AI Article Synopsis
- Low dose computed tomography (LDCT) often suffers from poor image quality due to mottle noise and streak artifacts, making diagnostics challenging.
- While iterative reconstruction (IR) algorithms improve image quality, their high computational costs are a significant drawback.
- The paper introduces a deep iterative reconstruction estimation (DIRE) method using a 3D residual convolutional network (3D ResNet) that effectively enhances LDCT images, validated through tests on both simulated and real datasets.
Article Abstract
The image quality in low dose computed tomography (LDCT) can be severely degraded by amplified mottle noise and streak artifacts. Although the iterative reconstruction (IR) algorithms bring sound improvements, their high computation cost remains a major inconvenient. In this work, a deep iterative reconstruction estimation (DIRE) strategy is developed to estimate IR images from LDCT analytic reconstructions images. Within this DIRE strategy, a 3D residual convolutional network (3D ResNet) architecture is proposed. Experiments on several simulated and real datasets as well as comparisons with state-of-the-art methods demonstrate that the proposed approach is effective in providing improved LDCT images.
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| http://dx.doi.org/10.1088/1361-6560/ab18db | DOI Listing |
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