Multi-parametric MRI (mpMRI) studies are widely available in clinical
practice for the diagnosis of various diseases. As the volume of mpMRI exams
increases yearly, there are concomitant inaccuracies that exist within the
DICOM header fields of these exams. This precludes the use of the header
information for the arrangement of the different series as part of the
radiologist's hanging protocol, and clinician oversight is needed for
correction. In this pilot work, we propose an automated framework to classify
the type of 8 different series in mpMRI studies. We used 1,363 studies acquired
by three Siemens scanners to train a DenseNet-121 model with 5-fold
cross-validation. Then, we evaluated the performance of the DenseNet-121
ensemble on a held-out test set of 313 mpMRI studies. Our method achieved an
average precision of 96.6%, sensitivity of 96.6%, specificity of 99.6%, and F1
score of 96.6% for the MRI series classification task. To the best of our
knowledge, we are the first to develop a method to classify the series type in
mpMRI studies acquired at the level of the chest, abdomen, and pelvis. Our
method has the capability for robust automation of hanging protocols in modern
radiology practice.

提出了一种自动化的框架来对多参数磁共振成像（mpMRI）研究中的不同系列进行分类，使用了 DenseNet-121 模型，以 96.6% 的平均精确度、敏感性和 F1 值以及 99.6% 的特异度在 mpMRI 研究中取得了较高的分类性能。为我们所知，这是首个能够对胸部、腹部和盆腔层次的 mpMRI 研究中不同系列进行分类的方法，具备了在现代放射学实践中进行稳健自动化悬挂方案的能力。

多参美体 MRI 系列的自动分类

Automated classification of multi-parametric body MRI series

Multi-parametric MRI of the body is routinely acquired for the identification
of abnormalities and diagnosis of diseases. However, a standard naming
convention for the MRI protocols and associated sequences does not exist due to
wide variations in imaging practice at institutions and myriad MRI scanners
from various manufacturers being used for imaging. The intensity distributions
of MRI sequences differ widely as a result, and there also exists information
conflicts related to the sequence type in the DICOM headers. At present,
clinician oversight is necessary to ensure that the correct sequence is being
read and used for diagnosis. This poses a challenge when specific series need
to be considered for building a cohort for a large clinical study or for
developing AI algorithms. In order to reduce clinician oversight and ensure the
validity of the DICOM headers, we propose an automated method to classify the
3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis. In
our pilot work, our 3D DenseNet-121 model achieved an F1 score of 99.5% at
differentiating 5 common MRI sequences obtained by three Siemens scanners
(Aera, Verio, Biograph mMR). To the best of our knowledge, we are the first to
develop an automated method for the 3D classification of MRI sequences in the
chest, abdomen, and pelvis, and our work has outperformed the previous
state-of-the-art MRI series classifiers.

提出了一种自动分类胸、腹部和盆腔的三维 MRI 序列的方法，以减少临床医生的监督并确保 DICOM 标头的有效性，实验结果表明这种方法优于先前的 MRI 序列分类器。