Multi-sequence magnetic resonance imaging (MRI) has found wide applications in both modern clinical studies and deep learning research. However, in clinical practice, it frequently occurs that one or more of the MRI sequences are missing due to different image acquisition protocols or contrast agent contraindications of patients, limiting the utilization of deep learning models trained on multi-sequence data. One promising approach is to leverage generative models to synthesize the missing sequences, which can serve as a surrogate acquisition. State-of-the-art methods tackling this problem are based on convolutional neural networks (CNN) which usually suffer from spectral biases, resulting in poor reconstruction of high-frequency fine details. In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation. The proposed model uses a multi-layer perceptron (MLP) instead of a CNN as the decoder for pixel-to-pixel mapping. Hence, each target image is represented as a neural field that is conditioned on the source image via shift modulation with a learned latent code. Experiments on BraTS 2018 and an in-house clinical dataset of vestibular schwannoma patients showed that the proposed method outperformed state-of-the-art methods for multi-sequence MRI translation both visually and quantitatively. Moreover, we conducted spectral analysis, showing that CoNeS was able to overcome the spectral bias issue common in conventional CNN models. To further evaluate the usage of synthesized images in clinical downstream tasks, we tested a segmentation network using the synthesized images at inference.

翻译：多序列磁共振成像（MRI）在现代临床研究和深度学习研究中均有广泛应用。然而，临床实践中常因图像采集协议差异或患者对造影剂存在禁忌症，导致一个或多个MRI序列缺失，从而限制了基于多序列数据训练的深度学习模型的应用。一种有前景的方法是借助生成模型合成缺失序列，作为替代性采集数据。当前解决该问题的最先进方法基于卷积神经网络（CNN），但这类网络通常存在频谱偏差，导致高频细节的恢复效果不佳。本文提出基于移位调制的条件神经场（CoNeS），该模型以体素坐标作为输入，学习目标图像的表示用于多序列MRI翻译。所提模型采用多层感知机（MLP）替代CNN作为像素级映射的解码器，每个目标图像被表示为神经场，通过移位调制与源图像的条件关联，并引入学习到的潜在编码。在BraTS 2018数据集及内部收集的听神经瘤患者临床数据集上的实验表明，所提方法在多序列MRI翻译任务中，无论从视觉效果还是量化指标上均优于当前最先进方法。此外，我们进行了频谱分析，证明CoNeS能克服传统CNN模型常见的频谱偏差问题。为进一步评估合成图像在临床下游任务中的应用价值，我们测试了推理时使用合成图像的分割网络性能。