APIR4EMC: Autocalibrated parallel imaging reconstruction for extended multi-contrast imaging

Chaoping Zhang,Stefan Klein,Alexandra Cristobal-Huerta,Juan A Hernandez-Tamames,Dirk H.J Poot

doi:10.1016/j.mri.2021.02.002

Chaoping Zhang, Stefan Klein + Show 3 more

Open Access

https://doi.org/10.1016/j.mri.2021.02.002

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Abstract

PurposeTo improve image quality of multi-contrast imaging with the proposed Autocalibrated Parallel Imaging Reconstruction for Extended Multi-Contrast Imaging (APIR4EMC). MethodsAPIR4EMC reconstructs multi-contrast images in an autocalibrated parallel imaging reconstruction framework by adding contrasts as virtual coils. Compensation of signal evolution along the echo train of different contrasts is performed to improve signal prediction for missing samples. As a proof of concept, we performed prospectively accelerated phantom and in-vivo brain acquisitions with T1, T1-fat saturated (Fatsat), T2, PD, and FLAIR contrasts. The k-space sampling patterns of these acquisitions were jointly optimized. Images were jointly reconstructed with the proposed APIR4EMC method as well as individually with GRAPPA. Root mean square error (RMSE) to fully sampled reference images and g-factor maps were computed for both methods in the phantom experiment. Visual evaluation was performed in the in-vivo experiment. ResultsCompared to GRAPPA, APIR4EMC reduced artifacts and improved SNR of the reconstructed images in the phantom acquisitions. Quantitatively, APIR4EMC substantially reduced noise amplification (g-factor) as well as RMSE compared to GRAPPA. Signal evolution compensation reduced artifacts. In the in-vivo experiments, 1 mm3 isotropic 3D images with contrasts of T1, T1-Fatsat, T2, PD, and FLAIR were acquired in as little as 7.5 min with the acceleration factor of 9. Reconstruction quality was consistent with the phantom results. ConclusionCompared to single contrast reconstruction with GRAPPA, APIR4EMC reduces artifacts and noise amplification in accelerated multi-contrast imaging.

Highlights

In magnetic resonance imaging (MRI), multiple contrasts, like T1, T2, proton-density (PD), and FLAIR weighted images, are routinely ac quired in clinical practice, as these images provide complementary in formation for diagnosis [1]
The average of Root mean square error (RMSE) on all contrasts computed on all kspace patterns was 1.82 × 10− 2 with the subsampling factor of 6, whereas the optimal k-space pattern achieved an RMSE of 1.70 × 10− 2
Compared to the GRAPPA images, which suffered from substantial artifacts, APIR4EMC shows lower noise amplification and less severe artifacts in each contrast image

Summary

Introduction

In magnetic resonance imaging (MRI), multiple contrasts, like T1, T2, proton-density (PD), and FLAIR weighted images, are routinely ac quired in clinical practice, as these images provide complementary in formation for diagnosis [1]. Compressed sensing has been extended to multi-contrast scenarios by performing joint reconstruction, exploiting similarities of spatial structures across different contrasts by assuming the same set of support in the sparsity domain [2,3,4,5,6,8]. The phase independent GRASE recon struction [21,22] and APIR4GRASE [23] assume spin echo and gradient echo as virtual coils for autocalibrated reconstruction. They exploit the correlation between the different echo types as the sensitivity encoding of virtual coil channels, and reconstruct different contrast weighted images. A recent extension of JVC-GRAPPA [25], using variational neural networks, achieved good image quality with 16fold acceleration for multi-contrast images, fully sampled data was needed for training of the networks

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