Automated Classification of Body MRI Sequence Type Using Convolutional
  Neural Networks
Kimberly Helm Tejas Sudharshan Mathai Boah Kim Pritam Mukherjee Jianfei Liu Ronald M. Summers
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.