Nakashima Masahiro, Fukui Ryohei, Sugimoto Seiichiro, Iguchi Toshihiro
Division of Radiological Technology, Okayama University Hospital, 2-5-1 Shikatacho, Kitaku, Okayama, 700-8558, Japan.
Department of Radiological Technology, Faculty of Health Sciences, Okayama University, 2-5-1 Shikatacho, Kitaku, Okayama, 700-8558, Japan.
Radiol Phys Technol. 2025 Mar;18(1):47-57. doi: 10.1007/s12194-024-00853-3. Epub 2024 Oct 23.
We aimed to evaluate the image quality and diagnostic performance of chronic lung allograft dysfunction (CLAD) with lung ventilation single-photon emission computed tomography (SPECT) images acquired briefly using a convolutional neural network (CNN) in patients after lung transplantation and to explore the feasibility of short acquisition times. We retrospectively identified 93 consecutive lung-transplant recipients who underwent ventilation SPECT/computed tomography (CT). We employed a CNN to distinguish the images acquired in full time from those acquired in a short time. The image quality was evaluated using the structural similarity index (SSIM) loss and normalized mean square error (NMSE). The correlation between functional volume/morphological volume (F/M) ratios of full-time SPECT images and predicted SPECT images was evaluated. Differences in the F/M ratio were evaluated using Bland-Altman plots, and the diagnostic performance was compared using the area under the curve (AUC). The learning curve, obtained using MSE, converged within 100 epochs. The NMSE was significantly lower (P < 0.001) and the SSIM was significantly higher (P < 0.001) for the CNN-predicted SPECT images compared to the short-time SPECT images. The F/M ratio of full-time SPECT images and predicted SPECT images showed a significant correlation (r = 0.955, P < 0.0001). The Bland-Altman plot revealed a bias of -7.90% in the F/M ratio. The AUC values were 0.942 for full-time SPECT images, 0.934 for predicted SPECT images and 0.872 for short-time SPECT images. Our findings suggest that a deep-learning-based approach can significantly curtail the acquisition time of ventilation SPECT, while preserving the image quality and diagnostic accuracy for CLAD.
我们旨在评估在肺移植术后患者中，使用卷积神经网络（CNN）简短采集的肺通气单光子发射计算机断层扫描（SPECT）图像对慢性肺移植功能障碍（CLAD）的图像质量和诊断性能，并探讨短采集时间的可行性。我们回顾性纳入了93例连续接受通气SPECT/计算机断层扫描（CT）的肺移植受者。我们使用CNN区分全时采集的图像和短时间采集的图像。使用结构相似性指数（SSIM）损失和归一化均方误差（NMSE）评估图像质量。评估全时SPECT图像与预测SPECT图像的功能体积/形态体积（F/M）比值之间的相关性。使用Bland-Altman图评估F/M比值的差异，并使用曲线下面积（AUC）比较诊断性能。使用均方误差（MSE）获得的学习曲线在100个epoch内收敛。与短时间SPECT图像相比，CNN预测的SPECT图像的NMSE显著更低（P < 0.001），SSIM显著更高（P < 0.001）。全时SPECT图像与预测SPECT图像的F/M比值显示出显著相关性（r = 0.955，P < 0.0001）。Bland-Altman图显示F/M比值的偏差为- 7.90%。全时SPECT图像的AUC值为0.942，预测SPECT图像的AUC值为0.934，短时间SPECT图像的AUC值为0.872。我们的研究结果表明，基于深度学习的方法可以显著缩短通气SPECT的采集时间，同时保持CLAD的图像质量和诊断准确性。
