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

doi:10.1117/12.3008780

3 April 2024 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

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129270K (2024) https://doi.org/10.1117/12.3008780
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee, Jianfei Liu, and Ronald M. Summers "Automated classification of body MRI sequence type using convolutional neural networks", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129270K (3 April 2024); https://doi.org/10.1117/12.3008780

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