AI-enhanced PET/CT image synthesis using CycleGAN for improved ovarian cancer imaging.

Polish journal of radiology Pub Date : 2025-01-17 eCollection Date: 2025-01-01 DOI:10.5114/pjr/196804
Amir Hossein Farshchitabrizi, Mohammad Hossein Sadeghi, Sedigheh Sina, Mehrosadat Alavi, Zahra Nasiri Feshani, Hamid Omidi
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引用次数: 0

Abstract

Purpose: Ovarian cancer is the fifth fatal cancer among women. Positron emission tomography (PET), which offers detailed metabolic data, can be effectively used for early cancer screening. However, proper attenuation correction is essential for interpreting the data obtained by this imaging modality. Computed tomography (CT) imaging is commonly performed alongside PET imaging for attenuation correction. This approach may introduce some issues in spatial alignment and registration of the images obtained by the two modalities. This study aims to perform PET image attenuation correction by using generative adversarial networks (GANs), without additional CT imaging.

Material and methods: The PET/CT data from 55 ovarian cancer patients were used in this study. Three GAN architectures: Conditional GAN, Wasserstein GAN, and CycleGAN, were evaluated for attenuation correction. The statistical performance of each model was assessed by calculating the mean squared error (MSE) and mean absolute error (MAE). The radiological performance assessments of the models were performed by comparing the standardised uptake value and the Hounsfield unit values of the whole body and selected organs, in the synthetic and real PET and CT images.

Results: Based on the results, CycleGAN demonstrated effective attenuation correction and pseudo-CT generation, with high accuracy. The MAE and MSE for all images were 2.15 ± 0.34 and 3.14 ± 0.56, respectively. For CT reconstruction, such values were found to be 4.17 ± 0.96 and 5.66 ± 1.01, respectively.

Conclusions: The results showed the potential of deep learning in reducing radiation exposure and improving the quality of PET imaging. Further refinement and clinical validation are needed for full clinical applicability.

Abstract Image

Abstract Image

Abstract Image

人工智能增强PET/CT图像合成使用CycleGAN改善卵巢癌成像。
目的:卵巢癌是女性第五大致命癌症。正电子发射断层扫描(PET)可以提供详细的代谢数据,可以有效地用于早期癌症筛查。然而,适当的衰减校正对于解释这种成像方式获得的数据至关重要。计算机断层扫描(CT)成像通常与PET成像一起进行衰减校正。这种方法可能会带来空间对齐和配准的问题,由两种模式得到的图像。本研究旨在通过生成对抗网络(GANs)进行PET图像衰减校正,而无需额外的CT成像。材料与方法:本研究采用55例卵巢癌患者的PET/CT资料。评估了三种GAN架构:条件GAN、Wasserstein GAN和CycleGAN的衰减校正。通过计算均方误差(MSE)和平均绝对误差(MAE)来评估每个模型的统计性能。通过比较合成和真实PET和CT图像中全身和选定器官的标准化摄取值和Hounsfield单位值,对模型的放射学性能进行评估。结果:基于实验结果,CycleGAN显示了有效的衰减校正和伪ct生成,具有较高的精度。所有图像的MAE和MSE分别为2.15±0.34和3.14±0.56。CT重建时,这两个值分别为4.17±0.96和5.66±1.01。结论:结果显示深度学习在减少辐射暴露和提高PET成像质量方面具有潜力。需要进一步完善和临床验证,以实现完全的临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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