IMJENSE：用于并行MRI中联合线圈灵敏度和图像估计的扫描特定隐式表示
IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI
AI总结：
提出IMJENSE，一种基于扫描特定隐式神经表示的并行MRI重建方法，通过神经网络和多项式联合估计MRI图像与线圈灵敏度，在极有限校准数据下实现高倍加速的稳健重建。
摘要：
并行成像是一种常用于加速磁共振成像（MRI）数据采集的技术。在数学上，并行MRI重建可以表述为一个将稀疏采样的k空间测量值与所需MRI图像联系起来的逆问题。尽管许多现有的重建算法取得了成功，但从高度减少的k空间测量值中可靠地重建高质量图像仍然是一个挑战。最近，隐式神经表示作为一种强大的范式出现，利用部分采集数据的内部信息和物理特性来生成所需对象。在本研究中，我们引入了IMJENSE，这是一种基于扫描特定隐式神经表示的方法，用于改进并行MRI重建。具体而言，将底层的MRI图像和线圈灵敏度分别建模为空间坐标的连续函数，并由神经网络和多项式进行参数化。网络中的权重和多项式中的系数直接从稀疏采集的k空间测量值中同时学习，无需完全采样的真值数据进行训练。得益于强大的连续表示以及MRI图像和线圈灵敏度的联合估计，IMJENSE优于传统的图像或k空间域重建算法。在校准数据极其有限的情况下，IMJENSE比有监督的无校准和基于校准的深度学习方法更稳定。结果表明，IMJENSE能够稳健地重建在2D笛卡尔采集中仅使用4或8条校准线、对应于22.0%和19.5%欠采样率的5倍和6倍加速采集的图像。高质量的结果和扫描特异性使得该方法具有进一步加速并行MRI数据采集的潜力。
英文摘要：
Parallel imaging is a commonly used technique to accelerate magnetic resonance imaging (MRI) data acquisition. Mathematically, parallel MRI reconstruction can be formulated as an inverse problem relating the sparsely sampled k-space measurements to the desired MRI image. Despite the success of many existing reconstruction algorithms, it remains a challenge to reliably reconstruct a high-quality image from highly reduced k-space measurements. Recently, implicit neural representation has emerged as a powerful paradigm to exploit the internal information and the physics of partially acquired data to generate the desired object. In this study, we introduced IMJENSE, a scan-specific implicit neural representation-based method for improving parallel MRI reconstruction. Specifically, the underlying MRI image and coil sensitivities were modeled as continuous functions of spatial coordinates, parameterized by neural networks and polynomials, respectively. The weights in the networks and coefficients in the polynomials were simultaneously learned directly from sparsely acquired k-space measurements, without fully sampled ground truth data for training. Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms. With extremely limited calibration data, IMJENSE is more stable than supervised calibrationless and calibration-based deep-learning methods. Results show that IMJENSE robustly reconstructs the images acquired at 5$\mathbf{\times}$ and 6$\mathbf{\times}$ accelerations with only 4 or 8 calibration lines in 2D Cartesian acquisitions, corresponding to 22.0% and 19.5% undersampling rates. The high-quality results and scanning specificity make the proposed method hold the potential for further accelerating the data acquisition of parallel MRI.
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