腹部临床扫描中的深度学习CT重建:系统性审查和元分析
Mostafa A Shehata1, Abdelrahman M Saad2, Serageldin Kamel3
1Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, 1400 Pressler Street, Unit 1473, Houston, TX, 77030-4009, USA.
Abdominal radiology (New York)
|June 6, 2023
概括
深度学习重建显著提高CT图像质量,减少噪音和改善腹部扫描中的病变检测. 精心选择辐射剂量对于最佳结果至关重要,特别是在肝损伤评估中.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 深度学习重建 (DLR) 算法越来越多地用于计算机断层扫描 (CT).
- 真正忠实 (TF) 和高级智能清晰智商引擎 (AiCE) 是CT最常见的商用DLR算法.
- 评估这些DLR算法的有效性对于优化CT成像协议至关重要.
结论:
- 深度学习重建显著改善腹部CT图像质量.
- 需要对不同剂量水平和临床适应症的DLR进行进一步评估.
- 在DLR应用中,对辐射剂量的明智选择至关重要,特别是在评估小肝损伤时.
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