3D-EPI blip-up/down acquisition (BUDA) with CAIPI and joint Hankel structured low-rank reconstruction for rapid distortion-free high-resolution T 2 * mapping

Abstract

Purpose: This work aims to develop a novel distortion-free 3D-EPI acquisition and image reconstruction technique for fast and robust, high-resolution, whole-brain imaging as well as quantitative $T_2^{*}$ mapping.

Methods: 3D Blip-up and -down acquisition (3D-BUDA) sequence is designed for both single- and multi-echo 3D gradient recalled echo (GRE)-EPI imaging using multiple shots with blip-up and -down readouts to encode B₀ field map information. Complementary k-space coverage is achieved using controlled aliasing in parallel imaging (CAIPI) sampling across the shots. For image reconstruction, an iterative hard-thresholding algorithm is employed to minimize the cost function that combines field map information informed parallel imaging with the structured low-rank constraint for multi-shot 3D-BUDA data. Extending 3D-BUDA to multi-echo imaging permits $T_2^{*}$ mapping. For this, we propose constructing a joint Hankel matrix along both echo and shot dimensions to improve the reconstruction.

Results: Experimental results on in vivo multi-echo data demonstrate that, by performing joint reconstruction along with both echo and shot dimensions, reconstruction accuracy is improved compared to standard 3D-BUDA reconstruction. CAIPI sampling is further shown to enhance image quality. For $T_2^{*}$ mapping, parameter values from 3D-Joint-CAIPI-BUDA and reference multi-echo GRE are within limits of agreement as quantified by Bland-Altman analysis.

Conclusions: The proposed technique enables rapid 3D distortion-free high-resolution imaging and $T_2^{*}$ mapping. Specifically, 3D-BUDA enables 1-mm isotropic whole-brain imaging in 22 s at 3T and 9 s on a 7T scanner. The combination of multi-echo 3D-BUDA with CAIPI acquisition and joint reconstruction enables distortion-free whole-brain $T_2^{*}$ mapping in 47 s at 1.1 × 1.1 × 1.0 mm³ resolution.

Keywords: T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping; 3D-BUDA; CAIPI; distortion-free EPI; joint Hankel low-rank reconstruction.

Figure 1.

The sequence diagram of the 3D-BUDA-EPI for multi-echo imaging (a) and k-space trajectory (b). 3D slab-selective EPI data were acquired using a complementary blip-up /down acquisitions multi-shot encoding with water excitation. Blip-up /down sampling was implemented for each echo (see yellow and green blips in phase encoding gradient), followed by a rewinder gradient before the next echo. All blip-up shots with all echoes were acquired first, then blip-down shots.

Figure 2.

The proposed 3D-Joint-BUDA image reconstruction framework for multi-echo multi-shot GRE-EPI data. The new Hankel matrix is formed for joint image reconstruction using both echo and shot neighborhood information of the GRE-EPI dataset.

Figure 3.

Comparison of reconstructed results and error maps of different time-matched acquisition schemes and reconstruction approaches. (a) Reference image by fully-sampled 3D-BUDA image reconstruction. (b) 4-shot R_z = 2 CAIPI acquisition with 3D-BUDA reconstruction result (2 blip-up shots and 2 blip-down shots). (c) 4-shot R_z = 2 conventional acquisition with 3D-BUDA image reconstruction (2 blip-up shots and 2 blip-down shots). (d) 2-shot R_z = 1 acquisition with 3D-BUDA image reconstruction (1 blip-up shot and 1 blip-down shot). (e) 2-shot R_z = 1 acquisition with Hybrid-space SENSE image reconstruction (1 blip-up shot and 1 blip-down shot). (f)-(i) are the corresponding difference maps. The numbers in the sampling mask represent the acquisition order of the shots.

Figure 4.

Comparison of different approaches (SENSE, TOPUP, Hybrid-space SENSE, and 3D-BUDA) on image quality and distortion-correction effect for the same 2-shot GRE-EPI BUDA dataset from a 3T scanner. First column: Blip-up EPI SENSE results. Second column: Blip-down EPI SENSE results. Third column: TOPUP results. Fourth column: Hybrid-space SENSE results. Last column: The 3D-BUDA image reconstruction. Three rows are the three planes of 3D imaging. In this dataset, R_inplane×R_z = 4×1. The total acquisition times of blip-up EPI, blip-down EPI, TOPUP, Hybrid-space SENSE and 3D-BUDA are 12 s, 12 s, 22 s, 22 s, and 22 s, respectively. A 2-s FOV-matched FLASH low-resolution scan for coil sensitivity map is included in these experiments. Local SNR values are calculated for all the approaches. Region of interest is drawn in the Hybrid-space SENSE result in axial view (see red square in the first row).

Figure 5.

Comparison of different approaches (SENSE, TOPUP, Hybrid-space SENSE, and 3D-BUDA) on image quality and distortion-correction effect for the same 2-shot BUDA at 7T. First column: Blip-up SENSE results. Second column: Blip-down SENSE results. Third column: TOPUP results. Fourth column: Hybrid-space SENSE results. Last column: The 3D-BUDA image reconstruction results. Three rows are the three planes of 3D imaging. In this dataset, R_inplane×R_z = 5×2. The total acquisition times of blip-up EPI, blip-down EPI, TOPUP, Hybrid-space SENSE and 3D-BUDA are 5.5 s, 5.5 s, 9 s, 9 s, and 9 s, respectively. A 2-s FOV-matched FLASH low-resolution scan for coil sensitivity map is counted in these acquisitions.

Figure 6.

Comparison of different approaches with different sampling patterns (lower left corner of each subpart) on image quality for 3D-BUDA dataset with the same sampling amount (TA: 47 s). The difference maps were located in the lower-right corner of each subpart. First column: conventional 4-shot R_z = 1 without and with joint structured low-rank reconstruction. Second column: conventional 8-shot sampling without and with joint structured low-rank reconstruction. Third column: 8-shot with CAIPIRINHA sampling without and with joint structured low-rank reconstruction. Three columns in each subpart are the three echoes of 3D-BUDA imaging. The numbers in the sampling mask represent the acquisition order of the shots.

Figure 7.

Comparison of the Bland-Altman plots displaying the mean and difference of T₂* mapping generated by 3D-Joint-CAIPI-BUDA image reconstruction and standard multi-contrast GRE. (a) T₂* mapping generated by 3D-Joint-CAIPI-BUDA (8-shot, R_inplane×R_z = 8×2). (b) Selected regions of interest for Bland-Altman plots. (c) 3D-Joint-CAIPI-BUDA (R_inplane×R_z = 8×2, 8-shot) vs. standard multi-contrast GRE (mean: GRE = 51.58 vs. 3D-Joint-CAIPI-BUDA = 52.88).