Adaptive multi-resolution hash-encoding framework for INR-based dental CBCT reconstruction with truncated FOV

Implicit neural representation (INR), particularly in combination with hash encoding, has recently emerged as a promising approach for computed tomography (CT) image reconstruction. However, directly applying INR techniques to 3D dental cone-beam computed tomography (CBCT) with a truncated field of view (FOV) is challenging. During the training process, if the FOV does not fully encompass the patient's head, a discrepancy arises between the measured projections and the forward projections computed within the truncated domain. This mismatch leads the network to estimate attenuation values inaccurately, producing severe artifacts in the reconstructed images.

This study aims to develop a computationally efficient INR-based reconstruction framework that leverages multi-resolution hash encoding for 3D dental CBCT with a truncated FOV.

To mitigate truncation artifacts, we train the network over an expanded reconstruction domain that fully encompasses the patient's head. For computational efficiency, we adopt an adaptive training strategy that uses a multi-resolution grid: finer resolution levels and denser sampling inside the truncated FOV, and coarser resolution levels with sparser sampling outside. To maintain consistent input dimensionality of the network across spatially varying resolutions, we introduce an adaptive hash encoder that selectively activates the lower-level features of the hash hierarchy for points outside the truncated FOV.

The proposed method with an extended FOV effectively mitigates truncation artifacts. Compared with a naive domain extension, using fixed resolution levels and a fixed sampling rate, the adaptive strategy reduces computational time by 60% for an image volume of $800\times 800\times 600$ , while preserving the peak signal-to-noise ratio (PSNR) within the truncated FOV.

We propose an INR-based reconstruction framework for 3D dental CBCT with a truncated FOV, effectively reducing truncation artifacts and training cost.