Enabling self-supervised learned primal dual with Noise2Inverse

• URL: http://arxiv.org/abs/2606.26991v1
• Date: Thu, 25 Jun 2026 13:07:15 GMT
• Title: Enabling self-supervised learned primal dual with Noise2Inverse
• Abstract summary: We propose a self-supervised reconstruction method by extending the Noise2Inverse framework to the Learned Primal-Dual algorithm.<n>The resulting approach, called Noise2Inverse Learned Primal-Dual (N2I-LPD), enables training of a learned iterative reconstruction operator without ground-truth images.
• Score: 2.32391394124633
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: X-ray computed tomography reconstruction is an ill-posed inverse problem, particularly in low-dose and sparse-angle settings where measurements are noisy and incomplete. While learned reconstruction methods such as the Learned Primal-Dual algorithm achieve strong performance, they typically rely on supervised training with access to ground-truth data, which is often unavailable in practice. In this work, we propose a self-supervised reconstruction method by extending the Noise2Inverse framework to the Learned Primal-Dual algorithm. The resulting approach, called Noise2Inverse Learned Primal-Dual (N2I-LPD), enables training of a learned iterative reconstruction operator without ground-truth images by exploiting the statistical independence of noise in distinct measurements with respect to angular rotation of the CT-scan. We compare the proposed method with classical reconstruction methods, as well as neural network-based approaches such as a U-Net trained within the same N2I framework. The results demonstrate that N2I-LPD achieves improved reconstruction quality, highlighting the potential of combining learned reconstruction operators with self-supervised training strategies for practical CT imaging scenarios where ground-truth data is unavailable.

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