Sen Jia^{1,2}, Haifeng Wang^{1}, Xin Liu^{1}, Hairong Zheng^{1}, and Dong Liang^{1,2}

TGRAPPA acceleration alleviates the intense tradeoff between spatial and temporal resolutions for real-time cardiac cine imaging. However, it suffers from significant noise amplification due to ill-conditioned inverse reconstruction at high acceleration factors. A quadruple extended TGRAPPA reconstruction model is established to jointly utilize the additional spatial encoding capability of background phase and the high-order noise model by nonlinear kernel method. Prospective real-time cine experiments showed superior noise suppression of this non-iterative technique at 6-8X acceleration.

- Breuer FA, Kellman P, Griswold MA, Jakob PM. Dynamic Autocalibrated Parallel Imaging using Temporal GRAPPA (TGRAPPA). Magn Reson Med 2005; 53:981-985.
- Blaimer M, Heim M, Neumann D, Jakob PM, Kannengiesser SA, Breuer FA. Comparison of Phase-Constrained Parallel MRI Approaches: Analogies and Differences. Magn Reson Med 2016; 75:1086-1099.
- Blaimer M, Gutberlet M, Kellman P, Breuer FA, Kostler H, Griswold MA. Virtual Coil Concept for Improved Parallel MRI Employing Conjugate Symmetric Signals. Magn Reson Med 2009; 61:93-102.
- Chang Y, Liang D, Ying L. Nonlinear GRAPPA: A Kernel Approach to Parallel MRI Reconstruction. Magn Reson Med 2012; 68:730-740.
- Robson PM, Grant AK, Madhuranthakam AJ, Lattanzi R, Sodickson DK, McKenzie CA. Comprehensive Quantification of Signal-to-Noise Ratio and g-Factor for Image-Based and k-Space-Based Parallel Imaging Reconstructions. Magn Reson Med 2008; 60:895-907.

Figure 1. The schematic description of
proposed CarRace
algorithm for improved dynamic parallel imaging reconstruction. (a) Generating
nonlinear kernel mapped and phase conjugated autocalibration (ACS) data in
virtual coils. (b) Constructing a convolution kernel based on extended ACS data. (c)
Data synthesis formula using both physical and virtual coils for reconstructing
missing samples in physical coil.

Figure 2. The g-factor maps for adjacent
three frames of 6X accelerated real-time cine reconstructed by CarRace and
TGRAPPA.
Multiple kernel calibrations (depends on the acceleration factors) are needed
for CarRace due
to the shifted positions of known data between physical coil and conjugate
virtual coil. The g-factor maps
calculated by pseudo replica method varied slightly between adjacent frames
indicates that CarRace
achieves homogeneous noise behavior across the time dimension.

Figure 3. Comparing the reconstruction quality between traditional TGRAPPA, NL-, VCC-extended TGRAPPA and proposed CarRace methods on a 8X accelerated time-interleaved real-time cine dataset. The proposed virtual coils implementation of CarRace algorithm enables flexible switch between these algorithms. CarRace achieves the best visual reconstruction quality among these methods.

Figure 4. Prospectively comparing
proposed CarRace with
traditional TGRAPPA reconstruction at 4X, 6X and 8X acceleration rates for
time-interleaved real-time cine. The imaging parameters: TR/TE = 2.75/1.25 ms,
acquisition matrix Size = 192 x 124, FOV = 330 x 330 mm, 28 channel cardiac
coil on a 3T scanner.