Automated classification of multi-parametric body MRI series

TL;DR

This paper presents an automated deep learning framework that accurately classifies different series types in multi-parametric MRI studies, improving workflow efficiency in radiology by reducing manual oversight.

Contribution

The study introduces the first method to classify series in mpMRI across chest, abdomen, and pelvis, achieving high accuracy and robustness.

Findings

Achieved 96.6% precision and sensitivity in series classification.

Demonstrated robustness across multiple scanners and anatomical regions.

Enabled automation of radiology hanging protocols.

Abstract

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.