METHODS: The search engine used was PubMed with the search period being 2010-2024. The key words used were: \'comput* tomography\', \'CT\', \'abdom*\', \'dose\', \'risk\', \'SSDE\', \'image quality\', \'water equivalent diameter\', \'size\', \'body composition\', \'habit*\', \'BMI\', \'obes*\', \'overweight\'. Since BH is critical for patient specific optimisation, articles correlating RD vs BH, and IQ vs BH were reviewed.
RESULTS: The inventory includes 11 BH, 12 IQ and 6 RD metrics. 25 RD vs BH correlation studies and 9 IQ vs BH correlation studies were identified. 7 articles in the latter group correlated metrics from all three categories concurrently.
CONCLUSIONS: Protocol optimisation should be fine-tuned to the level of the individual patient and particular clinical query. This would require a judicious choice of metrics from each of the three categories. It is suggested that, for increased utility in clinical practice, more future optimisation studies be clinical task based and involve the three categories of metrics concurrently.
方法:使用的搜索引擎是PubMed,搜索周期为2010-2024。使用的关键词是:\'计算机断层摄影术\',\'CT\',\'abdom*\',\'剂量\',\'风险\',\'SSDE\',\'图像质量\',“水当量直径”,\'大小\',\'身体成分\',\'习惯*\',\'BMI\',\'obes*\',\'超重\'。由于BH对于患者特定的优化至关重要,与RD和BH相关的文章,并对IQ和BH进行了回顾。
结果:清单包括11BH,12个IQ和6个RD指标。确定了25项RD与BH的相关性研究和9项IQ与BH的相关性研究。后一组中的7篇文章同时关联了所有三个类别的指标。
结论:方案优化应根据个体患者和特定临床查询的水平进行微调。这将需要从三个类别中的每一个中明智地选择度量。有人建议,为了增加临床实践中的效用,未来更多的优化研究是基于临床任务的,同时涉及三类指标。