To improve the PET image quality by a deep progressive learning (DPL) reconstruction algorithm and evaluate the DPL performance in lesion quantification. We reconstructed PET images from 48 oncological patients using ordered subset expectation maximization (OSEM) and deep progressive learning (DPL) methods. The patients were enrolled into three overlapped studies: 11 patients for image quality assessment (study 1), 34 patients for sub-centimeter lesion quantification (study 2), and 28 patients for imaging of overweight or obese individuals (study 3). In study 1, we evaluated the image quality visually based on four criteria: overall score, image sharpness, image noise, and diagnostic confidence. We also measured the image quality quantitatively using the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), contrast-to-background ratio (CBR), and contrast-to-noise ratio (CNR). To evaluate the performance of the DPL algorithm in quantifying lesions, we compared the maximum standardized uptake values (SUVmax), SBR, CBR, SNR and CNR of 63 sub-centimeter lesions in study 2 and 44 lesions in study 3. DPL produced better PET image quality than OSEM did based on the visual evaluation methods when the acquisition time was 0.5, 1.0 and 1.5 min/bed. However, no discernible differences were found between the two methods when the acquisition time was 2.0, 2.5 and 3.0 min/bed. Quantitative results showed that DPL had significantly higher values of SBR, CBR, SNR, and CNR than OSEM did for each acquisition time. For sub-centimeter lesion quantification, the SUVmax, SBR, CBR, SNR, and CNR of DPL were significantly enhanced, compared with OSEM. Similarly, for lesion quantification in overweight and obese patients, DPL significantly increased these parameters compared with OSEM. The DPL algorithm dramatically enhanced the quality of PET images and enabled more accurate quantification of sub-centimeters lesions in patients and lesions in overweight or obese patients. This is particularly beneficial for overweight or obese patients who usually have lower image quality due to the increased attenuation.

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研究 PET 图像质量与采集时间/床位的关系，并通过深度渐进学习提高病变量化准确性

通过深度渐进学习（DPL）重建算法提高 PET 图像质量，并评估 DPL 在病灶量化方面的性能。我们使用有序子集期望最大化 (OSEM) 和深度渐进学习 (DPL) 方法重建了 48 名肿瘤患者的 PET 图像。这些患者被纳入三项重叠研究：11 名患者进行图像质量评估（研究 1），34 名患者进行亚厘米病变量化（研究 2），28 名患者进行超重或肥胖个体成像（研究 3）。在研究 1 中，我们根据四个标准对图像质量进行视觉评估：总体得分、图像清晰度、图像噪声和诊断置信度。我们还使用信号与背景比（SBR）、信噪比（SNR）、对比度与背景比（CBR）和对比度与噪声比（CNR）定量测量图像质量。为了评估 DPL 算法在量化病灶方面的性能，我们比较了研究 2 中的 63 个亚厘米病灶和研究 3 中的 44 个病灶的最大标准化摄取值 (SUVmax)、SBR、CBR、SNR 和 CNR。DPL 产生了更好的 PET当采集时间为 0.5、1.0 和 1.5 分钟/床时，基于目视评估方法的图像质量优于 OSEM。然而，当采集时间为 2.0、2.5 和 3.0 分钟/床时，两种方法之间没有发现明显差异。定量结果表明，在每个采集时间，DPL 的 SBR、CBR、SNR 和 CNR 值均显着高于 OSEM。对于亚厘米病变量化，与 OSEM 相比，DPL 的 SUVmax、SBR、CBR、SNR 和 CNR 显着增强。同样，对于超重和肥胖患者的病变量化，与 OSEM 相比，DPL 显着增加了这些参数。DPL 算法极大地提高了 PET 图像的质量，并能够更准确地量化患者的亚厘米病变以及超重或肥胖患者的病变。这对于超重或肥胖的患者尤其有益，他们通常由于衰减增加而图像质量较低。