论文标题
通过隐式神经表示，一种扫描特定的无监督方法用于平行MRI重建
A scan-specific unsupervised method for parallel MRI reconstruction via implicit neural representation
论文作者
论文摘要
平行成像是一种广泛使用的技术，用于加速磁共振成像（MRI）。但是，当前方法在从高度不足的K空间数据中重建无伪影的MRI图像方面仍然表现较差。最近，隐式神经表示（INR）已成为学习对象内部连续性的新的深度学习范式。在这项研究中，我们通过INR进行了平行的MRI重建。 MRI图像被建模为空间坐标的连续函数。该功能通过神经网络参数化，并直接从测量的K空间本身中学习，而无需其他完全采样的高质量训练数据。受益于INR提供的强大连续表示形式，该提出的方法通过抑制混叠的伪像和噪声来优于现有方法，尤其是在较高的加速度和较小尺寸的自动校准信号时。高质量的结果和扫描特异性使提出的方法具有进一步加速并行MRI的数据获取的潜力。
Parallel imaging is a widely-used technique to accelerate magnetic resonance imaging (MRI). However, current methods still perform poorly in reconstructing artifact-free MRI images from highly undersampled k-space data. Recently, implicit neural representation (INR) has emerged as a new deep learning paradigm for learning the internal continuity of an object. In this study, we adopted INR to parallel MRI reconstruction. The MRI image was modeled as a continuous function of spatial coordinates. This function was parameterized by a neural network and learned directly from the measured k-space itself without additional fully sampled high-quality training data. Benefitting from the powerful continuous representations provided by INR, the proposed method outperforms existing methods by suppressing the aliasing artifacts and noise, especially at higher acceleration rates and smaller sizes of the auto-calibration signals. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.
