IMJENSE: Scan-specific Implicit Representation for Joint Coil Sensitivity and Image Estimation in Parallel MRI

IF 8.9 1区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS IEEE Transactions on Medical Imaging Pub Date : 2023-11-21 DOI:10.48550/arXiv.2311.12892
Rui-jun Feng, Qing Wu, Jie Feng, Huajun She, Chunlei Liu, Yuyao Zhang, Hongjiang Wei
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Abstract

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× and 6× 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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IMJENSE:用于并行磁共振成像中关节线圈灵敏度和图像估计的特定扫描隐式表示法
并行成像是加速磁共振成像(MRI)数据采集的常用技术。从数学上讲,并行磁共振成像重建可表述为一个将稀疏采样的 k 空间测量值与所需磁共振成像图像相关联的逆问题。尽管许多现有的重建算法都取得了成功,但要从高度缩小的 k 空间测量数据中可靠地重建出高质量的图像,仍然是一项挑战。最近,隐式神经表征作为一种强大的范例出现了,它能利用部分获取数据的内部信息和物理特性生成所需的对象。在这项研究中,我们引入了 IMJENSE,这是一种基于特定扫描的隐式神经表征方法,用于改进并行 MRI 重建。具体来说,基础 MRI 图像和线圈灵敏度被建模为空间坐标的连续函数,分别由神经网络和多项式参数化。神经网络中的权重和多项式中的系数同时直接从稀疏获取的 k 空间测量数据中学习,而不需要完全采样的地面实况数据进行训练。得益于强大的连续表示法以及对磁共振成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 比无监督校准和基于校准的深度学习方法更加稳定。结果表明,在二维笛卡尔采集中,IMJENSE 仅用 4 或 8 条校准线就能稳健地重建以 5 倍和 6 倍加速度采集的图像,这相当于 22.0% 和 19.5% 的欠采样率。高质量的结果和扫描特异性使所提出的方法有望进一步加速并行磁共振成像的数据采集。
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来源期刊
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging 医学-成像科学与照相技术
CiteScore
21.80
自引率
5.70%
发文量
637
审稿时长
5.6 months
期刊介绍: The IEEE Transactions on Medical Imaging (T-MI) is a journal that welcomes the submission of manuscripts focusing on various aspects of medical imaging. The journal encourages the exploration of body structure, morphology, and function through different imaging techniques, including ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods. It also promotes contributions related to cell and molecular imaging, as well as all forms of microscopy. T-MI publishes original research papers that cover a wide range of topics, including but not limited to novel acquisition techniques, medical image processing and analysis, visualization and performance, pattern recognition, machine learning, and other related methods. The journal particularly encourages highly technical studies that offer new perspectives. By emphasizing the unification of medicine, biology, and imaging, T-MI seeks to bridge the gap between instrumentation, hardware, software, mathematics, physics, biology, and medicine by introducing new analysis methods. While the journal welcomes strong application papers that describe novel methods, it directs papers that focus solely on important applications using medically adopted or well-established methods without significant innovation in methodology to other journals. T-MI is indexed in Pubmed® and Medline®, which are products of the United States National Library of Medicine.
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