From the Department of Radiology (F.I.B., N.R.H., A.F., K.R., K.N.G., S.L., C.H.M., J.G.F.), Division of Biomedical Statistics and Informatics, Department of Quantitative Health Sciences (N.B.L.), and Division of Hematology, Department of Medicine (S.K., J.M.C.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; and Siemens Medical Solutions USA, Malvern, Pa (E.R.S.).
Radiology. 2023 Jan;306(1):229-236. doi: 10.1148/radiol.220311. Epub 2022 Sep 6.
Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatial resolution in whole-body low-dose CT scans for viewing multiple myeloma by using PCD CT with deep learning denoising compared with conventional EID CT. Materials and Methods Between April and July 2021, adult participants who underwent a whole-body EID CT scan were prospectively enrolled and scanned with a PCD CT system in ultra-high-resolution mode at matched radiation dose (8 mSv for an average adult) at an academic medical center. EID CT and PCD CT images were reconstructed with Br44 and Br64 kernels at 2-mm section thickness. PCD CT images were also reconstructed with Br44 and Br76 kernels at 0.6-mm section thickness. The thinner PCD CT images were denoised by using a convolutional neural network. Image quality was objectively quantified in two phantoms and a randomly selected subset of participants (10 participants; median age, 63.5 years; five men). Two radiologists scored PCD CT images relative to EID CT by using a five-point Likert scale to detect findings reflecting multiple myeloma. The scoring for the matched reconstruction series was blinded to scanner type. Reader-averaged scores were tested with the null hypothesis of equivalent visualization between EID and PCD. Results Twenty-seven participants (median age, 68 years; IQR, 61-72 years; 16 men) were included. The blinded assessment of 2-mm images demonstrated improvement in viewing lytic lesions, intramedullary lesions, fatty metamorphosis, and pathologic fractures for PCD CT versus EID CT ( < .05 for all comparisons). The 0.6-mm PCD CT images with convolutional neural network denoising also demonstrated improvement in viewing all four pathologic abnormalities and detected one or more lytic lesions in 21 of 27 participants compared with the 2-mm EID CT images ( < .001). Conclusion Ultra-high-resolution photon-counting detector CT improved the visibility of multiple myeloma lesions relative to energy-integrating detector CT. © RSNA, 2022
背景 光子计数探测器 (PCD) CT 和深度学习降噪技术与能量积分探测器 (EID) CT 相比，可在更低辐射剂量下提高空间分辨率。
目的 本研究旨在通过使用带深度学习降噪的 PCD CT 与常规 EID CT 进行比较，展示全身低剂量 CT 扫描中空间分辨率提高对多发性骨髓瘤诊断的影响。
材料与方法 本前瞻性研究于 2021 年 4 月至 7 月在一家学术医疗中心进行，纳入了接受全身 EID CT 扫描的成年参与者，然后使用 PCD CT 系统以匹配的辐射剂量（平均成人 8 mSv）进行超高分辨率扫描。EID CT 和 PCD CT 图像分别采用 Br44 和 Br64 内核以 2-mm 层厚重建，PCD CT 图像还分别采用 Br44 和 Br76 内核以 0.6-mm 层厚重建。较薄的 PCD CT 图像通过卷积神经网络进行降噪。在两个体模和随机选择的部分参与者（10 名参与者；中位年龄 63.5 岁；5 名男性）中对图像质量进行了客观量化。两名放射科医生使用 5 分制对 PCD CT 图像相对于 EID CT 的图像质量进行评分，以检测反映多发性骨髓瘤的发现。匹配重建系列的评分对扫描仪类型设盲。使用 EID 和 PCD 之间可视化等效的零假设对读者平均评分进行检验。
结果 27 名参与者（中位年龄 68 岁；IQR，61-72 岁；16 名男性）纳入研究。2-mm 图像的盲法评估显示，PCD CT 与 EID CT 相比，在观察溶骨性病变、骨髓内病变、脂肪变质和病理性骨折方面有所改善（所有比较的 P 值均<.05）。经卷积神经网络降噪的 0.6-mm PCD CT 图像在观察所有 4 种病理性异常方面也有所改善，与 2-mm EID CT 图像相比，在 27 名参与者中的 21 名中检测到一个或多个溶骨性病变（<.001）。
结论 与能量积分探测器 CT 相比，超高分辨率光子计数探测器 CT 提高了多发性骨髓瘤病变的可视性。
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