ErisNet：用于CT图像降噪的深度学习模型。

IF 3.7 3区医学 Q2 ENGINEERING, BIOMEDICAL

Bioengineering Pub Date : 2025-09-19 DOI:10.3390/bioengineering12090997

Fabio Mattiussi, Francesco Magoga, Andrea Cozzi, Salvatore Ferraro, Gabrio Cadei, Chiara Martini, Svenja Leu, Ebticem Ben Khalifa, Alcide Alessandro Azzena, Marco Pileggi, Ermidio Rezzonico, Stefania Rizzo

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引用次数: 0

摘要

背景：ErisNet是一种新型的人工智能模型，用于降低计算机断层扫描图像中的噪声。方法：对23例尸体全身CT扫描进行ErisNet训练。我们用均方误差（MSE）、峰值信噪比（PSNR）、结构相似指数（SSIM）、视觉信息保真度（VIF）、边缘保持指数（EPI）和噪声方差（NV）来评估客观性能。我们评估了六位放射科医生的定性表现。为了支持视觉评估，我们在玻璃体、脑、肝和脾实质以及椎旁肌肉中放置了圆形感兴趣区域（ROI）。结果：ErisNet的MSE为64.07±46.81，PSNR为31.32±3.69 dB， SSIM为0.93±0.06，VIF为0.49±0.09，EPI为0.97±0.01，NV为64.69±46.80。ROI分析显示噪声降低：玻璃体HU的SD降低了8%（从17.6 HU降低到16.2 HU），脑实质HU降低了18%（从18.85 HU降低到15.40 HU），肝脏、脾脏和椎旁肌肉HU降低了15-19%。六位放射科医生通过评分（从1到5）来证实这些结果：整体质量4.5±0.6，噪声抑制/细节保存4.7±0.5，诊断置信度4.8±0.4 （p < 0.01）。结论：ErisNet提高了CT图像的质量，在处理低剂量扫描方面显示出强大的潜力。

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ErisNet: A Deep Learning Model for Noise Reduction in CT Images.

Background: ErisNet, a novel AI model to reduce noise in Computed Tomography images. Methods: We trained ErisNet on 23 post-mortem whole-body CT scans. We assessed the objective performance with mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) measure, visual information fidelity (VIF), edge preservation index (EPI) and noise variance (NV). We assessed the qualitative performance by six radiologists. To support the visual assessment, we placed circular regions of interest (ROI) in the vitreous body, brain, liver and spleen parenchyma and paravertebral muscle. Results: ErisNet achieved MSE 64.07 ± 46.81, PSNR 31.32 ± 3.69 dB, SSIM 0.93 ± 0.06, VIF 0.49 ± 0.09, EPI 0.97 ± 0.01 and NV 64.69 ± 46.80. The ROI analysis showed a reduction in noise: the SD of the HU decreased by 8% in the vitreous body (from 17.6 to 16.2 HU), by 18% in the brain parenchyma (from 18.85 to 15.40 HU) and by 15-19% in the liver, spleen and paravertebral muscle. The six radiologists confirmed these results by assigning high scores (scale from one to five): overall quality 4.5 ± 0.6, noise suppression/detail preservation 4.7 ± 0.5 and diagnostic confidence 4.8 ± 0.4 (p < 0.01). Conclusions: ErisNet improves the quality of CT images and shows strong potential for processing low-dose scans.

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来源期刊

Bioengineering Chemical Engineering-Bioengineering

CiteScore

4.00

自引率

8.70%

发文量

661

期刊介绍： Aims Bioengineering (ISSN 2306-5354) provides an advanced forum for the science and technology of bioengineering. It publishes original research papers, comprehensive reviews, communications and case reports. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. All aspects of bioengineering are welcomed from theoretical concepts to education and applications. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, four key features of this Journal: ● We are introducing a new concept in scientific and technical publications “The Translational Case Report in Bioengineering”. It is a descriptive explanatory analysis of a transformative or translational event. Understanding that the goal of bioengineering scholarship is to advance towards a transformative or clinical solution to an identified transformative/clinical need, the translational case report is used to explore causation in order to find underlying principles that may guide other similar transformative/translational undertakings. ● Manuscripts regarding research proposals and research ideas will be particularly welcomed. ● Electronic files and software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material. ● We also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds. Scope ● Bionics and biological cybernetics: implantology; bio–abio interfaces ● Bioelectronics: wearable electronics; implantable electronics; “more than Moore” electronics; bioelectronics devices ● Bioprocess and biosystems engineering and applications: bioprocess design; biocatalysis; bioseparation and bioreactors; bioinformatics; bioenergy; etc. ● Biomolecular, cellular and tissue engineering and applications: tissue engineering; chromosome engineering; embryo engineering; cellular, molecular and synthetic biology; metabolic engineering; bio-nanotechnology; micro/nano technologies; genetic engineering; transgenic technology ● Biomedical engineering and applications: biomechatronics; biomedical electronics; biomechanics; biomaterials; biomimetics; biomedical diagnostics; biomedical therapy; biomedical devices; sensors and circuits; biomedical imaging and medical information systems; implants and regenerative medicine; neurotechnology; clinical engineering; rehabilitation engineering ● Biochemical engineering and applications: metabolic pathway engineering; modeling and simulation ● Translational bioengineering