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

Abstract

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.

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

“…Thus, it is not straightforward to extend the former to an iterative algorithm that can incorporate low‐dimensional regularizers. Nevertheless, our previous work includes approaches that can combine both one‐time projection and self‐consistency kernels in the same objective function, along with low‐dimensional regularizers 23,31 . However, the application of such methods to virtual slice and the readout concatenation concepts warrant further investigation, which is beyond the scope of this work.…”

“…[9][10][11][12][13][14][15][16][17][18][19][20][21][22] In particular, SMS acceleration has been used for myocardial T 1 mapping, 13,18 for cine imaging, 16,21 and for perfusion imaging 11,14,15,17,22 using both Cartesian and non-Cartesian acquisitions at different acceleration rates. However, due to coil geometry limitations, SMS acceleration remains limited for CMR, especially in conjunction with in-plane parallel imaging, in which leakage artifacts and noise amplification are observed at high acceleration rates, 23 necessitating improvements in reconstruction.…”

DEep learning‐based rapid Spiral Image REconstruction (DESIRE) for high‐resolution spiral first‐pass myocardial perfusion imaging

“…The interleaved SS and SMS images can be reconstructed using a motion‐compensated technique, namely (SMS‐slice‐)L1‐SPIRiT, 6,8,9 where the through‐plane operator was incorporated in the proposed SMS‐slice‐L1‐SPRIiT reconstruction for spiral perfusion imaging to reduce the slice leakage and improve the reconstruction performance, which can be expressed as follows: where are motion‐corrected multichannel dynamic perfusion images to be reconstructed for each slice, are the acquired spiral SS or SMS data that might contain motion, is an inverse Fourier gridding operator (NUFFT) that transforms from Cartesian image space to spiral k‐space, 31 is the motion‐correction operator mapping the motion‐corrected perfusion image series to the motion‐corrupted image series, 32 is an image‐space SPIRiT operator for each slice, 33 is an image‐space through‐plane operator that is calibrated by enforcing consistency on the desired slice and blocking the signal from the interfering slices to reduce slice leakage artifacts, 7,34,35 is the finite time difference transform that operates on each individual coil separately enforcing sparsity in the temporal domain of perfusion image series, is the identity matrix, , and are parameters that balance the data acquisition consistency with SPIRiT calibration consistency, temporal sparsity and slice consistency, and is the S...…”

“…where x are motion-corrected multichannel dynamic perfusion images to be reconstructed for each slice, y are the acquired spiral SS or SMS data that might contain motion, F u is an inverse Fourier gridding operator (NUFFT) that transforms from Cartesian image space to spiral k-space, 31 R is the motion-correction operator mapping the motion-corrected perfusion image series to the motion-corrupted image series, 32 G SPIRiT is an image-space SPIRiT operator for each slice, 33 G TP is an image-space through-plane operator that is calibrated by enforcing consistency on the desired slice and blocking the signal from the interfering slices to reduce slice leakage artifacts, 7,34,35 Ψ is the finite time difference transform that operates on each individual coil separately enforcing sparsity in the temporal domain of perfusion image series, I is the identity matrix, λ 1 , λ 2 and λ 3 are parameters that balance the data acquisition consistency with SPIRiT calibration consistency, temporal sparsity and slice consistency, and Φ is the SMS Hadamard phase modulation operator 29 that depends on the number of interleaves and the desired SMS factor. For SS acquisition, the same equation can be utilized by setting Φ to I and setting λ 3 ¼ 0, which results in the SS L1-SPIRiT reconstruction.…”

Signal-Intensity Informed Multi-Coil MRI Encoding Operator for Improved Physics-Guided Deep Learning Reconstruction of Dynamic Contrast-Enhanced MRI

“…Free-breathing first-pass perfusion cardiac MRI (CMR) was performed with the injection of 0.05 mmol/kg gadobutrol (Gadovist) at 4mL/s followed by a 10-mL saline flush. A saturation-prepared GRE sequence was used along with slab-selective saturation pulses for outer volume suppression (OVS) [21]. Relevant imaging…”