在PET/CT成像中的深度学习技术:从sinogram到图像空间的全面审查
Maryam Fallahpoor1, Subrata Chakraborty2, Biswajeet Pradhan3
1Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), School of Civil and Environmental Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.
Computer methods and programs in biomedicine
|November 4, 2023
概括
深度学习 (DL) 通过改进病变检测和细分来增强对诊断的正子发射断层扫描/计算机断层扫描 (PET/CT) 成像. 这次审查强调了DL的重点.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 混合正子发射断层扫描/计算机断层扫描 (PET/CT) 影像提供了瘤学,神经学和心脏学方面的全面数据.
- 手动解释PET/CT扫描是耗时的,需要专业知识.
- 在医学成像中对AI的现有评论往往缺乏对深度学习 (DL) 对PET/CT的具体应用的关注.
研究的目的:
- 综合审查将深度学习 (DL) 应用于 pozitron发射断层扫描/计算机断层扫描 (PET/CT) 成像的研究.
- 确定有效的DL方法,预处理技术和现场限制.
- 分析PET/CT图像分析中DL应用的特征.
主要方法:
- 2017年至2022年间发表的99项研究的系统审查,重点关注PET/CT中的DL.
- 在选定的研究中使用的预处理算法和深度学习模型的分析.
- 识别常见和特定的预处理技术及其有效性.
主要成果:
- 深度学习显示了PET/CT在病变检测,瘤细分和疾病分类方面的巨大潜力.
- 确定了用于PET/CT分析的有效DL模型和预处理技术.
- 关键的局限性包括注释数据集的稀缺性和模型可解释性和不确定性量化方面的挑战.
结论:
- 深度学习显著提高了PET/CT图像解释的准确性和效率.
- 新兴的DL模型 (如基于注意力的变压器) 和放射学显示出 PET/CT 应用的前景.
- 持续的研究对于克服局限性和扩大DL在PET/CT成像中的使用至关重要.
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