ISSN 1004-4140
    CN 11-3017/P

    深度学习重建算法联合CE-Boost技术实现80 kVp头颈部CT血管成像辐射剂量与碘摄入量双重降低:与100 kVp混合迭代重建方案的对比研究

    Reducing Both Radiation Dose and Iodine Intake in 80 kVp Head and Neck CT Angiography Using Deep Learning Image Reconstruction Combined with Contrast- Enhancement-boost Technology: A Comparison with 100 kVp Imaging Using Hybrid Iterative Reconstruction

    • 摘要: 目的:评估 80kVp 头颈部计算机断层扫描血管造影(CTA)中,深度学习重建(DLR)联合柔性减影(CE-boost)技术,在显著降低辐射剂量和对比剂(CM)用量的情况下,相较于采用混合迭代重建(HIR)的标准 100kVp 方案的临床效能。方法 :前瞻性纳入 66 例患者,随机分为两组:低剂量组(n=33)采用 80 kVp 管电压、28 mL 对比剂,噪声指数(NI)设为 15;常规剂量组(n=33)采用 100 kVp 管电压、40 mL 对比剂,噪声指数(NI)设为 10。低剂量组图像分别采用混合迭代重建(HIR)及轻、中、高强度算法DLR,并分别在联合与不联合CE-boost技术的条件下进行重建,最终生成 8 组图像:L-HIR 组 、L-DLRmild 组 、L-DLRstandard 组 、L-DLRstrong 组 、L-HIR-CE 组 、L-DLRmild- CE 组 、L-DLRstandard-CE 组和 L-DLRstrong-CE 组。常规剂量组图像仅采用混合迭代重建(R-HIR)。定量分析包括计算并对比 6 条关键血管的 CT 衰减值、图像噪声、信噪比(SNR)及对比噪声比(CNR),包括主动脉弓(AA)、颈内动脉(ICA)、颈外动脉(ECA)、椎动脉(VA)、基底动脉(BA)及大脑中动脉(MCA)。由两名放射科医师采用 5 分制量表独立评估主观图像质量,统计学显著性定义为 P < 0.05。结果:与常规剂量组相比,低剂量方案显著减少了30%对比剂用量(28 mL vs. 40 mL )和65%辐射剂量((0.41 ± 0.08)mSv vs.(1.18 ± 0.12)mSv)。L-DLRstandard 和 L-DLRstrong 组在所有血管的 SNR 和 CNR 均达到或优于 R-HIR 。但各强度水平L-DLR 的主观图像质量评分均低于 R-HIR(两名医师评估结果均 P < 0.05)。低剂量方案联合 CE-boost 技术后,L-DLRstrong-CE组的客观图像性能显著提升(均 P < 0.05),且主观图像质量评分与 R-HIR 无统计学差异(阅片者 1:P=0.15;阅片者 2:P=0.06)。结论: 与标准 100kVp 头颈部 CTA 方案相比,80kVp 下联合 DLR 与 CE-boost 技术可使对比剂用量减少 30%、辐射剂量降低 65%,同时维持良好的客观及主观图像质量。

       

      Abstract: Purpose: To assess the clinical efficacy of integrating deep learning reconstruction (DLR) with contrast-enhancement-boost (CE-boost) in 80 kVp head and neck CT angiography (CTA) using substantially lowered radiation and contrast medium (CM) doses, compared to the standard 100 kVp protocol using hybrid iterative reconstruction (HIR). Methods: Sixty-six patients were prospectively enrolled and randomly assigned to one of two groups: the low-dose group (n=33), receiving 80 kVp and 28 mL contrast medium (CM) with a noise index (NI) of 15; and the regular-dose group (n=33), receiving 100 kVp and 40 mL CM with an NI of 10. For the low-dose group, images underwent reconstruction using both hybrid iterative reconstruction (HIR) and deep learning reconstruction (DLR) at mild-, standard-, and strong-strength levels, both before and after combination with contrast enhancement-boost (CE-boost). This generated eight distinct datasets: L-HIR, L-DLRmild, L-DLRstandard, L-DLRstrong, L-HIR-CE, L-DLRmild-CE, L-DLRstandard-CE, and L-DLRstrong-CE. Images for the regular-dose group were reconstructed solely with HIR (R-HIR). Quantitative analysis involved calculating and comparing CT attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) within six key vessels: the aortic arch (AA), internal carotid artery (ICA), external carotid artery (ECA), vertebral arteries (VA), basilar artery (BA), and middle cerebral artery (MCA). Two radiologists independently assessed subjective image quality using a 5-point scale, with statistical significance defined as P < 0.05. Results: Compared to the regular-dose group, the low-dose protocol achieved a substantial reduction in contrast media volume (28 mL versus 40 mL, a 30% decrease) and radiation exposure ((0.41±0.08) mSv versus (1.18±0.12) mSv, a 65% reduction). Both L-DLRstandard and L-DLRstrong delivered comparable or superior SNR and CNR across all vascular segments relative to R-HIR. However, subjective image quality scores for L-DLR at all strength levels fell below those for R-HIR (all P < 0.05 for both readers). Combining CE-boost with the low-dose protocol significantly enhanced the objective image performance of L-DLRstrong-CE (all P < 0.05) and produced subjective image scores comparable to R-HIR (reader 1: P=0.15; reader 2: P=0.06). Conclusion: When compared to the standard 100 kVp head and neck CTA, the combination of the DLR and CE-boost techniques at 80 kVp can achieve a 30% reduction in contrast dose and a 65% reduction in radiation dose, while maintaining both objective and subjective image quality.

       

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