PDF(Pubmed)
Abstract：
UNASSIGNED: To develop the following three attenuation correction (AC) methods for brain 18F-fluorodeoxyglucose-positron emission tomography (PET), using deep learning, and to ascertain their precision levels: (i) indirect method; (ii) direct method; and (iii) direct and high-resolution correction (direct+HRC) method.
UNASSIGNED: We included 53 patients who underwent cranial magnetic resonance imaging (MRI) and computed tomography (CT) and 27 patients who underwent cranial MRI, CT, and PET. After fusion of the magnetic resonance, CT, and PET images, resampling was performed to standardize the field of view and matrix size and prepare the data set. In the indirect method, synthetic CT (SCT) images were generated, whereas in the direct and direct+HRC methods, a U-net structure was used to generate AC images. In the indirect method, attenuation correction was performed using SCT images generated from MRI findings using U-net instead of CT images. In the direct and direct+HRC methods, AC images were generated directly from non-AC images using U-net, followed by image evaluation. The precision levels of AC images generated using the indirect and direct methods were compared based on the normalized mean squared error (NMSE) and structural similarity (SSIM).
UNASSIGNED: Visual inspection revealed no difference between the AC images prepared using CT-based attenuation correction and those prepared using the three methods. The NMSE increased in the order indirect, direct, and direct+HRC methods, with values of 0.281×10-3, 4.62×10-3, and 12.7×10-3, respectively. Moreover, the SSIM of the direct+HRC method was 0.975.
UNASSIGNED: The direct+HRC method enables accurate attenuation without CT exposure and high-resolution correction without dedicated correction programs.
UNASSIGNED: We included 53 patients who underwent cranial magnetic resonance imaging (MRI) and computed tomography (CT) and 27 patients who underwent cranial MRI, CT, and PET. After fusion of the magnetic resonance, CT, and PET images, resampling was performed to standardize the field of view and matrix size and prepare the data set. In the indirect method, synthetic CT (SCT) images were generated, whereas in the direct and direct+HRC methods, a U-net structure was used to generate AC images. In the indirect method, attenuation correction was performed using SCT images generated from MRI findings using U-net instead of CT images. In the direct and direct+HRC methods, AC images were generated directly from non-AC images using U-net, followed by image evaluation. The precision levels of AC images generated using the indirect and direct methods were compared based on the normalized mean squared error (NMSE) and structural similarity (SSIM).
UNASSIGNED: Visual inspection revealed no difference between the AC images prepared using CT-based attenuation correction and those prepared using the three methods. The NMSE increased in the order indirect, direct, and direct+HRC methods, with values of 0.281×10-3, 4.62×10-3, and 12.7×10-3, respectively. Moreover, the SSIM of the direct+HRC method was 0.975.
UNASSIGNED: The direct+HRC method enables accurate attenuation without CT exposure and high-resolution correction without dedicated correction programs.
摘要：
■为开发以下三种用于大脑18F-氟脱氧葡萄糖-正电子发射断层扫描（PET）的衰减校正（AC）方法，使用深度学习，并确定其精度水平：（i）间接方法；（ii）直接方法；（iii）直接和高分辨率校正（直接HRC）方法。
我们包括53例接受头颅磁共振成像（MRI）和计算机断层扫描（CT）的患者和27例接受头颅MRI的患者，CT,和PET。磁共振融合后，CT,和PET图像，进行重采样以标准化视野和矩阵大小并准备数据集.在间接方法中，生成合成CT(SCT)图像，而在直接和直接+HRC方法中，使用U型网络结构生成AC图像。在间接方法中，使用U-net而不是CT图像,根据MRI检查结果生成的SCT图像进行衰减校正.在直接和直接+HRC方法中，使用U-net直接从非AC图像生成AC图像，其次是图像评价。基于归一化均方误差（NMSE）和结构相似性（SSIM），比较了使用间接和直接方法生成的AC图像的精度水平。
■视觉检查显示使用基于CT的衰减校正制备的AC图像与使用三种方法制备的AC图像之间没有差异。NMSE按间接顺序增加，直接，和直接+HRC方法，值分别为0.281×10-3、4.62×10-3和12.7×10-3。此外，直接+HRC法的SSIM为0.975。
■直接+HRC方法无需CT曝光即可实现精确衰减，无需专用校正程序即可实现高分辨率校正。
我们包括53例接受头颅磁共振成像（MRI）和计算机断层扫描（CT）的患者和27例接受头颅MRI的患者，CT,和PET。磁共振融合后，CT,和PET图像，进行重采样以标准化视野和矩阵大小并准备数据集.在间接方法中，生成合成CT(SCT)图像，而在直接和直接+HRC方法中，使用U型网络结构生成AC图像。在间接方法中，使用U-net而不是CT图像,根据MRI检查结果生成的SCT图像进行衰减校正.在直接和直接+HRC方法中，使用U-net直接从非AC图像生成AC图像，其次是图像评价。基于归一化均方误差（NMSE）和结构相似性（SSIM），比较了使用间接和直接方法生成的AC图像的精度水平。
■视觉检查显示使用基于CT的衰减校正制备的AC图像与使用三种方法制备的AC图像之间没有差异。NMSE按间接顺序增加，直接，和直接+HRC方法，值分别为0.281×10-3、4.62×10-3和12.7×10-3。此外，直接+HRC法的SSIM为0.975。
■直接+HRC方法无需CT曝光即可实现精确衰减，无需专用校正程序即可实现高分辨率校正。
