Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI T<sub>1</sub> Mapping

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

doi:10.23919/eusipco.2019.8903058

Cited by 6 publications

(10 citation statements)

References 16 publications

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“…Accordingly, the quantitative data was found to have increased standard deviations of up to 15% compared with data from site 1. This might be improved by extending the acquisition scheme when using SMS or by using regularized SMS reconstructions 59 …”

Section: Discussionmentioning

confidence: 99%

Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning

Hermann

Martínez‐Heras

Rieger

et al. 2021

Magnetic Resonance in Med

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Section: Discussionmentioning

confidence: 99%

Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning

Hermann

Martínez‐Heras

Rieger

et al. 2021

Magnetic Resonance in Med

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“…In this study, we proposed a regularized leakage-blocking reconstruction for improving the quality of OVS-prepared perfusion CMR. In our previous works [11,12], we used consistency with slice-GRAPPA [17] in the proposed method. Although slice-GRAPPA showed sufficient image quality at 8-fold acceleration (SMS=3 × 2-fold in-plane × partial Fourier 6/8) [11,12], it suffered from leakage artifacts in perfusion CMR due to the high 16-fold acceleration factor (SMS=3 × 4-fold in-plane × partial Fourier 6/8).…”

Section: Discussionmentioning

confidence: 99%

“…The acquired raw data with 3-fold SMS and 4-fold in-plane acceleration with 6/8 partial Fourier, leading to overall 16fold acceleration were exported from the scanner for offline processing. The proposed method builds on our earlier work [11,12]. For perfusion CMR, our method combines the advantages of ss-GRAPPA [10] in removing aliasing artifacts, and SPIRiT [15] in enforcing self-consistency while allowing further regularization.…”

Section: Reconstructionmentioning

confidence: 99%

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Improved Simultaneous Multi-Slice Imaging for Perfusion Cardiac MRI Using Outer Volume Suppression and Regularized Reconstruction

Demirel

Weingärtner

Moeller

et al. 2020

2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

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Perfusion cardiac MRI (CMR) is a radiation-free and noninvasive imaging tool which has gained increasing interest for the diagnosis of coronary artery disease. However, resolution and coverage are limited in perfusion CMR due to the necessity of single snapshot imaging during the first-pass of a contrast agent. Simultaneous multi-slice (SMS) imaging has the potential for high acceleration rates with minimal signal-tonoise ratio (SNR) loss. However, its utility in CMR has been limited to moderate acceleration factors due to residual leakage artifacts from the extra-cardiac tissue such as the chest and the back. Outer volume suppression (OVS) with leakageblocking reconstruction has been used to enable higher acceleration rates in perfusion CMR, but suffers from higher noise amplification. In this study, we sought to augment OVS-SMS/MB imaging with a regularized leakage-blocking reconstruction algorithm to improve image quality. Results from highly-accelerated perfusion CMR show that the method improves upon SMS-SPIRiT in terms of leakage reduction and split slice (ss)-GRAPPA in terms of noise mitigation.

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“…28 Split slice-GRAPPA further incorporates a leakageblocking kernel optimization to this reconstruction approach, albeit at the cost of increased noise amplification. 29,30 Recently, SPIRiT-type reconstructions 6 have also been used for myocardial T 1 mapping 31 and for non-Cartesian SMS imaging. 15,32 Finally, ReadOut (RO)-SENSE-GRAPPA proposes the idea of readout concatenation in image domain to encode SMS acceleration, 27,33 which reduces the reconstruction problem into a conventional one-dimensional or 2D GRAPPA interpolation problem, when the acquisition is performed without or with in-plane regularization, respectively.…”

Section: Introductionmentioning

confidence: 99%

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

Demirel

Weingärtner

Moeller

et al. 2021

Magnetic Resonance in Med

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To develop and evaluate a simultaneous multislice (SMS) reconstruction technique that provides noise reduction and leakage blocking for highly accelerated cardiac MRI. Methods: ReadOut Concatenated k-space SPIRiT (ROCK-SPIRiT) uses the concept of readout concatenation in image domain to represent SMS encoding, and performs coil self-consistency as in SPIRiT-type reconstruction in an extended k-space, while allowing regularization for further denoising. The proposed method is implemented with and without regularization, and validated on retrospectively SMS-accelerated cine imaging with threefold SMS and twofold in-plane acceleration. ROCK-SPIRiT is compared with two leakage-blocking SMS reconstruction methods: readout-SENSE-GRAPPA and split slice-GRAPPA. Further evaluation and comparisons are performed using prospectively SMS-accelerated cine imaging. Results: Results on retrospectively threefold SMS and twofold in-plane accelerated cine imaging show that ROCK-SPIRiT without regularization significantly improves on existing methods in terms of PSNR (readout-SENSE-GRAPPA:

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Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI T₁ Mapping

Cited by 6 publications

References 16 publications

Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning

Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning

Improved Simultaneous Multi-Slice Imaging for Perfusion Cardiac MRI Using Outer Volume Suppression and Regularized Reconstruction

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

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Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI T1 Mapping

Cited by 6 publications

References 16 publications

Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning

Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning

Improved Simultaneous Multi-Slice Imaging for Perfusion Cardiac MRI Using Outer Volume Suppression and Regularized Reconstruction

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

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Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI T₁ Mapping

Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning

Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning