Possible Radiation Dose Reduction in Abdominal Plain CT Using Deep Learning Reconstruction

Onizuka Yasuhiro, Sakai Yuki, Shirasaka Takashi, Kondo Masatoshi, Kato Toyoyuki

doi:10.6009/jjrt.2023-1289

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Deep learning reconstructionを用いた腹部単純CTにおける放射線被ばく低減の可能性
Deep learning reconstruction オモチイタフクブタンジュン CT ニオケルホウシャセンヒバクテイゲンノカノウセイ

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Abstract

<p>Purpose: The purposes of this study were to evaluate the low-contrast detectability of CT images assuming hepatocellular carcinoma and to determine whether dose reduction in abdominal plain CT imaging is possible. Methods: A Catphan 600 was imaged at 350, 250, 150, and 50 mA using an Aquilion ONE PRISM Edition (Canon) and reconstructed using deep learning reconstruction (DLR) and model-based iterative reconstruction (MBIR). A low-contrast object-specific contrast-to-noise ratio (CNR_LO) was measured and compared in a 5-mm module with a CT value difference of 10 HU, assuming hepatocellular carcinoma; a visual examination was also performed. Moreover, an NPS within a uniform module was measured. Results: CNR_LO was higher for DLR at all doses (1.12 at 150 mA for DLR and 1.07 at 250 mA for MBIR). On visual evaluation, DLR could detect up to 150 mA and MBIR up to 250 mA. The NPS was lower for DLR at 0.1 cycles/mm at 150 mA. Conclusion: The low-contrast detection performance was better with DLR than with MBIR, indicating the possibility of dose reduction.</p>

Journal

Japanese Journal of Radiological Technology

Japanese Journal of Radiological Technology 79 (5), 446-452, 2023

Japanese Society of Radiological Technology

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