  Dynamic cone-beam computed tomography (CBCT) can capture
high-spatial-resolution, time-varying images for motion monitoring, patient
setup, and adaptive planning of radiotherapy. However, dynamic CBCT
reconstruction is an extremely ill-posed spatiotemporal inverse problem, as
each CBCT volume in the dynamic sequence is only captured by one or a few X-ray
projections. We developed a machine learning-based technique, prior-model-free
spatiotemporal implicit neural representation (PMF-STINR), to reconstruct
dynamic CBCTs from sequentially acquired X-ray projections. PMF-STINR employs a
joint image reconstruction and registration approach to address the
under-sampling challenge. Specifically, PMF-STINR uses spatial implicit neural
representation to reconstruct a reference CBCT volume, and it applies temporal
INR to represent the intra-scan dynamic motion with respect to the reference
CBCT to yield dynamic CBCTs. PMF-STINR couples the temporal INR with a
learning-based B-spline motion model to capture time-varying deformable motion
during the reconstruction. Compared with previous methods, the spatial INR, the
temporal INR, and the B-spline model of PMF-STINR are all learned on the fly
during reconstruction in a one-shot fashion, without using any patient-specific
prior knowledge or motion sorting/binning. PMF-STINR was evaluated via digital
phantom simulations, physical phantom measurements, and a multi-institutional
patient dataset featuring various imaging protocols (half-fan/full-fan, full
sampling/sparse sampling, different energy and mAs settings, etc.). The results
showed that the one-shot learning-based PMF-STINR can accurately and robustly
reconstruct dynamic CBCTs and capture highly irregular motion with high
temporal (~0.1s) resolution and sub-millimeter accuracy. It can be a promising
tool for motion management by offering richer motion information than
traditional 4D-CBCTs.
