关键词: as low as reasonably achievable computed tomography convolutional neural network deep learning dose reduction generative adversarial network image processing machine learning medical imaging noise

来  源:   DOI:10.3390/children9071044

Abstract:
Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question \"What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?\" Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36-70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies.
摘要:
辐射剂量优化在儿科放射学中尤为重要。因为儿童更容易受到电离辐射的潜在有害影响。然而,目前仅发表了一篇关于儿科计算机断层扫描(CT)剂量优化人工智能(AI)的叙述性综述.这项系统评价的目的是回答以下问题:“在儿科放射学中引入的用于剂量优化的AI技术和架构是什么?它们的特定应用领域,和表演?“使用电子数据库的文献检索于2022年6月3日进行。包括16篇符合选择标准的文章。包括的研究表明,深度卷积神经网络(CNN)是用于儿科放射学剂量优化的最常见的AI技术和架构。除三项研究外,所有研究均评估了AI在腹部剂量优化中的表现,胸部,头部,脖子,和骨盆CT;CT血管造影;和通过深度学习图像重建的双能CT。大多数研究表明,人工智能可以将辐射剂量减少36-70%,而不会丢失诊断信息。尽管基于深度CNN的商用AI模型占据主导地位,并取得了有希望的结果,本土模型可以提供可比的性能。由于样本量小,范围窄,未来探索AI在儿科放射学中的剂量优化价值是必要的(只有三种模式,CT,正电子发射断层扫描/磁共振成像和移动射线照相术,并非涵盖所有检查类型)现有研究。
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