论文标题
语义具有辅助模式间磁共振图像的多尺度重建
Semantic Features Aided Multi-Scale Reconstruction of Inter-Modality Magnetic Resonance Images
论文作者
论文摘要
由于多模式的MR图像（尤其是具有更长AQT的T2加权图像（T2WI）），虽然对疾病诊断有益，但由于多模式MR图像（尤其是T2加权图像（T2WI）的串联获取），实际上是不良的，这实际上是不希望的，这实际上是不受欢迎的。我们提出了一种基于深层网络的新型解决方案，以使用编码器decoder架构从T1W图像（T1WI）重建T2W图像。通过在两个正交方向上使用具有强度值和图像梯度的多通道输入，提出的学习具有语义特征。通过模块化训练对内置的编码器模型（SBM）内置的编码器模型（rm）以及域名模块（DAM）加强网络（DAM）。提出的网络通过可忽略不计的定性伪像和定量损失大大减少了总AQT（在大约1秒钟内重建一个体积）。该测试是在具有真实MR图像的公开数据集上进行的，并且建议的网络显示（约1DB）PSNR比SOTA增加了。
Long acquisition time (AQT) due to series acquisition of multi-modality MR images (especially T2 weighted images (T2WI) with longer AQT), though beneficial for disease diagnosis, is practically undesirable. We propose a novel deep network based solution to reconstruct T2W images from T1W images (T1WI) using an encoder-decoder architecture. The proposed learning is aided with semantic features by using multi-channel input with intensity values and gradient of image in two orthogonal directions. A reconstruction module (RM) augmenting the network along with a domain adaptation module (DAM) which is an encoder-decoder model built-in with sharp bottleneck module (SBM) is trained via modular training. The proposed network significantly reduces the total AQT with negligible qualitative artifacts and quantitative loss (reconstructs one volume in approximately 1 second). The testing is done on publicly available dataset with real MR images, and the proposed network shows (approximately 1dB) increase in PSNR over SOTA.
