Multi-source semi-stationary CT for brain imaging: development and assessment of a prototype system and image formation algorithms

Multi-source array (MXA) Computed Tomography systems pose challenges related to sampling and x-ray scatter. We present a semi-stationary head CT system and image formation pipeline including adaptive scatter estimation and image reconstruction based on learned diffusion models. The CT was evaluated on a robotic bench system including a miniaturized carbon-nanotube x-ray source and a curved-panel detector. Scatter correction was achieved with an Adaptive Deep Scatter Estimation (ADSE) method combining geometry-invariant projection-based scatter estimation with geometry-adaptive registration and scaling. Image reconstruction followed a Diffusion Posterior Sampling method (DPS-Recon) combining an unconditional diffusion model with measured data consistency. Image quality was assessed using anthropomorphic phantoms for a semi-stationary protocol involving a 21-source MXA rotated to three positions. ADSE resulted in 118% mean increase in feature contrast accuracy, 1.75 to 13-fold improvement in CNR for variable contrast features (-337HU to 885HU), and 3.56-fold improvement in CNR for variable size features (2mm to 12mm, 110HU) compared to uncorrected reconstructions. Non-uniformity reduced 50% for the three slices. DPS-Recon reduced limited sampling artifacts and improved visualization of soft-tissue structures, particularly in less densely sampled and bony anatomy locations, and further reduced non-uniformity by 20% in the superior brain location. We present first experimental results from a semi-stationary, multi-source CT utilizing CNT x-ray sources and curved-panel detector coupled to an imaging chain that addressed the main challenges inherent to the architecture. Metrics of CT number accuracy, image uniformity, and soft-tissue visualization showed promising performance for visualization of stroke radiological markers with the proposed approach.