CT respiratory motion synthesis using joint supervised and adversarial
  learning
Yi-Heng Cao Vincent Bourbonne François Lucia Ulrike Schick Julien Bert Vincent Jaouen Dimitris Visvikis
Objective: Four-dimensional computed tomography (4DCT) imaging consists in
reconstructing a CT acquisition into multiple phases to track internal organ
and tumor motion. It is commonly used in radiotherapy treatment planning to
establish planning target volumes. However, 4DCT increases protocol complexity,
may not align with patient breathing during treatment, and lead to higher
radiation delivery. Approach: In this study, we propose a deep synthesis method
to generate pseudo respiratory CT phases from static images for motion-aware
treatment planning. The model produces patient-specific deformation vector
fields (DVFs) by conditioning synthesis on external patient surface-based
estimation, mimicking respiratory monitoring devices. A key methodological
contribution is to encourage DVF realism through supervised DVF training while
using an adversarial term jointly not only on the warped image but also on the
magnitude of the DVF itself. This way, we avoid excessive smoothness typically
obtained through deep unsupervised learning, and encourage correlations with
the respiratory amplitude. Main results: Performance is evaluated using real
4DCT acquisitions with smaller tumor volumes than previously reported. Results
demonstrate for the first time that the generated pseudo-respiratory CT phases
can capture organ and tumor motion with similar accuracy to repeated 4DCT scans
of the same patient. Mean inter-scans tumor center-of-mass distances and Dice
similarity coefficients were $1.97$mm and $0.63$, respectively, for real 4DCT
phases and $2.35$mm and $0.71$ for synthetic phases, and compares favorably to
a state-of-the-art technique (RMSim).