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.
翻译:目的:同一解剖结构的磁共振成像(MRI)模式不同,需要从物理层面提供不同病理信息,以提供诊断需要;然而,由于时间消耗和高成本等限制,往往难以获得患者的全序列磁共振成像;这项工作的目的是为目标磁共振序列预测开发一个算法,高精度,并为临床诊断提供更多信息。方法:我们提议了基于深层次学习的多模式计算模型,用于与特征脱钩战略的混合合成。为了充分利用不同模式提供的补充信息,将多模式MRI序列用作投入。值得注意的是,拟议的方法将每种输入模式与共享的信息和特定模式空间分离,从而将特性分离,以便有效地处理投入数据数据。随后,这两种方法都可以通过调适性实例(AdIN)在解析过程中进行整合。此外,为了解决测试阶段缺乏目标模式的具体信息问题,将多模式MRI序列序列序列序列用作投入。