Interleaved EPI diffusion imaging using SPIR i T ‐based reconstruction with virtual coil compression

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133

Purpose To develop an efficient distortion‐ and blurring‐free multi‐shot EPI technique for time‐resolved multiple‐contrast and/or quantitative imaging. Methods EPI is a commonly used sequence but suffers from geometric distortions and blurring. Here, we introduce a new multi‐shot EPI technique termed echo planar time‐resolved imaging (EPTI), which has the ability to rapidly acquire distortion‐ and blurring‐free multi‐contrast data set. The EPTI approach performs encoding in ky‐t space and uses a new highly accelerated spatio–temporal CAIPI sampling trajectory to take advantage of signal correlation along these dimensions. Through this acquisition and a B0‐informed parallel imaging reconstruction, hundreds of “time‐resolved” distortion‐ and blurring‐free images at different TEs across the EPI readout window can be created at sub‐millisecond temporal increments using a small number of EPTI shots. Moreover, a method for self‐estimation and correction of shot‐to‐shot B0 variations was developed. Simultaneous multi‐slice acquisition was also incorporated to further improve the acquisition efficiency. Results We evaluated EPTI under varying simulated acceleration factors, B0‐inhomogeneity, and shot‐to‐shot B0 variations to demonstrate its ability to provide distortion‐ and blurring‐free images at multiple TEs. Two variants of EPTI were demonstrated in vivo at 3T: (1) a combined gradient‐ and spin‐echo EPTI for quantitative mapping of T2, T2*, proton density, and susceptibility at 1.1 × 1.1 × 3 mm3 whole‐brain in 28 s (0.8 s/slice), and (2) a gradient‐echo EPTI, for multi‐echo and quantitative T2* fMRI at 2 × 2 × 3 mm3 whole‐brain at a 3.3 s temporal resolution. Conclusion EPTI is a new approach for multi‐contrast and/or quantitative imaging that can provide fast acquisition of distortion‐ and blurring‐free images at multiple TEs.

Section: Introductionmentioning

confidence: 99%

Echo planar time‐resolved imaging (EPTI)

Wang

Dong

Reese

et al. 2019

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133

“…As such, existing msEPI techniques circumvent this difficulty by dividing the reconstruction into 2 separate parts: shot‐phase estimation and combination of multishot given the estimated phase information. Navigator‐based approaches derive this phase information from additional calibration acquisitions made for each shot . Diffusion imaging with MUSE and its extensions operate without a navigator and perform the phase‐estimation step using parallel imaging to reconstruct a complex image for each shot.…”

Section: Discussionmentioning

confidence: 99%

“…MUSSELS has allowed R inplane = 8‐fold acceleration per each shot in msEPI diffusion imaging using as few as 4 shots (R net = 2). Unlike earlier navigator‐based or navigator‐free approaches, where the number of acquired shots was equal to the in‐plane acceleration factor (

N_{s}

= R inplane ), MUSSELS could thus perform in the (

N_{s}

< R inplane ) regime to improve acquisition efficiency. With SMS‐NEATR, we pushed the efficiency gain even further to enable R net = 8‐fold acceleration (R inplane × MB = 8 × 2 with 2 shots) in structural imaging and R net = 3.6‐fold (R inplane × MB = 9 × 2 with 5 shots) in diffusion imaging.…”

Section: Discussionmentioning

confidence: 99%

Highly accelerated multishot echo planar imaging through synergistic machine learning and joint reconstruction

Bilgic̦

Chatnuntawech

Manhard

et al. 2019

Purpose To introduce a combined machine learning (ML)‐ and physics‐based image reconstruction framework that enables navigator‐free, highly accelerated multishot echo planar imaging (msEPI) and demonstrate its application in high‐resolution structural and diffusion imaging. Methods Single‐shot EPI is an efficient encoding technique, but does not lend itself well to high‐resolution imaging because of severe distortion artifacts and blurring. Although msEPI can mitigate these artifacts, high‐quality msEPI has been elusive because of phase mismatch arising from shot‐to‐shot variations which preclude the combination of the multiple‐shot data into a single image. We utilize deep learning to obtain an interim image with minimal artifacts, which permits estimation of image phase variations attributed to shot‐to‐shot changes. These variations are then included in a joint virtual coil sensitivity encoding (JVC‐SENSE) reconstruction to utilize data from all shots and improve upon the ML solution. Results Our combined ML + physics approach enabled Rinplane × multiband (MB) = 8‐ × 2‐fold acceleration using 2 EPI shots for multiecho imaging, so that whole‐brain T2 and T2* parameter maps could be derived from an 8.3‐second acquisition at 1 × 1 × 3‐mm3 resolution. This has also allowed high‐resolution diffusion imaging with high geometrical fidelity using 5 shots at Rinplane × MB = 9‐ × 2‐fold acceleration. To make these possible, we extended the state‐of‐the‐art MUSSELS reconstruction technique to simultaneous multislice encoding and used it as an input to our ML network. Conclusion Combination of ML and JVC‐SENSE enabled navigator‐free msEPI at higher accelerations than previously possible while using fewer shots, with reduced vulnerability to poor generalizability and poor acceptance of end‐to‐end ML approaches.

“…Finally, phase correction methods that are insensitive to the mismatch between image‐echo and navigator are preferable in this situation. Currently, these include several k‐space‐based methods in which generalized autocalibrating partially parallel acquisitions (GRAPPA) or iterative self‐consistent parallel imaging reconstruction (SPIRiT) is used for data recovery …”

Section: Introductionmentioning

confidence: 99%

The effects of navigator distortion and noise level on interleaved EPI DWI reconstruction: a comparison between image‐ and k‐space‐based method

Dai

Zhang

et al. 2018

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