59.0
BACKGROUND : Four-dimensional computed tomography (4DCT) provides 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 and end-exhalation 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 4-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. This article is protected by copyright. All rights reserved.
Sang Yudi, Ruan Dan
2023-Jan-18
deep learning, image registration, image synthesis
