Improved simultaneous multislice cardiac MRI using readout concatenated k‐space SPIRiT (ROCK‐SPIRiT)

Demirel, Ömer Burak; Weingärtner, Sebastian; Moeller, Steen; Akçakaya, Mehmet

doi:10.1002/mrm.28680

Cited by 13 publications

(13 citation statements)

References 58 publications

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“…Recently, an SMS‐balance steady state free precession compressed sensing method was proposed with spatial and temporal wavelet regularizations 12 ; however, it did not use through‐plane coil information in the MB data consistency term. Another method, readout concatenated k‐space‐SPIRiT, based on readout concatenation and coil self‐consistency, has not been combined with joint sparsity 41 …”

Section: Discussionmentioning

confidence: 99%

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A Slice‐Low‐Rank Plus Sparse (slice‐L + S) Reconstruction Method for k‐t Undersampled Multiband First‐Pass Myocardial Perfusion MRI

Sun

Wang

Bilchick

et al. 2022

Magnetic Resonance in Med

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Purpose The synergistic use of k‐t undersampling and multiband (MB) imaging has the potential to provide extended slice coverage and high spatial resolution for first‐pass perfusion MRI. The low‐rank plus sparse (L + S) model has shown excellent performance for accelerating single‐band (SB) perfusion MRI. Methods A MB data consistency method employing ESPIRiT maps and through‐plane coil information was developed. This data consistency method was combined with the temporal L + S constraint to form the slice‐L + S method. Slice‐L + S was compared to SB L + S and the sequential operations of split slice‐GRAPPA and SB L + S (seq‐SG‐L + S) using synthetic data formed from multislice SB images. Prospectively k‐t undersampled MB data were also acquired and reconstructed using seq‐SG‐L + S and slice‐L + S. Results Using synthetic data with total acceleration rates of 6–12, slice‐L + S outperformed SB L + S and seq‐SG‐L + S (N = 7 subjects) with respect to normalized RMSE and the structural similarity index (P < 0.05 for both). For the specific case with MB factor = 3 and rate 3 undersampling, or for SB imaging with rate 9 undersampling (N = 7 subjects), the normalized RMSE values were 0.037 ± 0.007, 0.042 ± 0.005, and 0.031 ± 0.004; and the structural similarity index values were 0.88 ± 0.03, 0.85 ± 0.03, and 0.89 ± 0.02 for SB L + S, seq‐SG‐L + S, and slice‐L + S, respectively (P < 0.05 for both). For prospectively undersampled MB data, slice‐L + S provided better image quality than seq‐SG‐L + S for rate 6 (N = 7) and rate 9 acceleration (N = 7) as scored by blinded experts. Conclusion Slice‐L + S outperformed SB‐L + S and seq‐SG‐L + S and provides 9 slice coverage of the left ventricle with a spatial resolution of 1.5 mm × 1.5 mm with good image quality.

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Section: Discussionmentioning

confidence: 99%

“…Another method, readout concatenated k-space-SPIRiT, based on readout concatenation and coil self-consistency, has not been combined with joint sparsity. 41…”

Section: Comparison With Other K-t Mb Reconstruction Modelsmentioning

confidence: 99%

A Slice‐Low‐Rank Plus Sparse (slice‐L + S) Reconstruction Method for k‐t Undersampled Multiband First‐Pass Myocardial Perfusion MRI

Sun

Wang

Bilchick

et al. 2022

Magnetic Resonance in Med

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“…The concatenated unaccelerated images are then used for estimating the interpolation rule, e.g. linear convolutions in GRAPPA, in this extended space ( Demirel et al, 2021 ; Moeller et al, 2010 ). In order to reduce overfitting issues for the GRAPPA-type convolution from a single calibration frame, additional calibration data was generated from the baseline SMS/MB = 8 reconstructions from the first 8 time frames.…”

Section: Methodsmentioning

confidence: 99%

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

et al. 2022

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“…where y Ω SMS ∈ C P is the acquired SMS k-space, Ω is the inplane undersampling pattern, S is number of simultaneously excited slices, x i ∈ C M ×N is the i th slice image, E Ω i : C M×N → C P is the multi-coil encoding operator of the i th slice, and n is measurement noise. The notation can be condensed by letting x SM S be the concatenation of the individual slices {x i } S i=1 along the readout direction [4], [16], and…”

Section: A Self-supervised DL Reconstruction For Sms Fmrimentioning

confidence: 99%

20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction

Demirel¹,

Dowdle²,

Moeller³

et al. 2021

Preprint

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High spatial and temporal resolution across the whole brain is essential to accurately resolve neural activities in fMRI. Therefore, accelerated imaging techniques target improved coverage with high spatio-temporal resolution. Simultaneous multi-slice (SMS) imaging combined with in-plane acceleration are used in large studies that involve ultrahigh field fMRI, such as the Human Connectome Project. However, for even higher acceleration rates, these methods cannot be reliably utilized due to aliasing and noise artifacts. Deep learning (DL) reconstruction techniques have recently gained substantial interest for improving highly-accelerated MRI. Supervised learning of DL reconstructions generally requires fully-sampled training datasets, which is not available for high-resolution fMRI studies. To tackle this challenge, self-supervised learning has been proposed for training of DL reconstruction with only undersampled datasets, showing similar performance to supervised learning. In this study, we utilize a self-supervised physics-guided DL reconstruction on a 5-fold SMS and 4-fold in-plane accelerated 7T fMRI data. Our results show that our self-supervised DL reconstruction produce high-quality images at this 20-fold acceleration, substantially improving on existing methods, while showing similar functional precision and temporal effects in the subsequent analysis compared to a standard 10-fold accelerated acquisition.

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Improved simultaneous multislice cardiac MRI using readout concatenated k‐space SPIRiT (ROCK‐SPIRiT)

Cited by 13 publications

References 58 publications

A Slice‐Low‐Rank Plus Sparse (slice‐L + S) Reconstruction Method for k‐t Undersampled Multiband First‐Pass Myocardial Perfusion MRI

A Slice‐Low‐Rank Plus Sparse (slice‐L + S) Reconstruction Method for k‐t Undersampled Multiband First‐Pass Myocardial Perfusion MRI

Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning

20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction

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