Evaluating the Efficacy of Deep Learning Reconstruction in Reducing Radiation Dose for Computer-Aided Volumetry for Liver Tumor: A Phantom Study.

IF 1 4区 医学 Q4 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Computer Assisted Tomography Pub Date : 2024-11-05 DOI:10.1097/RCT.0000000000001657
Masahiko Nomura, Yoshiharu Ohno, Yuya Ito, Hirona Kimata, Kenji Fujii, Naruomi Akino, Hiroyuki Nagata, Takahiro Ueda, Takeshi Yoshikawa, Daisuke Takenaka, Yoshiyuki Ozawa
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Abstract

Objective: The purpose of this study was to compare radiation dose reduction capability for accurate liver tumor measurements of a computer-aided volumetry (CADv) software for filtered back projection (FBP), hybrid-type iterative reconstruction (IR), mode-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR) at a phantom study.

Methods: A commercially available anthropomorphic abdominal phantom was scanned five times with a 320-detector row CT at 600 mA, 400 mA, 200 mA, and 100 mA and reconstructed by four methods. Signal-to-noise ratios (SNRs) of all lesions within the arterial and portal-venous phase inserts were calculated, and SNR of the lesion phantom was compared with that of all reconstruction methods by means of Tukey's honestly significant difference (HSD) test. Then, tumor volume (V) of each nodule was automatically measured using commercially available CADv software. To compare dose reduction capability for each reconstruction method at both phases, mean differences between measured V and standard references were compared by Tukey's honestly significant difference test among the four different reconstruction methods on CT obtained at each of the four tube currents.

Results: With each of the tube currents, SNRs for MBIR and DLR were significantly higher than those for FBP and hybrid-type IR (p < 0.05). At the arterial phase, the mean difference in V for the CT protocol obtained at 600 or 100 mA and reconstructed with DLR was significantly smaller than that for others (p < 0.05). At the portal-venous phase, the mean differences in V for the CT protocol obtained at 100 mA and reconstructed with hybrid-type IR, MBIR, and DLR were significantly smaller than that for FBP (p < 0.05).

Conclusions: Findings of our phantom study show that reconstruction method had influence on CADv merits for abdominal CT with not only standard but also reduced dose examinations and that DLR can potentially yield better image quality and CADv measurements than FBP, hybrid-type IR, or MBIR in this setting.

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评估深度学习重建在减少肝脏肿瘤计算机辅助容积测量辐射剂量方面的功效:模型研究。
研究目的本研究旨在比较计算机辅助容积测量(CADv)软件的滤波背投影(FBP)、混合型迭代重建(IR)、基于模式的迭代重建(MBIR)和深度学习重建(DLR)在模型研究中准确测量肝脏肿瘤时减少辐射剂量的能力:使用 320 个探头的行式 CT 以 600 mA、400 mA、200 mA 和 100 mA 对一个市售的拟人腹部模型进行了五次扫描,并使用四种方法进行了重建。计算动脉期和门静脉期插入物内所有病灶的信噪比(SNR),并通过 Tukey 的诚实显著性差异(HSD)检验比较病灶模型与所有重建方法的信噪比。然后,使用市售的 CADv 软件自动测量每个结节的肿瘤体积(V)。为了比较每种重建方法在两个阶段降低剂量的能力,通过 Tukey's 诚实显著差异检验比较了四种不同重建方法在四种管电流下获得的 CT 上测量的 V 与标准参考值之间的平均差异:在每种管电流下,MBIR 和 DLR 的信噪比都明显高于 FBP 和混合型 IR(P < 0.05)。在动脉期,用 600 或 100 mA 获取并用 DLR 重建的 CT 方案的平均 V 值差异明显小于其他方案(P < 0.05)。在门-静脉期,在 100 毫安时获得的 CT 方案并使用混合型 IR、MBIR 和 DLR 重建的 V 平均值差异明显小于 FBP(P < 0.05):我们的模型研究结果表明,重建方法不仅会影响腹部 CT 的 CADv 值,而且还会影响减低剂量检查的 CADv 值,在这种情况下,DLR 有可能比 FBP、混合型 IR 或 MBIR 获得更好的图像质量和 CADv 测量值。
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The mission of Journal of Computer Assisted Tomography is to showcase the latest clinical and research developments in CT, MR, and closely related diagnostic techniques. We encourage submission of both original research and review articles that have immediate or promissory clinical applications. Topics of special interest include: 1) functional MR and CT of the brain and body; 2) advanced/innovative MRI techniques (diffusion, perfusion, rapid scanning); and 3) advanced/innovative CT techniques (perfusion, multi-energy, dose-reduction, and processing).
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