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.
翻译:平行成像是一种广泛使用的加速磁共振成像(MRI)技术。然而,目前的方法在从高度低采率的K-空间数据中重建无文物MRI图像方面仍然表现不佳。最近,隐性神经表征(INR)已成为学习一个物体内部连续性的新的深层学习模式。在这项研究中,我们采用了IRR以平行的MRI重建。MRI图像建模为空间坐标的连续功能。该功能由神经网络作为参数,直接从测量的 k-空间本身学习,而没有经过充分抽样的高质量培训数据。从IRR提供的强有力的连续演示中得益,拟议方法通过抑制别名的文物和噪音,特别是在加速率更高和自动校准信号较小尺寸的情况下,超越了现有方法。高质量结果和扫描特性使得拟议方法有可能进一步加速平行MRI的数据获取。