Evaluation of slice accelerations using multiband echo planar imaging at 3 Tesla

By Junqian Xu, Steen Moeller, E. Auerbach, J. Strupp, Stephen M. Smith, David A. Feinberg, Essa Yacoub, Kamil Ugurbil. NeuroImage, 2013

516 citations recorded by the source corpus.

We evaluate residual aliasing among simultaneously excited and acquired slices in slice accelerated multiband (MB) echo planar imaging (EPI). No in-plane accelerations were used in order to maximize and evaluate achievable slice acceleration factors at 3 Tesla. We propose a novel leakage (L-) factor to quantify the effects of signal leakage between simultaneously acquired slices. With a standard 32-channel receiver coil at 3 Tesla, we demonstrate that slice acceleration factors of up to eight (MB = 8) with blipped controlled aliasing in parallel imaging (CAIPI), in the absence of in-plane accelerations, can be used routinely with acceptable image quality and integrity for whole brain imaging. Spectral analyses of single-shot fMRI time series demonstrate that temporal fluctuations due to both neuronal and physiological sources were distinguishable and comparable up to slice-acceleration factors of nine (MB = 9). The increased temporal efficiency could be employed to achieve, within a given acquisition period, higher spatial resolution, increased fMRI statistical power, multiple TEs, faster sampling of temporal events in a resting state fMRI time series, increased sampling of q-space in diffusion imaging, or more quiet time during a scan.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4e4976472bdf73baaba218dac16df6790ac3cb8e

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Citations in the corpus

ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging (2014, methodology): “R Slice accelerated EPI has seen rapid adoption in the neuroimaging community, for example, in the Human Connectome Project and the most recent Thousand Connectomes datasets from the Nathan Kline Institute.”
Origins of the brain networks for advanced mathematics in expert mathematicians (2016, unknown): “We used a 3-Tesla whole body system (Siemens Trio) with a 32-channel head-coil and high-resolution multiband imaging sequences developed by the Center for Magnetic Resonance Research (CMRR) (58) [multiband factor = 4, Grappa factor = 2, 80 interleaved axial slices, 1.5-mm thickness and 1.5-mm isotro”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “Next, tissue-type segmentation is applied using FAST (FMRIB’s Automated Segmentation Tool Zhang et al. (2001)).”
The Human Connectome Project’s Neuroimaging Approach (2016, unknown): “4 ) 5,44,53–55 .”