Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are widely used detection technology in screening, diagnosis, and image-guided therapy for both clinical and research. However, CT imposes ionizing radiation to patients during acquisition. Compared to CT, MRI is much safer and does not involve any radiations, but it is more expensive and has prolonged acquisition time. Therefore, it is necessary to estimate one modal image from another given modal image of the same subject for the case of radiotherapy planning. Considering that there is currently no bidirectional prediction model between MRI and CT images, we propose a bidirectional prediction by using multi-generative multi-adversarial nets (BPGAN) for the prediction of any modal from another modal image in paired and unpaired fashion. In BPGAN, two nonlinear maps are learned by projecting same pathological features from one domain to another with cycle consistency strategy. Technologically, pathological prior information is introduced to constrain the feature generation to attack the potential risk of pathological variance, and edge retention metric is adopted to preserve geometrically distortion and anatomical structure. Algorithmically, spectral normalization is designed to control the performance of discriminator and to make predictor learn better and faster, and the localization is proposed to impose regularizer on predictor to reduce generalization error. Experimental results show that BPGAN generates better predictions than recently state-of-the-art methods. Specifically, BPGAN achieves average increment of MAE 33.2% and 37.4%, and SSIM 24.5% and 44.6% on two baseline datasets than comparisons.

中文翻译：

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BPGAN：使用具有频谱归一化和定位功能的多生成多对抗网络进行双向CT到MRI预测。

磁共振成像（MRI）和计算机断层扫描（CT）被广泛用于临床和研究的筛查，诊断和图像引导治疗中的检测技术。但是，CT在采集过程中会向患者施加电离辐射。与CT相比，MRI更加安全并且不涉及任何辐射，但是它更昂贵并且采集时间更长。因此，对于放射治疗计划，有必要从同一受试者的另一个给定模态图像中估计一个模态图像。考虑到目前在MRI和CT图像之间没有双向预测模型，我们提出使用多生成多对抗网（BPGAN）进行双向预测，以成对和不成对方式预测来自另一个模态图像的任何模态。在BPGAN中，通过使用周期一致性策略将相同的病理特征从一个域投影到另一个域，可以学习两个非线性图。从技术上讲，引入了病理先验信息以约束特征生成以攻击潜在的病理变异风险，并采用边缘保留度量来保留几何变形和解剖结构。在算法上，频谱归一化被设计为控制鉴别器的性能，并使预测器更好，更快地学习，并且提出了局部化以在预测器上施加正则化器以减少泛化误差。实验结果表明，BPGAN比最近的最新方法产生更好的预测。具体而言，BPGAN实现了MAE的平均增量为33.2％和37.4％，SSIM为24.5％和44。