In clinical brain SPECT, correction for photon attenuation in the patient is essential to obtain images which provide quantitative information on the regional activity concentration per unit volume (kBq.). This correction generally requires an attenuation map ( map) denoting the attenuation coefficient at each voxel which is often derived from a CT or MRI scan. However, such an additional scan is not always available and the method may suffer from registration errors. Therefore, we propose a SPECT-only-based strategy for map estimation that we apply to a stationary multi-pinhole clinical SPECT system (G-SPECT-I) for ^99mTc-HMPAO brain perfusion imaging. The method is based on the use of a convolutional neural network (CNN) and was validated with Monte Carlo simulated scans. Data acquired in list mode was used to employ the energy information of both primary and scattered photons to obtain information about the tissue attenuation as much as possible. Multiple SPECT reconstructions were performed from different energy windows over a large energy range. Locally extracted 4D SPECT patches (three spatial plus one energy dimension) were used as input for the CNN which was trained to predict the attenuation coefficient of the corresponding central voxel of the patch. Results show that Attenuation Correction using the Ground Truth maps (GT-AC) or using the CNN estimated maps (CNN-AC) achieve comparable accuracy. This was confirmed by a visual assessment as well as a quantitative comparison; the mean deviation from the GT-AC when using the CNN-AC is within 1.8% for the standardized uptake values in all brain regions. Therefore, our results indicate that a CNN-based method can be an automatic and accurate tool for SPECT attenuation correction that is independent of attenuation data from other imaging modalities or human interpretations about head contours.

在临床脑 SPECT 中，对患者的光子衰减进行校正对于获得提供有关每单位体积的区域活动浓度 (kBq. ) 的定量信息的图像至关重要。这种校正通常需要一个衰减图（map）来表示每个体素处的衰减系数，该衰减系数通常来自 CT 或 MRI 扫描。然而，这种额外的扫描并不总是可用的，并且该方法可能会出现注册错误。因此，我们提出了一种仅基于 SPECT 的地图估计策略，该策略应用于^99m的固定多针孔临床 SPECT 系统 (G-SPECT-I)Tc-HMPAO 脑灌注成像。该方法基于卷积神经网络 (CNN) 的使用，并通过蒙特卡洛模拟扫描进行了验证。以列表模式获取的数据用于利用初级和散射光子的能量信息，以尽可能多地获得有关组织衰减的信息。在大能量范围内从不同的能量窗口进行多次 SPECT 重建。局部提取的 4D SPECT 贴片（三个空间加一个能量维度）被用作 CNN 的输入，该 CNN 被训练来预测贴片相应中心体素的衰减系数。结果表明，使用地面实况图 (GT-AC) 或使用 CNN 估计的衰减校正地图（CNN-AC）达到了相当的准确性。目视评估和定量比较证实了这一点；对于所有大脑区域的标准化摄取值，使用 CNN-AC 时与 GT-AC 的平均偏差在 1.8% 以内。因此，我们的结果表明，基于 CNN 的方法可以成为一种自动和准确的 SPECT 衰减校正工具，它独立于来自其他成像模式的衰减数据或人类对头部轮廓的解释。