mlsanityData to 13 Oct 2026

A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction

arXiv 2205.00463 ↗First posted 1 May 2022eess.IV cs.CV cs.NAcited by 1

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Abstract (arXiv)

Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation. In recent years, supervised deep learning (DL) has been extensively studied for LDCT image reconstruction, which trains a network over a dataset containing many pairs of normal-dose and low-dose images. However, the challenge on collecting many such pairs in the clinical setup limits the application of such supervised-learning-based methods for LDCT image reconstruction in practice. Aiming at addressing the challenges raised by the collection of training dataset, this paper proposed a unsupervised deep learning method for LDCT image reconstruction, which does not require any external training data. The proposed method is built on a re-parametrization technique for Bayesian inference via deep network with random weights, combined with additional total variational~(TV) regularization. The experiments show that the proposed method noticeably outperforms existing dataset-free image reconstruction methods on the test data.

Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 15 of its 49 references that are in the map. First posted on arXiv 1 May 2022.

Builds on

15 of 49 references.

References · 15

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3 more
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