IMJENSE: Scan-specific Implicit Representation for Joint Coil
  Sensitivity and Image Estimation in Parallel MRI
Ruimin Feng Qing Wu Jie Feng Huajun She Chunlei Liu Yuyao Zhang Hongjiang Wei
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.