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[Preprint]. 2024 Dec 9:rs.3.rs-5334453.
doi: 10.21203/rs.3.rs-5334453/v1.

Automated Characterization of Abdominal MRI Exams Using Deep Learning

Joonghyun Kim  1 , Allison Chae  1 , Jeffrey Duda  1 , Ari Borthakur  1 , Daniel Rader  1 , James C Gee  1 , Charles E Kahn Jr  1 , Penn Medicine BioBank  1 , Walter R Witschey  1 , Hersh Sagreiya  1
Affiliations

Automated Characterization of Abdominal MRI Exams Using Deep Learning

Joonghyun Kim et al. Res Sq. .

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Abstract

Advances in magnetic resonance imaging (MRI) have revolutionized disease detection and treatment planning. However, as the volume and complexity of MRI data grow with increasing heterogeneity between institutions in imaging protocol, scanner technology, and data labeling, there is a need for a standardized methodology to efficiently identify, characterize, and label MRI sequences. Such a methodology is crucial for advancing research efforts that incorporate MRI data from diverse populations to develop robust machine learning models. This research utilizes convolutional neural networks (CNNs) to automatically classify sequence, orientation, and contrast, specifically tailored for abdominal MRI. Three distinct CNN models with similar backbone architectures were trained to classify single image slices into one of 12 sequences, 4 orientations, and 2 contrast classes. Results derived from this method demonstrate high levels of performance for the three specialized CNN models, with model accuracies for sequence, orientation, and contrast of 96.9%, 97.4%, and 97.3%, respectively.

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Conflict of interest statement

Competing interests statement I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Figures

Figure 1
Figure 1
Sample sequence (A), orientation (B), and contrast (C) labeling of nine MRI image slices.
Figure 2
Figure 2
Convolutional neural network model architecture.
Figure 3
Figure 3
Confusion matrices for the classification of sequence, orientation, and contrast.
See this image and copyright information in PMC

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