Robust EPI Nyquist ghost removal by incorporating phase error correction with sensitivity encoding (PEC‐SENSE)

Purpose To evaluate the use of magnetic resonance fingerprinting (MRF) for simultaneous quantification of T1 and T2∗ in a single breath‐hold in the kidneys. Methods The proposed kidney MRF sequence was based on MRF echo‐planar imaging. Thirty‐five measurements per slice and overall 4 slices were measured in 15.4 seconds. Group matching was performed for in‐line quantification of T1 and T2∗. Images were acquired in a phantom and 8 healthy volunteers in coronal orientation. To evaluate our approach, region of interests were drawn in the kidneys to calculate mean values and standard deviations of the T1 and T2∗ times. Precision was calculated across multiple repeated MRF scans. Gaussian filtering is applied on baseline images to improve SNR and match stability. Results T1 and T2∗ times acquired with MRF in the phantom showed good agreement with reference measurements and conventional mapping methods with deviations of less than 5% for T1 and less than 10% for T2∗. Baseline images in vivo were free of artifacts and relaxation times yielded good agreement with conventional methods and literature (deviation T1:7±4%, T2∗:6±3%). Conclusions In this feasibility study, the proposed renal MRF sequence resulted in accurate T1 and T2∗ quantification in a single breath‐hold.

Section: Discussionmentioning

confidence: 99%

Magnetic resonance fingerprinting for simultaneous renal T₁ and T₂^* mapping in a single breath‐hold

Hermann

Chacon‐Caldera

Brumer

et al. 2020

“…Therefore, the effective channel number and in‐plane acceleration factor (R) are both doubled. The positive echoes are set to have the reference coil sensitivity maps without modification, and the coil sensitivity maps for negative echoes are multiplied slice‐wise by their phase error maps . Mathematically, the aliased images in i th channel can be expressed as

I_{p, i}^{a l i a s e d} = \frac{1}{2} {true \sum}_{z = 1}^{M B} (C_{i, z} I_{z} + C_{i, z}^{'} I_{z}^{'})

I_{n, i}^{a l i a s e d} = \frac{1}{2} {true \sum}_{z = 1}^{M B} (C_{i, z} E_{z} I_{z} - C_{i, z}^{'} E_{z}^{'} I_{z}^{'}),

where

I_{p, i}^{a l i a s e d}

and

I_{n, i}^{a l i a s e d}

are the aliased positive‐echo image and negative‐echo image, respectively,

M B

the multiband factor,

z

the slice index from 1 to

M B

C_{i, z}

the coil sensitivity map,

E_{z}

the phase error map,

I_{z}

the aliasing‐free positive‐echo image, and the superscript ′ denotes half FOV shift along phase‐encoding direction.…”

Section: Methodsmentioning

confidence: 99%

“…Then, full‐matrix positive‐ and negative‐echo images are SENSE reconstructed from the single‐band reference data, and phase error maps are calculated slice‐wise with denoising. Last, SMS EPI is reconstructed using phase error correction SENSE (PEC‐SENSE), which removes slice‐dependent 2D Nyquist ghosts by incorporating the pixel‐wise phase error maps into coil sensitivity maps …”

Section: Introductionmentioning

confidence: 99%

Robust SENSE reconstruction of simultaneous multislice EPI with low‐rank enhanced coil sensitivity calibration and slice‐dependent 2D Nyquist ghost correction

Lyu

Barth

Xie

et al. 2018

Self Cite

“…However, these classic navigator‐free methods are generally ineffective compared to the reference‐based methods, or the computational complexity is higher. Recently, some correction methods used phased array coils information to correct ghost artifact . Dual polarity generalized autocalibrating partial parallel acquisition (dual polarity GRAPPA) uses multiple GRAPPA kernels to correct phase mismatch and fill the missing k‐space simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…This method reconstructs the negative echo and positive echo images using SENSE. Although PEC‐SENSE preserves the image SNR by correcting ghost artifact from the downsampled k‐space, it always requires accurate coil sensitivity maps to apply SENSE …”

Section: Introductionmentioning

confidence: 99%

k‐Space deep learning for reference‐free EPI ghost correction

Lee

Han

Ryu

et al. 2019

Purpose Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high‐field MRI due to the nonlinear and time‐varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k‐space interpolation problem that can be solved using structured low‐rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k‐space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non‐accelerated EPI acquisitions. Theory and Methods To take advantage of the even and odd‐phase directional redundancy, the k‐space data are divided into 2 channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi‐coil k‐space data into additional input channels. Then, our k‐space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k‐space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k‐space data from both ghost and subsampling. Results Reconstruction results using 3T and 7T in vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster. Conclusions The proposed k‐space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high‐field MRI without changing the current acquisition protocol.