A comparison of volumetric GRAPPA algorithms for in-vivo MRI
Seon Young Shin1, JungHyun Song1, Yeji Han1, and Jun-Young Chung1

1Gachon Advanced Institute of Health Sciences and Technology, Gachon University, Incheon, Korea, Republic of

Synopsis

To compare the volumetric GRAPPA algorithms in the presence of physiological artifacts, five different algorithms were used, i.e., 2D-GRAPPA-OP, 3D-GRAPPA, EX-3D-GRAPPA and SK-3D-GRAPPA. The performance of algorithms were compared using the root mean squared error (RMSE) of the image reconstructed from fully acquired 3D in-vivo k-space data and the image reconstructed using different reconstruction algorithms from undersampled dataset.

Introduction

When 3D MR data is acquired with undersampling in the phase-encoding and the partition-encoding directions, the image can be reconstructed using volumetric generalized auto-calibrating partially parallel acquisitions (GRAPPA) algorithms such as 2D-GRAPPA-Operator (OP), 3D-GRAPPA, extension (EX)-3D-GRAPPA and single kernel (SK)-3D-GRAPPA [1-4]. As demonstrated in the previous works, 3D-GRAPPA methods generally showed better performance than 2D-GRAPPA-OP with regards to aliasing artifacts [5-6]. However, the verification was performed for the phantom data only, which do not include physiological artifacts such as motion and blood flow, thereby limiting the scope of the verification. In this study, investigation was carried out to show the performance of volumetric GRAPPA algorithms in the presence of physiological artifacts.

Methods

Schematic diagrams for each reconstruction algorithm are presented in Fig. 1. The 2D-GRAPPA-OP algorithm, utilizing two 2D-kernels, is considered as two different reconstruction methods, i.e., 2D-GRAPPA-OP-YZ and 2D-GRAPP-OP-ZY, according to the order of unaliasing directions, i.e. in which direction the image is unaliased first (Fig. 1(a) and (b)). 3D-GRAPPA uses three different kernels, consisting of two 2D- and one 3D-kernels (Fig. 1(c)). EX-3D-GRAPPA uses three different 3D kernels (Fig. 1(d)). SK-3D-GRAPPA utilizes only one 3D-kernel (Fig. 1(e)).

To demonstrate the performance of these reconstruction algorithms, phantom and in-vivo human data were obtained with a 3D gradient echo sequence from a 3T MRI scanner (Verio, Siemens) equipped with a 12-channel head coil using the following parameters: FOV = 210 × 210 mm2, slice thickness = 0.8mm, matrix size = 512 × 256 × 208, TR = 20ms, TE = 14ms, flip angle = 25º.

From the fully acquired 3D k-space data, undersampling was performed with reduction factors of two for both phase-encoding and partition-encoding directions and 24 auto-calibration signal (ACS) lines were selected for each reconstruction algorithm. To calculate the coefficients for estimation of the not-acquired k-space data, the kernels were then selected as illustrated in the schematics of Fig.1 as follows: 2D-GRAPPA-OP (3 × 4 × 1, 3 × 1 × 4), 3D-GRAPPA (3 × 2 × 2, 3 × 4 × 1, 3 × 1 × 4), EX-3D-GRAPPA (3 × 2 × 2, 3 × 2 × 3, 3 × 3 × 2) and SK-3D-GRAPPA (3 × 2 × 2). By utilizing the estimated k-space data, images were generated using different reconstruction algorithms.

Results

In Figs. 2 and 3, the images reconstructed from the phantom data and the in-vivo data are respectively presented. The upper rows show the reconstructed images and the bottom rows show the difference images. The difference images were calculated by subtracting the images reconstructed from the undersampled data from the true image reconstructed from the fully acquired 3D k-space data and. The contrast of the difference images was adjusted for viewing purposes. As demonstrated by Figs. 2(a, b) and 3(a, b), the directions of the dominant N/2 ghost varied according to the direction of initial unaliasing in 2D-GRAPPA-OP. All 3D GRAPPA algorithms generally showed better performance than 2D-GRAPPA-OP, with more improvements provided by EX-3D-GRAPPA and SK-3D-GRAPPA. In Fig. 4, root mean squared error (RMSE) values were plotted. For phantom data, the RMSE of EX-3D-GRAPPA and SK-3D-GRAPPA was reduced by 0.0274 and 0.0166, respectively, compared to the RMSE of 2D-GRPPA-OP-YZ. For in-vivo data, the RMSE was reduced by 0.0348 and 0.0245. In general, the 3D-GRAPPA algorithms showed larger amount of improvements with respect to the noise figures in human data.

Discussion & Conclusions

For both phantom and human experiments, 3D-GRAPPA reconstructions were generally more effective than the 2D-GRAPPA-OP because 2D-GRAPPA algorithm cannot prevent the accumulation of errors, which is inevitable as the second unaliasing is performed by utilizing the k-space data estimated in the first unalising step. More specifically, EX-3D- and SK-3D-GRAPPA algorithms showed better performance for noise reduction because they use acquired data only by using 3D kernels for estimation of k-space data. In case of the human data, which have additional physiological effects, the results showed similar tendency across different reconstruction algorithms as in the phantom data. However, as the RMSE plotted in Fig. 4 demonstrated, the degree of improvements in 3D-GRAPPA compared to 2D-GRAPPA-OP was greater for human data, while EX-3D-GRAPPA exhibited the lowest values of RMSE. This is partly because artifacts due to the physiological effects were introduced in the in-vivo data and they influence the image quality due to the accumulation of errors. Thus, it is suggested to use EX-3D-GRAPPA for volumetric GRAPPA reconstruction of in-vivo images.

Acknowledgements

No acknowledgement found.

References

[1] Breuer et al., MRM, 2006;56:1359-1364

[2] Griswold et al., MRM 2002;47:1202-10

[3] Wang et al., MRM 2005; 54:738-742

[4] F.Breuer et al, ISMRM (2006)

[5] Chung et al, ESMRMB, 2012(#763)

[6] Chung et al, ESMRMB, 2012(#765)

[7] S.Bauer et al, MRM 2011;66:402-409

Figures

Fig.1 (a) 2D-GRAPPA-OP-YZ, (b) 2D-GRAPPA-OP-ZY, (c) 3D-GRAPPA, (d) EX-3D-GRAPPA, (e) SK-3D-GRAPPA

Fig.2 Upper images : Images reconstructed with different reconstruction methods.

Lower images : Difference between images reconstructed from the fully acquired k-space data and from the k-space data estimated by different algorithms.

(a) 2D-GRAPPA-OP-YZ (b) 2D-GRAPPA-OP-ZY (c) 3D-GRAPPA (d)EX-3D-GRAPPA (e) SK-3D-GRAPPA


Fig.3 Upper images : Images reconstructed with different reconstruction methods.

Lower images : Difference between images reconstructed from the fully acquired k-space data and from the k-space data estimated by different algorithms.

(a) 2D-GRAPPA-OP-YZ (b) 2D-GRAPPA-OP-ZY (c) 3D-GRAPPA (d)EX-3D-GRAPPA (e) SK-3D-GRAPPA


Fig. 4 RMSE values calculated from images reconstructed with different algorithms.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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