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。