The problem of how to assess cross-modality medical image synthesis has been largely unexplored. The most used measures like PSNR and SSIM focus on analyzing the structural features but neglect the crucial lesion location and fundamental k-space speciality of medical images. To overcome this problem, we propose a new metric K-CROSS to spur progress on this challenging problem. Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location, together with a tumor encoder for representing features, such as texture details and brightness intensities. To further reflect the frequency-specific information from the magnetic resonance imaging principles, both k-space features and vision features are obtained and employed in our comprehensive encoders with a frequency reconstruction penalty. The structure-shared encoders are designed and constrained with a similarity loss to capture the intrinsic common structural information for both modalities. As a consequence, the features learned from lesion regions, k-space, and anatomical structures are all captured, which serve as our quality evaluators. We evaluate the performance by constructing a large-scale cross-modality neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist judgments. Extensive experiments demonstrate that the proposed method outperforms other metrics, especially in comparison with the radiologists on NIRPS.

翻译：跨模态医学图像合成的评估问题在很大程度上尚未被探索。最常用的度量指标如PSNR和SSIM侧重于分析结构特征，但忽略了医学图像中关键的病灶位置和基本的k空间特性。为克服这一问题，我们提出了一种新的度量指标K-CROSS，以推动这一挑战性问题的进展。具体而言，K-CROSS使用预训练的多模态分割网络预测病灶位置，并结合肿瘤编码器表示纹理细节和亮度强度等特征。为进一步反映磁共振成像原理中频率特定信息，我们在综合编码器中获取并使用了k空间特征和视觉特征，并引入了频率重建惩罚项。设计了结构共享编码器，并通过相似性损失进行约束，以捕捉两种模态共有的内在通用结构信息。因此，从病灶区域、k空间和解剖结构中学习到的特征均被捕获，作为我们的质量评估器。我们通过构建包含6000名放射科医生判断的大规模跨模态神经影像感知相似性（NIRPS）数据集来评估性能。大量实验表明，所提方法优于其他度量指标，尤其在NIRPS上与放射科医生的比较中表现突出。