Robust GRAPPA-accelerated diffusion-weighted readout-segmented (RS)-EPI

Abstract

Readout segmentation (RS-EPI) has been suggested as a promising variant to echo-planar imaging (EPI) for high-resolution imaging, particularly when combined with parallel imaging. This work details some of the technical aspects of diffusion-weighted (DW)-RS-EPI, outlining a set of reconstruction methods and imaging parameters that can both minimize the scan time and afford high-resolution diffusion imaging with reduced distortions. These methods include an efficient generalized autocalibrating partially parallel acquisition (GRAPPA) calibration for DW-RS-EPI data without scan time penalty, together with a variant for the phase correction of partial Fourier RS-EPI data. In addition, the role of pulsatile and rigid-body brain motion in DW-RS-EPI was assessed. Corrupt DW-RS-EPI data arising from pulsatile nonlinear brain motion had a prevalence of approximately 7% and were robustly identified via k-space entropy metrics. For DW-RS-EPI data corrupted by rigid-body motion, we showed that no blind overlap was required. The robustness of RS-EPI toward phase errors and motion, together with its minimized distortions compared with EPI, enables the acquisition of exquisite 3 T DW images with matrix sizes close to 512(2).

FIG. 1

a: Pulse sequence timing diagram for the RS-EPI, twice-refocused spin echo–based diffusion sequence (9). The RF pulses (spectral-spatial 90° and refocusing 180°) and the diffusion gradients (shaded regions) are shown. TE₁ and TE₂ are the echo times of the imaging and navigator echo, respectively, T_over the time for collecting the number of overscans (N_o). The strength of G_dp varies, depending upon the position of the blind along the x-direction. Note that the phase-encoding gradient size for the imaging and navigator echo is equivalent. b: k-Space imaging and navigator blind trajectories for the RS-EPI sequence (9). k-Space is filled with an odd number of separate blinds, B, of a given blind width, W, and OF = dW/W. c: Two options for estimating the ghost calibration parameters and GRAPPA weights. The first is to estimate the ghost parameters and GRAPPA weights on a fully sampled central b = 0 s/mm² blind, and the second is to estimate these parameters on a fully sampled central strip formed by R b = 0 s/mm² navigators.

FIG. 2

a: Summary of the reconstruction steps applied to the partial Fourier RS-EPI b = 0 and DW data. b: Triangular phase correction process applied to each partial Fourier DW blind in RS-EPI. Since the navigator blind is partial Fourier-encoded in the k_y direction, the k-space peak is centered so that a low-resolution phase map can be extracted without the y-encoding phase present. To achieve this, the bottommost pixels of the navigator are discarded, and this truncated k-space was zero-padded back to its full size to center the k-space peak. This ensures that resolution (or size) of the navigator and imaging blind remains equivalent. Since the phase is inverted due to the 180° refocusing pulse between the imaging and navigator blinds, the phase correction step is a multiplication. Note that the blinds are zero-padded prior to the inverse FFT to avoid wrapping of the signal due to the phase correction process. c: Rigid-body motion correction method applied to the RS-EPI data.

FIG. 3

Multishot (NEX = R = 3) RS-EPI b = 0 and b = 1000 s/mm² (S/I direction) images. In (a), the image is reconstructed without performing GRAPPA. In (b) the GRAPPA weights are estimated from a fully sampled central b = 0 s/mm² imaging blind, and in (c) the GRAPPA weights are estimated from a fully sampled central strip formed by R b = 0 s/mm² navigator blinds. The right-hand sides of (b) and (c) depict the difference image using the dataset reconstructed without GRAPPA and are windowed by one order of magnitude. The DW-RS-EPI dataset is acquired with peripheral cardiac gating at a target resolution of 288 × 288, using 33 blinds of W = 64.

FIG. 4

Plot showing the entropy of k-space calculated for 84 DW-RS-EPI navigator blinds for (a) L/R, (b) A/P, and (c) S/I diffusion-encoding directions (b = 1000 s/mm²). The diffusion images were acquired on a healthy volunteer, both without (red lines) and with (black lines) peripheral cardiac gating. Imaging parameters were N = 288 × 288, W = 64, with a TR = 3 s for ungated acquisitions and three RR intervals and minimum trigger delay for gated acquisitions. As shown in (c), nonlinear pulsatile brain motion causes significant dispersion of k-space, which correlates with high k-space entropy and signal voids in image space. Note that the 84 repetitions arise from four repetitions of a typical RS-EPI dataset with B = 7 blinds and NEX = 3. The black dotted line corresponds to the threshold (given by the mean of the entropy over the 84 navigator blinds + 2 standard deviations) above which data are discarded. The large peaks in the graphs refer to single incidents where the k-space entropy and signal dropout in the image domain are significant.

FIG. 5

Two slices from a DW-RS-EPI ungated dataset where (a) shows all the blinds that exceed the k-space entropy threshold (given by the mean plus 2 standard deviations). In (b), the minimum intensity projection (minIP) is performed over the remaining 77 ‘uncorrupted’ blinds, and (c) shows the corresponding maximum intensity projection (MIP) for reference. This dataset was acquired on a healthy volunteer using 84 repetitions of the diffusion scheme with R = 3, W = 64, and N = 288 × 288. Both the minIP and MIP are sensitive to residual outliers and demonstrate how well the entropy threshold is able to single out corrupted blinds. The blinds were zero-filled to achieve the same aspect ratio in the y and x direction.

FIG. 6

Reconstruction of one repetition of the DW-RS-EPI selected from the (a) gated and (b,c) ungated dataset from Fig. 4 (b = 1000 s/mm², S/I direction). Imaging parameters were N = 288 × 288, R = 3, B = 7, and W = 64. In (b), a repetition with a corrupted center blind is shown. Despite the selection of two corrupted offcenter blinds in this particular dataset, replacing the central imaging blind with an uncorrupted central blind reveals good image quality, as shown in (c).

FIG. 7

RS-EPI isoDW images acquired (a) without and (b) with continuous rigid-body motion (b = 1000 s/mm² in the x,y,z direction, N = 288 × 288, R = 3, B = 7, and W = 64). In this case, the OF = 0%. After motion correction, one can see good image quality in (c), despite the gaps in k-space produced by the rotation of the coordinates. Even a negative OF in (d) shows similar high image quality. e: Aliasing artifacts become prominent as the OF drops further below the Nyquist limit, as indicated by the black arrows. f,g: The corrected k-space coordinates from one diffusion encoding direction.

FIG. 8

High-resolution DTI of (a) the brain stem and cerebellum and (b) the cerebrum. From left to right, the b = 0 s/mm², b = 1000 s/mm² isoDWI, FA, and FA with 1st eigenvector color encoding. The scan parameters were as follows: B = 15, W = 64, N = 480 × 480, R = 3, NEX = 3, 4 mm slice thickness, FOV = 24 cm, 2 b = 0 and 1 b = 1000 s/mm² scan along 15 noncollinear directions, TR/echo time = 3 sec/60 ms, and a 35-min total scan time. Retrospective motion correction was applied on each DW image before final tensor processing to minimize blurring. Note that the reddish hue in the color map arises from the slightly larger b-value given by the crushers that accompany the slice-select pulse.