Patient-specific data augmentation method to improve the training efficiency of convolutional neural network for metal artifact reduction with a single patient dental CT volume

Junhyun Ahn; Jongduk Baek

doi:10.1117/12.3000027

1 April 2024 Patient-specific data augmentation method to improve the training efficiency of convolutional neural network for metal artifact reduction with a single patient dental CT volume

Junhyun Ahn, Jongduk Baek

Author Affiliations +

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129251U (2024) https://doi.org/10.1117/12.3000027
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Deep learning-based metal artifact reduction methods often struggle to apply the trained network to real data due to the difference between simulated training and real application datasets. To solve this problem, we propose a patient-specific data augmentation method that can effectively train the convolutional neural network of metal artifact reduction with a single patient dental CT volume. The idea of the proposed method is to generate the dataset using metal-unaffected slices from the patient data that require metal artifact reduction. The dataset generated by leveraging the adjacency of both metal-affected and metal-unaffected slices closely resembles real metal artifact images in terms of teeth shape and CT system geometry. To overcome the problem of small data size due to using only a single patient's data, we augment the data by generating various patient-specific metal masks. We segment the bone and label each tooth to get the size and position of the tooth. We determine the size and shape of the metal objects in the metal mask based on the information of the labeled teeth. Metal artifacts are simulated from metal masks and patient data using a polychromatic sinogram simulation method and iterative estimation of metal attenuation coefficients. For experiments, we train the same U-net structure network with different train datasets and tested with real metal artifact. The results show that the patientspecific dataset of the proposed method is more suitable for reducing the real metal artifact than the dataset generated using large amounts of data.

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

Citation Download Citation

Junhyun Ahn and Jongduk Baek "Patient-specific data augmentation method to improve the training efficiency of convolutional neural network for metal artifact reduction with a single patient dental CT volume", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129251U (1 April 2024); https://doi.org/10.1117/12.3000027

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