A Real-Time Neural Representation via Algorithm-Hardware Synergy for Sparse-View CT Reconstruction

Sparse-view computed tomography (SVCT) is an advancement in computed tomography (CT) technology that aims to reduce the radiation dose during imaging. Reconstructing high-quality images from sparse-view (SV) projections is an ill-posed inverse problem. Recently, implicit neural representations (INRs) as a self-supervised paradigm for solving underdetermined inverse problems have demonstrated excellent performance in SVCT reconstruction. However, since INR-based approaches rely on subject-specific training, they require a significant investment of time to optimize from scratch. Consequently, previous INR methods have not been able to meet the requisite timeliness of reconstruction. In our work, we propose RTSyner, an algorithm-hardware collaboration framework that facilitates the real-time efficiency of CT reconstruction. On the algorithmic side, we introduce an efficient coordinate-based feature module that exploits the local latent features as a positional external condition, leveraging the limited structural information of corrupted images derived from the sensory domain. By fusing latent features and coordinate information, the model learns a neural representation of the final tomographic image. On the hardware side, we design a dedicated hardware architecture with a customized algorithm flow to improve reconstruction speed and reduce power consumption. Furthermore, we improve the efficiency of model inference through model quantization, which also facilitates the subsequent deployment of hardware. Our extensive experimental results demonstrate that the RTSyner based on neural representation has achieved real-time SVCT reconstruction through the synergistic acceleration of the algorithm and hardware. We further explore its application potential via volume reconstructions under more complex acquisition geometries.