Neuromorphic X-ray Computed Tomography

X-ray computed tomography (CT) reconstruction is inher-ently an ill-posed inverse problem, particularly with sparse-view pro-jections. Unlike traditional frame-based sensors that acquire projectionsat fixed intervals, neuromorphic event cameras operate asynchronouslyat the pixel level and trigger events only when the log-intensity changeexceeds a threshold. This asynchronous sensing naturally captures high-frequency angular variations during object rotation, providing comple-mentary information between sparsely sampled projections. However,the application of event cameras to CT reconstruction remains largelyunexplored. To bridge this gap, we introduce a Neuromorphic X-rayCT framework. Specifically, we propose an Event-enhanced Neural At-tenuation Field (ENAF) that models the volumetric attenuation fieldby jointly leveraging sparse projections and event streams. To supportand validate this framework, we construct synthetic neuromorphic CTdatasets and acquire a real-world dataset. Experiments on both syn-thetic and real-world datasets demonstrate that ENAF consistently out-performs state-of-the-art frame-only methods while maintaining train-ing efficiency. To the best of our knowledge, this work presents thefirst application of neuromorphic sensing to X-ray CT and establishesthe feasibility of event-enhanced sparse-view CT reconstruction. Thecode and datasets are available at https://wanghongjian98.github.io/projects/neuroxct/.