OBJECTIVES To assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm. METHODS Data acquisitions were performed at seven dose levels (CTDIvol : 15/10/7.5/5/2.5/1/0.5 mGy) using a standard phantom designed for image quality assessment. Raw data were reconstructed using the filtered back projection (FBP), two levels of IR (ASiR-V50% (AV50); ASiR-V100% (AV100)), and three levels of DLIR (TrueFidelity™ low, medium, high). Noise power spectrum (NPS) and task-based transfer function (TTF) were computed. Detectability index (d') was computed to model a large mass in the liver, a small calcification, and a small subtle lesion with low contrast. RESULTS NPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF50% obtained with DLIR was higher than that with IR. d' was higher with DLIR than with AV50 but lower with DLIR-L and DLIR-M than with AV100. d' values were higher with DLIR-H than with AV100 for the small low-contrast lesion (10 ± 4%) and in the same range for the other simulated lesions. CONCLUSIONS New DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR. KEY POINTS • This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm. • The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR. • As compared with IR, DLIR seems to open further possibility of dose optimization.

中文翻译：

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用于CT的深度学习图像重建算法的图像质量和降低剂量的机会：一项幻像研究。

目的评估与混合迭代重建（IR）算法相比，新的深度学习图像重建（DLIR）算法对图像质量和减少剂量的影响。方法采用设计用于图像质量评估的标准体模，以七个剂量水平（CTDIvol：15/10 / 7.5 / 5 / 2.5 / 1 / 0.5 mG​​y）进行数据采集。使用滤波后的反投影（FBP），两个级别的IR（ASiR-V50％（AV50）; ASiR-V100％（AV100））和三个级别的DLIR（TrueFidelity™低，中，高）重建原始数据。计算了噪声功率谱（NPS）和基于任务的传递函数（TTF）。计算可检测性指数（d'）以模拟肝脏中的大块，小钙化和低对比度的小细微病变。结果AV50的NPS峰值高于所有DLIR水平，而DLIR-H的NPS峰值高于AV100。DLIR的平均NPS空间频率高于IR。对于所有DLIR水平，用DLIR获得的TTF50％高于使用IR获得的TTF50％。DLIR的d'高于AV50，但DLIR-L和DLIR-M的d'低于AV100。对于小的低对比度病变（10±4％），DLIR-H的d'值高于AV100，其他模拟病变的d'值在相同范围内。结论新的DLIR算法可在不改变噪声纹理的情况下减少噪声并提高空间分辨率和可检测性。用DLIR获得的图像似乎比使用混合IR的图像显示出更大的剂量优化潜力。要点•本研究评估了与混合迭代重建（IR）算法相比，新的深度学习图像重建（DLIR）算法对图像质量和辐射剂量的影响。•新的DLIR算法减少了噪声，提高了空间分辨率和可检测性，而不会感觉到纹理的改变（通常通过IR进行报告）。•与IR相比，DLIR似乎为剂量优化提供了进一步的可能性。