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多参数体MRI系列的自动分类

Boah Kim¹, Tejas Sudharshan Mathai¹, Kimberly Helm¹

¹Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.

|June 21, 2024

概括

这项研究引入了一种自动化方法来分类多参数MRI (mpMRI) 系列,改进放射学悬挂协议. 人工智能模型准确地识别了不同的MRI系列类型,减少了手动校正需求.

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:

多参数MRI (mpMRI) 对于疾病诊断至关重要,但DICOM头部的不准确性阻碍了自动化系列安排.
目前的做法需要手动纠正放射科医生悬挂协议的mpMRI系列,增加工作量.

研究的目的:

开发和验证一个自动化框架,在mpMRI研究中分类8种不同的系列类型.
通过自动化系列分类来提高放射学悬挂协议的效率和准确性.

主要方法:

一个DenseNet-121模型使用来自三台西门子扫描仪的1,363个mpMRI研究进行了训练.
在模型培训中采用了5倍交叉验证.
该模型的性能在一个单独的测试组中评估了313个mpMRI研究.

主要成果:

自动化框架实现了高性能指标:96.6%的平均精度,96.6%的灵敏度,99.6%的特异性,96.6%的F1得分.
该方法证明了对胸部,腹部和骨盆成像的MRI系列的强有力的分类.

结论:

这项试点工作介绍了第一个分类mpMRI系列类型的方法,用于胸部,腹部和骨盆成像.
拟议的自动化框架为临床放射学中自动化悬挂协议提供了一个强大的解决方案.
这些发现表明,提高工作流程效率和减少现代放射学实践中的错误有很大的潜力.

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