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International Journal of Computer Assisted Radiology and Surgery

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14-01-2024 | Original Article

Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer

Authors: Yuhei Koike, Shingo Ohira, Yuki Yamamoto, Masayoshi Miyazaki, Koji Konishi, Satoaki Nakamura, Noboru Tanigawa

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 3/2024

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Abstract

Purpose

While dual-energy computed tomography (DECT) images provide clinically useful information than single-energy CT (SECT), SECT remains the most widely used CT system globally, and only a few institutions can use DECT. This study aimed to establish an artificial intelligence (AI)-based image-domain material decomposition technique using multiple keV-output learning of virtual monochromatic images (VMIs) to create DECT-equivalent images from SECT images.

Methods

This study involved 82 patients with head and neck cancer. Of these, the AI model was built with data from the 67 patients with only DECT scans, while 15 patients with both SECT and DECT scans were used for SECT testing. Our AI model generated VMI_50keV and VMI_100keV from VMI_70keV equivalent to 120-kVp SECT images. We introduced a loss function for material density images (MDIs) in addition to the loss for VMIs. For comparison, we trained the same model with the loss for VMIs only. DECT-equivalent images were generated from SECT images and compared with the true DECT images.

Results

The prediction time was 5.4 s per patient. The proposed method with the MDI loss function quantitatively provided more accurate DECT-equivalent images than the model trained with the loss for VMIs only. Using real 120-kVp SECT images, the trained model produced precise DECT images of excellent quality.

Conclusion

In this study, we developed an AI-based material decomposition approach for head and neck cancer patients by introducing the loss function for MDIs via multiple keV-output learning. Our results suggest the feasibility of AI-based image-domain material decomposition in a conventional SECT system without a DECT scanner.

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Title: Artificial intelligence-based image-domain material decomposition in single-energy computed tomography for head and neck cancer
Authors: Yuhei Koike
Shingo Ohira
Yuki Yamamoto
Masayoshi Miyazaki
Koji Konishi
Satoaki Nakamura
Noboru Tanigawa
Publication date: 14-01-2024
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 3/2024
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-023-03058-y

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Abstract

Purpose

Methods

Results

Conclusion

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