Greffier Joël, Durand Quentin, Serrand Chris, Sales Renaud, de Oliveira Fabien, Beregi Jean-Paul, Dabli Djamel, Frandon Julien
IMAGINE UR UM 103, Department of Medical Imaging, Nimes University Hospital, Montpellier University, 30029 Nimes, France.
Department of Biostatistics, Clinical Epidemiology, Public Health, and Innovation in Methodology (BESPIM), CHU Nimes, 30029 Nimes, France.
Diagnostics (Basel). 2023 Mar 20;13(6):1182. doi: 10.3390/diagnostics13061182.
The study's aim was to assess the impact of a deep learning image reconstruction algorithm (Precise Image; DLR) on image quality and liver metastasis conspicuity compared with an iterative reconstruction algorithm (IR). This retrospective study included all consecutive patients with at least one liver metastasis having been diagnosed between December 2021 and February 2022. Images were reconstructed using level 4 of the IR algorithm (i4) and the Standard/Smooth/Smoother levels of the DLR algorithm. Mean attenuation and standard deviation were measured by placing the ROIs in the fat, muscle, healthy liver, and liver tumor. Two radiologists assessed the image noise and image smoothing, overall image quality, and lesion conspicuity using Likert scales. The study included 30 patients (mean age 70.4 ± 9.8 years, 17 men). The mean CTDI was 6.3 ± 2.1 mGy, and the mean dose-length product 314.7 ± 105.7 mGy.cm. Compared with i4, the HU values were similar in the DLR algorithm at all levels for all tissues studied. For each tissue, the image noise significantly decreased with DLR compared with i4 ( < 0.01) and significantly decreased from Standard to Smooth (-26 ± 10%; < 0.01) and from Smooth to Smoother (-37 ± 8%; < 0.01). The subjective image assessment confirmed that the image noise significantly decreased between i4 and DLR ( < 0.01) and from the Standard to Smoother levels ( < 0.01), but the opposite occurred for the image smoothing. The highest scores for overall image quality and conspicuity were found for the Smooth and Smoother levels.
该研究的目的是评估深度学习图像重建算法（精确图像；DLR）与迭代重建算法（IR）相比，对图像质量和肝转移灶可见性的影响。这项回顾性研究纳入了2021年12月至2022年2月期间所有连续诊断出至少有一处肝转移的患者。图像使用IR算法的第4级（i4）以及DLR算法的标准/平滑/更平滑级别进行重建。通过在脂肪、肌肉、健康肝脏和肝肿瘤中放置感兴趣区（ROI）来测量平均衰减和标准差。两名放射科医生使用李克特量表评估图像噪声、图像平滑度、整体图像质量和病变可见性。该研究包括30名患者（平均年龄70.4±9.8岁，17名男性）。平均容积CT剂量指数（CTDI）为6.3±2.1毫西弗，平均剂量长度乘积为314.7±105.7毫西弗·厘米。与i4相比，在所研究的所有组织中，DLR算法各级别的HU值相似。对于每种组织，与i4相比，DLR算法下的图像噪声显著降低（<0.01），并且从标准级别到平滑级别显著降低（-26±10%；<0.01），从平滑级别到更平滑级别也显著降低（-37±8%；<0.01）。主观图像评估证实，i4和DLR之间的图像噪声显著降低（<0.01），从标准级别到更平滑级别也显著降低（<0.01），但图像平滑度的情况则相反。整体图像质量和可见性的最高分数出现在平滑和更平滑级别。
