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：摘要：多序列磁共振成像（MRI）在当代临床研究和深度学习研究中均有广泛应用。然而在临床实践中，由于图像采集协议差异或患者对造影剂的禁忌症，常出现一个或多个MRI序列缺失的情况，这限制了基于多序列数据训练的深度学习模型的可用性。一种有前景的方法是借助生成模型合成缺失序列，作为替代性采集方案。当前解决该问题的最先进方法基于卷积神经网络（CNN），但这类模型通常存在频谱偏差问题，导致高频细节重建效果不佳。本文提出基于移位调制的条件神经场（CoNeS），该模型以体素坐标为输入，通过学习目标图像表征实现多序列MRI转换。该模型采用多层感知器（MLP）替代CNN作为解码器进行像素级映射。因此，每个目标图像被表示为神经场，通过经过学习的潜在编码进行移位调制，以源图像为条件。在BraTS 2018数据集和内部前庭神经鞘瘤患者临床数据集上的实验表明，所提方法在多序列MRI转换中无论视觉质量还是量化指标均优于现有最先进方法。此外，通过频谱分析证实CoNeS能够克服传统CNN模型常见的频谱偏差问题。为评估合成图像在临床下游任务中的应用价值，我们在推理阶段使用合成图像测试了分割网络性能。