Motion artifact classification for quality assessment of cochlear implant postoperative CT images

Yubo Fan; Han Liu; Jack H. Noble; Benoit M. Dawant

doi:10.1117/12.3007495

29 March 2024 Motion artifact classification for quality assessment of cochlear implant postoperative CT images

Yubo Fan, Han Liu, Jack H. Noble, Benoit M. Dawant

Proceedings Volume 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling; 129280A (2024) https://doi.org/10.1117/12.3007495
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

A cochlear implant (CI) includes an electrode array (EA) that is inserted into the cochlea to restore hearing. Localizing the EA in postoperative computed tomography (CT) images is needed in image-guided CI programming, which has been shown to improve hearing outcomes. Postoperative images with adequate image quality are required to allow the EA to be reliably and precisely localized. However, these images are sometimes affected by motion artifacts, which can make the localization task unreliable or even cause it to fail. Thus, flagging these low-quality images prior to the subsequent clinical use is important. In this work, we propose to assess the image quality by using a 3D convolutional neural network to classify the level (no/mild/moderate/severe) of the motion artifacts that affect the image. To address the challenges of subjective annotations and class imbalance, several techniques (a new loss term, an oversampling strategy, and motion artifact simulation) are used during training. Results show that our proposed method can achieve accuracy values of 81% in the four-class motion artifact classification and 88% in binary classification (no vs. some artifacts), demonstrating that the proposed method has the potential to reduce time and effort in image quality assessment that is traditionally done through visual inspection.

Conference Presentation

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

Citation Download Citation

Yubo Fan, Han Liu, Jack H. Noble, and Benoit M. Dawant "Motion artifact classification for quality assessment of cochlear implant postoperative CT images", Proc. SPIE 12928, Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling, 129280A (29 March 2024); https://doi.org/10.1117/12.3007495

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