Evidence map›Paper›PMID 37571834›Full record

ArticleJournal of applied clinical medical physics2023

Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising.

Zhenchuan Wang, Minghui Liu, Xuan Cheng, Jinqi Zhu, Xiaomin Wang, Haigang Gong, Ming Liu, Lifeng Xu

Abstract read
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In one paragraph

Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Zhenchuan WangYangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Minghui LiuYangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Xuan ChengYangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Jinqi ZhuTianjin Normal University, Tianjin, China.
Xiaomin WangYangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Haigang GongYangtze Delta Region Institute(Quzhou), University of Electronic Science and Technology of China, Quzhou, China.
Ming LiuThe Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.
Lifeng XuThe Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.

Funding

Interdisciplinary Crossing and Integration of Medicine and Engineering for Talent Training Fund, West China Hospital, Sichuan University HXDZ22010Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China ZYGX2021YGLH213Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China ZYGX2022YGRH016Zhejiang Provincial Natural Science Foundation of China LGF22G010009
6 · The paper itself

Abstract

backgroundDeep learning has been successfully applied to low-dose CT (LDCT) denoising. But the training of the model is very dependent on an appropriate loss function. Existing denoising models often use per-pixel loss, including mean abs error (MAE) and mean square error (MSE). This ignores the difference in denoising difficulty between different regions of the CT images and leads to the loss of large texture information in the generated image. PURPOSE: In this paper, we propose a new hybrid loss function that adapts to the noise in different regions of CT images to balance the denoising difficulty and preserve texture details, thus acquiring CT images with high-quality diagnostic value using LDCT images, providing strong support for condition diagnosis.

methodsWe propose a hybrid loss function consisting of weighted patch loss (WPLoss) and high-frequency information loss (HFLoss). To enhance the model's denoising ability of the local areas which are difficult to denoise, we improve the MAE to obtain WPLoss. After the generated image and the target image are divided into several patches, the loss weight of each patch is adaptively and dynamically adjusted according to its loss ratio. In addition, considering that texture details are contained in the high-frequency information of the image, we use HFLoss to calculate the difference between CT images in the high-frequency information part.

resultsOur hybrid loss function improves the denoising performance of several models in the experiment, and obtains a higher peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Moreover, through visual inspection of the generated results of the comparison experiment, the proposed hybrid function can effectively suppress noise and retain image details.

conclusionsWe propose a hybrid loss function for LDCT image denoising, which has good interpretation properties and can improve the denoising performance of existing models. And the validation results of multiple models using different datasets show that it has good generalization ability. By using this loss function, high-quality CT images with low radiation are achieved, which can avoid the hazards caused by radiation and ensure the disease diagnosis for patients.

Indexed as

Image Processing, Computer-AssistedTomography, X-Ray ComputedAlgorithmsHumansSignal-To-Noise RatioCTdeep learningdenoisehybrid loss

Identifiers

PMID37571834
PMCPMC10476999

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.