Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, University of Leeds, Leeds, UK; Leeds Institute for Cardiovascular and Metabolic Medicine (LICAMM), School of Medicine, University of Leeds, Leeds, UK.
Queen Square Institute of Neurology, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
Med Image Anal. 2021 Jan;67:101812. doi: 10.1016/j.media.2020.101812. Epub 2020 Oct 2.
Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes. In I2-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold, conditioned on intensity features through a generator net. The generator comprises residual blocks with normalisation layers that are modulated with auxiliary slice information, enabling propagation of fine details through the network. In addition, a multi-scale discriminator was implemented, along with a discriminator-based feature matching loss, to further enhance performance and encourage the synthesis of visually realistic slices. Experimental results show that our method achieves significant improvements over the state-of-the-art, in missing slice imputation for CMR, with an average SSIM of 0.872. Linear regression analysis yields good agreement between reference and imputed CMR images for all cardiac measurements, with correlation coefficients of 0.991 for left ventricular volume, 0.977 for left ventricular mass and 0.961 for right ventricular volume.
准确的心室容积测量是评估正常/异常心脏功能的主要指标，这取决于心脏磁共振（CMR）容积是否完整。然而，由于呼吸或运动鬼影、混叠、振铃和 CMR 序列中的信号丢失等图像伪影的存在，导致部分切片缺失或无法使用，这极大地阻碍了心脏解剖和功能定量的准确性，而在人群成像中，这些问题的恢复方法还不够完善。在这项工作中，我们提出了一种新的稳健方法，即图像插补生成对抗网络（I2-GAN），以学习心脏短轴（SAX）切片中缺失信息附近的关键特征，并将其用作条件变量来推断查询容积中缺失的切片。在 I2-GAN 中，首先通过回归网络将切片映射到具有位置特征的潜在向量。然后，通过生成器网络将与所需位置对应的潜在向量投影到切片流形上，并通过强度特征进行条件处理。生成器由具有归一化层的残差块组成，这些块通过辅助切片信息进行调制，从而使网络能够传播精细的细节。此外，还实现了一个多尺度鉴别器，并结合基于鉴别器的特征匹配损失，以进一步提高性能，并鼓励合成具有真实视觉效果的切片。实验结果表明，与最先进的方法相比，我们的方法在 CMR 缺失切片插补方面取得了显著的改进，平均 SSIM 为 0.872。线性回归分析表明，参考和插补 CMR 图像之间的所有心脏测量值都具有很好的一致性，左心室容积的相关系数为 0.991，左心室质量的相关系数为 0.977，右心室容积的相关系数为 0.961。
