Sensitivity Encoding for Aligned Multishot Magnetic Resonance Reconstruction

Abstract

This paper introduces a framework for the reconstruction of magnetic resonance images in the presence of rigid motion. The rationale behind our proposal is to make use of the partial k-space information provided by multiple receiver coils in order to estimate the position of the imaged object throughout the shots that contribute to the image. The estimated motion is incorporated into the reconstruction model in an iterative manner to obtain a motion-free image. The method is parameter-free, does not assume any prior model for the image to be reconstructed, avoids blurred images due to resampling, does not make use of external sensors, and does not require modifications in the acquisition sequence. Validation is performed using synthetically corrupted data to study the limits for full motion-recovered reconstruction in terms of the amount of motion, encoding trajectories, number of shots and availability of prior information, and to compare with the state of the art. Quantitative and visual results of its application to a highly challenging volumetric brain imaging cohort of 207 neonates are also presented, showing the ability of the proposed reconstruction to generally improve the quality of reconstructed images, as evaluated by both sparsity and gradient entropy based metrics.

A dedicated neonatal brain imaging system

“…anatomical sequences at higher resolution (0.8 mm 3 ), the reduction of partial voluming effects (10) allowing improved anatomical segmentation (11,12), and an improved sensitivity profile that allows more robust motion correction techniques to be employed (13,14). In addition, echo planar imaging (EPI) sequences such as those used for dMRI and fMRI can be accelerated significantly using simultaneous multislice methods (15-18) with higher multiband (MB) factors as unfolding of aliased slices depends on localized coil sensitivities (19,20).…”

Annual Research Review: Not just a small adult brain: understanding later neurodevelopment through imaging the neonatal brain

“…To account for this, sequences designed for compliant, still, awake adults are being redesigned from the bottom up to allow faster (Smith-Collins, Luyt, Heep, & Kauppinen, 2015) or quieter imaging (Solana, Menini, Sacolick, Hehn, & Wiesinger, 2016), though not yet both. Novel approaches to motion correction using both prospective (Kuperman et al, 2011) and retrospective techniques have increased the success of structural (Cordero-Grande et al, 2016) and functional/diffusion data acquisition in difficult samples (Maziero et al, 2016). These approaches are particularly relevant in the case of foetal imaging, where motion is extreme.…”

Network Accelerated Motion Estimation and Reduction (NAMER): Convolutional neural network guided retrospective motion correction using a separable motion model

“…Next, a single slice was chosen from the imaging volume and NAMER was performed using both a single cost function (Equation ), and a separable cost function (Equation for all shots). Motion correction using an alternating minimization (only the 2nd and 3rd steps of the NAMER loop) was also performed with both a single and separable cost function to compare with previous methods . No intrashot motion corruption or correction was used for the comparison experiment.…”

“…On the other hand, retrospective methods correct the data after the acquisition, possibly incorporating information from navigators or trackers. Data‐driven retrospective approaches operating without tracker or navigator input are attractive because they minimally impact the clinical workflow . These algorithms estimate the motion parameters providing the best parallel imaging model agreement through the addition of motion operators in the encoding .…”