Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions
This work addresses a specific bottleneck in medical imaging for CT reconstruction, offering an incremental enhancement to neural field approaches.
The paper tackled the problem of limited-angle CT reconstruction, where neural field methods struggle, by introducing a novel loss term based on epipolar consistency conditions, resulting in qualitative and quantitative improvements over baseline methods.
Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques. While these approaches deliver state-of-the-art results in sparse-view CT reconstruction, they struggle in limited-angle settings, where input projections are captured over a restricted angle range. We present a novel loss term based on consistency conditions between corresponding epipolar lines in X-ray projection images, aimed at regularizing neural attenuation field optimization. By enforcing these consistency conditions, our approach, Epi-NAF, propagates supervision from input views within the limited-angle range to predicted projections over the full cone-beam CT range. This loss results in both qualitative and quantitative improvements in reconstruction compared to baseline methods.