  Objective: Dynamic cone-beam CT (CBCT) imaging is highly desired in
image-guided radiation therapy to provide volumetric images with high spatial
and temporal resolutions to enable applications including tumor motion
tracking/prediction and intra-delivery dose calculation/accumulation. However,
the dynamic CBCT reconstruction is a substantially challenging spatiotemporal
inverse problem, due to the extremely limited projection sample available for
each CBCT reconstruction (one projection for one CBCT volume). Approach: We
developed a simultaneous spatial and temporal implicit neural representation
(STINR) method for dynamic CBCT reconstruction. STINR mapped the unknown image
and the evolution of its motion into spatial and temporal multi-layer
perceptrons (MLPs), and iteratively optimized the neuron weighting of the MLPs
via acquired projections to represent the dynamic CBCT series. In addition to
the MLPs, we also introduced prior knowledge, in form of principal component
analysis (PCA)-based patient-specific motion models, to reduce the complexity
of the temporal INRs to address the ill-conditioned dynamic CBCT reconstruction
problem. We used the extended cardiac torso (XCAT) phantom to simulate
different lung motion/anatomy scenarios to evaluate STINR. The scenarios
contain motion variations including motion baseline shifts, motion
amplitude/frequency variations, and motion non-periodicity. The scenarios also
contain inter-scan anatomical variations including tumor shrinkage and tumor
position change. Main results: STINR shows consistently higher image
reconstruction and motion tracking accuracy than a traditional PCA-based method
and a polynomial-fitting based neural representation method. STINR tracks the
lung tumor to an averaged center-of-mass error of <2 mm, with corresponding
relative errors of reconstructed dynamic CBCTs <10%.
