Profile‐encoding reconstruction for multiple‐acquisition balanced steady‐state free precession imaging

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Purpose: To develop a rapid imaging framework for balanced steady-state free precession (bSSFP) that jointly reconstructs undersampled data (by a factor of R) across multiple coils (D) and multiple acquisitions (N). To devise a multi-acquisition coil compression technique for improved computational efficiency. Methods:The bSSFP image for a given coil and acquisition is modeled to be modulated by a coil sensitivity and a bSSFP profile. The proposed reconstruction by calibration over tensors (ReCat) recovers missing data by tensor interpolation over the coil and acquisition dimensions. Coil compression is achieved using a new method based on multilinear singular value decomposition (MLCC). ReCat is compared with iterative self-consistent parallel imaging (SPIRiT) and profile encoding (PE-SSFP) reconstructions.Results: Compared to parallel imaging or profile-encoding methods, ReCat attains sensitive depiction of high-spatial-frequency information even at higher R. In the brain, ReCat improves peak SNR (PSNR) by 1.1±1.0 dB over SPIRiT and by 0.9±0.3 dB over PE-SSFP (mean±std across subjects; average for N=2-8, R=8-16). Furthermore, reconstructions based on MLCC achieve 0.8±0.6 dB higher PSNR compared to those based on geometric coil compression (GCC) (average for N=2-8, R=4-16). Conclusion:ReCat is a promising acceleration framework for banding-artifact-free bSSFP imaging with high image quality; and MLCC offers improved computational efficiency for tensor-based reconstructions.

S_{n, d} (r) = P_{n} (r) S_{d} (r)

where

S_{d} (r)

denotes the coil image devoid of bSSFP‐profile modulation.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, we prescribed an interpolation kernel that covered a 11 × 11 neighborhood of k‐space samples as in . The regularization weight β was varied in the range

(0, 0.2]

.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

α = 60^{°}

(flip angle), TR/TE = 10.0/5.0 ms, and

Δ ϕ

π \frac{[0 : 1 : (N - 1)]}{N}

.…”

Section: Methodsmentioning

confidence: 99%

\approx

6–8 compared to conventional PI and CS reconstructions.…”

Section: Introductionmentioning

confidence: 99%

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Reconstruction by calibration over tensors for multi‐coil multi‐acquisition balanced SSFP imaging

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JVC-GRAPPA takes advantage of additional spatial encoding from phase information and image similarity, and employs different sampling patterns across acquisitions. J-LORAKS achieves a more parsimonious low-rank representation of local k-space by considering multiple images as additional coils. Both approaches provide dramatic improvement in artifact and noise mitigation over conventional single-contrast parallel imaging reconstruction. Magn Reson Med 80:619-632, 2018. © 2018 International Society for Magnetic Resonance in Medicine.