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Self-calibrated and Collaborative Propeller-EPI Reconstruction (SCOPER) for High-Quality Diffusion-Tensor Imaging
Xiaoxi Liu1, Di Cui1, Erpeng Dai2, Edward S. Hui1,3, Queenie Chan4, and Hing-Chiu Chang1

1Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 3The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China, 4Philips Healthcare, Hong Kong, China

Synopsis

Propeller-EPI is a self-navigated multi-shot technique for high-resolution diffusion-tensor imaging. However, corrections for 2D Nyquist ghost and distortion for each blade data are necessary to obtain high-quality image. In this study, we aim to develop a self-calibrated and collaborative Propeller-EPI reconstruction (SCOPER) framework that can 1) allows the estimation of phase errors and off-resonance map from the blade data, and 2) collaboratively reconstruct fully-corrected Propeller-EPI image from all blade data and the thereof. Our results demonstrated that SCOPER shows improved SNR performance, compared with conventional Propeller-EPI reconstruction pipelines.

Purpose

Propeller-EPI is a self-navigated multi-shot technique for high-resolution diffusion-tensor imaging (DTI)[1,2]. However, the presence of EPI-related artifacts, such as Nyquist ghost and geometric distortion, can significantly degrade the image quality of Propeller-EPI[3-5]. Previous studies have shown that application of parallel imaging and triangle weighting to each blade data of Propeller-EPI can reduce image distortion and blurring due to off-resonance effect at the expense of noise amplification[6,7]. Moreover, iterative 2D ghost correction method can reduce the residual ghost in each blade data before Propeller-EPI reconstruction[5]. As a result, corrections for 2D Nyquist ghost and distortion for each blade data are critical before final data combination in order to obtain high-quality Propeller-EPI image. In the conventional Propeller-EPI reconstruction pipeline, the distortion of each blade data is corrected by using either field map correction[1] or reversed gradient method[4]. In this study, we aim to develop a self-calibrated and collaborative Propeller-EPI reconstruction (SCOPER) framework that 1) allows the estimation of phase errors and off-resonance map from blade data, and 2) collaboratively reconstructs fully-corrected Propeller-EPI image from all blade data and the thereof. The performance of SCOPER was evaluated on both long-axis and short-axis Propeller-EPI acquisitions (LAP-EPI and SAP-EPI).

Methods

Signal model of Propeller-EPI:

The k-space signal along the jth phase encoding line of the nth blade data from Propeller-EPI acquisition for the $$$\gamma$$$th coil can be written as

$$S_{n,\gamma,k_{y|j}}=F_{n,k_{y|j}}(n\theta,j)C_{\gamma}\phi_{n,k_{y|j}}\psi_{k_{y|j}}\rho \qquad (1),$$

where $$$F_{n,k_{y|j}}(n\theta,j)$$$ represents the Fourier encoding matrix for the nth blade with angle $$$n\theta$$$, $$$C_{\gamma}$$$ the coil sensitivity, $$$\phi_{n,k_{y|j}}$$$ the 2D phase errors due to Nyquist ghost, and phase variations associated with diffusion gradient, $$$\psi_{k_{y|j}}$$$ the 2D phase error due to off-resonance, and $$$\rho$$$ the unknown 2D image.

SCOPER framework: The entire framework is summarized in Fig.1. In the pre-processing stage, the 2D phase errors $$$\phi$$$ for the odd and even echoes for each blade were estimated from the T2WI data. Nyquist ghosts were subsequently corrected for a pair of blades with 0° and 180° rotation angles, and the pixel displacement map (DM) was subsequently measured by using the reversed gradient method [4]. The phase error $$$\psi$$$ caused by off-resonance was estimated from the DM. For DWI data, the inter-shot phase variations are included in $$$\phi$$$. Finally, all blade data are collaboratively reconstructed by solving a signal model shown in Eq.(1) using the conjugate-gradient (CG) algorithm with a total variation constraint[11].

Experiments: Human brain DTI data sets with acquisition matrix of 128x128 were acquired using a 3.0T MRI scanner (Achieva TX, Philips) using LAP-EPI with four different blade acceleration factor (i.e., R=1, 2, 3, and 4) and the following imaging parameters: 24 blades, blade rotation of 15°, and blade size of 128x32. High-resolution DTI data sets with acquisition matrix of 192x192 were acquired using LAP-EPI and SAP-EPI with the following parameters: 20 blades, blade rotation of 18°, blade size of 192x20 (LAP-EPI) and 64x38 (SAP-EPI with 60% partial Fourier), and blade acceleration factor of 3. All data were acquired with FOV of 240 mm, and b-value of 800 s/mm2 in 6 diffusion directions. A multi-echo GRE sequence with same geometric parameters as those of the DTI acquisition was performed for the estimation of field map.

Data reconstruction and evaluations: All Propeller-EPI data were reconstructed using four different methods, namely 1) SCOPER, and conventional Propeller-EPI reconstruction pipelines 2) without distortion correction, 3) with field map correction, and 4) with reversed gradient correction. SNR was measured to assess the quality of these four reconstruction methods.

Results

Fig.2 shows the non-accelerated LAP-EPI images reconstructed from conventional Propeller-EPI reconstruction without distortion correction, and SCOPER with correction for off-resonance using field map and DM. Fig.3 and FIg.4 show the representative images reconstructed from the 128x128 and 192x192 DTI data sets, respectively. Fig.5 shows the SNR measurements from all data sets.

Discussion

To achieve high-resolution high-quality DTI using Propeller-EPI acquisition, corrections, prior to conventional Propeller-EPI reconstruction, for Nyquist ghost and distortion on a blade-by-blade basis are necessary. Our proposed SCOPER framework allows the estimation of the correction parameters from the blade data, thereby obviating the need of external measurement. We have demonstrated that the off-resonance effect on Propeller-EPI can be largely eliminated by SCOPER, efficacy similar to that using external field mapping (Fig.2). Our results demonstrate that SCOPER is robust in reconstructing either LAP-EPI or SAP-EPI data (Fig.3 and Fig.4). SNR analysis showed that SCOPER offers better SNR performance (Fig.5). One drawback of SCOPER framework is its long reconstruction time due to very large matrix inversion in solving the signal model. In conclusion, our proposed SCOPER framework is optimal for Propeller-EPI reconstruction with improved SNR performance.

Acknowledgements

The work was in part supported by grants from Hong Kong Research Grant Council (GRF HKU17138616 and GRF HKU17121517).

References

[1] Wang, Fu‐Nien; Huang, Teng‐Yi; Lin, Fa‐Hsuan; Chuang, Tzu‐Chao, et al. PROPELLER EPI: An MRI technique suitable for diffusion tensor imaging at high field strength with reduced geometric distortions. Magnetic Resonance in Medicine, November 2005, Vol.54(5), pp.1232-1240

[2] Skare, Stefan; Newbould, Rexford D.; Clayton, Dave B.; Bammer, Roland. Propeller EPI in the other direction. Magnetic Resonance in Medicine, June 2006, Vol.55(6), pp.1298-1307

[3] Chen, NK; Wyrwicz, AM. Optimized distortion correction technique for echo planar imaging. Magn Reson Med 2001; 45:525–528.

[4] Chang, H.C.; Chuang, T.C.; Lin, Y.R.; Wang, F.N. et al. Correction of geometric distortion in Propeller echo planar imaging using a modified reversed gradient approach Quant Imag Med Surg, 3(2) (2013), p.73

[5] Chang HC, Chen NK, Chuang TC, Juan CJ, Wu ML and Chung HW, “PROPELLER-EPI improved by 2D phase cycled reconstruction”, ISMRM, 20th Annual Meeting, Melbourne, Australia, May 2012.

[6] Chuang TC1, Huang TY, Lin FH, Wang FN, Juan CJ, Chung HW, Chen CY, Kwong KK. PROPELLER-EPI with parallel imaging using a circularly symmetric phased-array RF coil at 3.0 T: application to high-resolution diffusion tensor imaging. Magn Reson Med. 2006 Dec;56(6):1352-8.

[7] Chuang, T-C.; Huang, T-Y.; Wang, F-N. and Chung H-W. Advantages of long-axis PROPELLER EPI via k-space weighting: comparison of point spread function with short-axis PROPELLER EPI. ISMRM 2006; abstract no. 2955

[8] Aksoy, Murat; Skare, Stefan; Holdsworth, Samantha; Bammer, Roland. Effects of motion and b-matrix correction for high resolution DTI with short-axis PROPELLER-EPI. NMR in Biomedicine, August 2010, Vol.23(7), pp.794-802

[9] Voss, Henning U.; Watts, Richard; Uluğ, Aziz M.; Ballon, Doug. Fiber tracking in the cervical spine and inferior brain regions with reversed gradient diffusion tensor imaging. Magnetic Resonance Imaging, 2006, Vol.24(3), pp.231-239

[10] Block, Kai Tobias; Uecker, Martin; Frahm, Jens. Undersampled radial MRI with multiple coils. Iterative image reconstruction using a total variation constraint. Magnetic Resonance in Medicine, June 2007, Vol.57(6), pp.1086-1098

Figures

Fig.1: The reconstruction flowchart of SCOPER. In the pre-processing stage, we firstly derive the 2D phase errors $$$\phi_{n,k_{y|j}}$$$ between odd and even echoes from T2WI data for all blades. Afterward, the Nyquist ghosts are corrected for a pair of blades with 0° and 180° rotation angles, and the displacement map (DM) is subsequently measured by using reversed gradient method. For DWI data, the inter-shot phase variations are included in derived 2D phase errors $$$\phi_{n,k_{y|j}}$$$. Finally, all blade data are collaboratively reconstructed by solving a signal model with k-space encodings, 2D phase errors, coil sensitivity, and off-resonance effect taken into account.

Fig.2: The 128x128 T2WI and averaged DWI images reconstructed from LAP-EPI data (24 blades, 32 k-lines/blade, and 15° rotation) using (a) conventional Propeller-EPI reconstruction, (b) SCOPER with off-resonance information derived from an external field map measured from multi-echo GRE imaging, and (c) SCOPER with off-resonance information derived from displacement map (DM) measured internally from the raw data. The images produced from conventional Propeller-EPI reconstruction show the signal-loss and blurring due to off-resonance effect (yellow arrows). The proposed SCOPER framework can reduce the artifacts due to off-resonance effect in Propeller-EPI acquisition.

Fig.3: (a)-(d) The 128x128 T2WI and DWI images reconstructed from LAP-EPI data acquired with four different blade acceleration factors varying from 1 to 4 (24 blades, 15° rotation, and 32 k-lines for full-sampled blade data). All data were reconstructed with three different conventional Propeller-EPI reconstruction pipelines and proposed SCOPER framework. The three conventional Propeller-EPI reconstructions employ different strategies to reduce the artifacts associated with off-resonance effect, including 1) applying triangle weighting to each blade, 2) distortion correct of each blade using fieldmap, and 3) distortion correction of each pair of blades using reversed gradient method.

Fig.4: The comparison of 192x192 DTI data (with 1.25 x 1.25 mm2 in-plane resolution) acquired by using either (a) LAP-EPI or (b) SAP-EPI, with an acceleration factor of 3 for each blade data acquisition. All data were reconstructed with three different conventional Propeller-EPI reconstruction pipelines and proposed SCOPER framework. The resulted images demonstrate that the proposed SCOPER framework can successfully reconstruct either LAP-EPI or SAP-EPI data, with reduced noise amplification due to parallel imaging reconstruction and minimized artifacts caused by off-resonance effect.

Fig.5: The SNR measurements of T2WI and averaged DWI images reconstructed from (a) 128x128 data set with LAP-EPI acquisition (as shown in Figure 3), and (b) 192x192 data set with either LAP-EPI or SAP-EPI acquisition (as shown in Figure 4). The quantitative measurements show prefect agreement in improved SNR performance by using SCOPER reconstruction framework.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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