Purpose: Different Magnetic resonance imaging (MRI) modalities of the same anatomical structure are required to present different pathological information from the physical level for diagnostic needs. However, it is often difficult to obtain full-sequence MRI images of patients owing to limitations such as time consumption and high cost. The purpose of this work is to develop an algorithm for target MRI sequences prediction with high accuracy, and provide more information for clinical diagnosis. Methods: We propose a deep learning based multi-modal computing model for MRI synthesis with feature disentanglement strategy. To take full advantage of the complementary information provided by different modalities, multi-modal MRI sequences are utilized as input. Notably, the proposed approach decomposes each input modality into modality-invariant space with shared information and modality-specific space with specific information, so that features are extracted separately to effectively process the input data. Subsequently, both of them are fused through the adaptive instance normalization (AdaIN) layer in the decoder. In addition, to address the lack of specific information of the target modality in the test phase, a local adaptive fusion (LAF) module is adopted to generate a modality-like pseudo-target with specific information similar to the ground truth. Results: To evaluate the synthesis performance, we verify our method on the BRATS2015 dataset of 164 subjects. The experimental results demonstrate our approach significantly outperforms the benchmark method and other state-of-the-art medical image synthesis methods in both quantitative and qualitative measures. Compared with the pix2pixGANs method, the PSNR improves from 23.68 to 24.8. Conclusion: The proposed method could be effective in prediction of target MRI sequences, and useful for clinical diagnosis and treatment.

中文翻译：

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基于深度学习的具有特征分解的多模态计算的MRI图像合成

目的：需要使用具有相同解剖结构的不同磁共振成像（MRI）方式，才能从物理水平上呈现不同的病理信息，以满足诊断需求。然而，由于诸如时间消耗和高成本的限制，通常难以获得患者的全序列MRI图像。这项工作的目的是开发一种高精度的目标MRI序列预测算法，并为临床诊断提供更多信息。方法：我们提出了一种基于深度学习的多模态计算模型，用于特征分解策略的MRI合成。为了充分利用不同模态提供的补充信息，将多模态MRI序列用作输入。尤其，该方法将每个输入模态分解为具有共享信息的模态不变空间和具有特定信息的模态特定空间，以便分别提取特征以有效地处理输入数据。随后，它们都通过解码器中的自适应实例规范化（AdaIN）层进行融合。另外，为了解决测试阶段目标模态缺乏特定信息的问题，采用了局部自适应融合（LAF）模块来生成具有类似于地面真相的特定信息的类似模态的伪目标。结果：为了评估综合性能，我们在BRATS2015数据集中的164名受试者中验证了我们的方法。实验结果表明，在定量和定性方面，我们的方法均明显优于基准方法和其他最新医学图像合成方法。与pix2pixGANs方法相比，PSNR从23.68提高到24.8。结论：该方法可有效预测目标MRI序列，对临床诊断和治疗具有指导意义。