  Diffusion Posterior Sampling (DPS) can be used in Computed Tomography (CT)
reconstruction by leveraging diffusion-based generative models for
unconditional image synthesis while matching the observations (data) of a CT
scan. Of particular interest is its application in scenarios involving sparse
or limited angular sampling, where conventional reconstruction algorithms are
often insufficient. We developed a DPS algorithm for 3D reconstruction from a
stationary CT (sCT) portable brain stroke imaging unit based on a multi-x-ray
source array (MXA) of 31 x-ray tubes and a curved area detector. In this
configuration, conventional reconstruction e.g., Penalized Weighted Least
Squares (PWLS) with a Huber edge-preserving penalty, suffers from severe
directional undersampling artifacts. The proposed DPS integrates a
two-dimensional diffusion model, acting on image slices, coupled to sCT data
consistency and volumetric regularization terms to enable 3D reconstruction
robust to noise and incomplete sampling. To reduce the computational burden of
DPS, stochastic contraction with PWLS initialization was used to decrease the
number of diffusion steps. The validation studies involved simulations of
anthropomorphic brain phantoms with synthetic bleeds and experimental data from
an sCT bench. In simulations, DPS achieved ~130% reduction of directional
artifacts compared to PWLS and 30% better recovery of lesion shape (DICE
coefficient). Benchtop studies demonstrated enhanced visualization of brain
features in a Kyoto Kagaku head phantom. The proposed DPS achieved improved
visualization of intracranial hemorrhage and brain morphology compared to
conventional model-based reconstruction for the highly undersampled sCT system.
