English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Purpose: To introduce a combined machine learning (ML) and physics-basedimage reconstruction framework that enables navigator-free, highly acceleratedmultishot echo planar imaging (msEPI), and demonstrate its application inhigh-resolution structural and diffusion imaging.  Methods: Singleshot EPI is an efficient encoding technique, but does not lenditself well to high-resolution imaging due to severe distortion artifacts andblurring. While msEPI can mitigate these artifacts, high-quality msEPI has beenelusive because of phase mismatch arising from shot-to-shot variations whichpreclude the combination of the multiple-shot data into a single image. Weemploy deep learning to obtain an interim image with minimal artifacts, whichpermits estimation of image phase variations due to shot-to-shot changes. Thesevariations are then included in a Joint Virtual Coil Sensitivity Encoding(JVC-SENSE) reconstruction to utilize data from all shots and improve upon theML solution.  Results: Our combined ML + physics approach enabled Rinplane x MultiBand (MB)= 8x2-fold acceleration using 2 EPI-shots for multi-echo imaging, so thatwhole-brain T2 and T2* parameter maps could be derived from an 8.3 secacquisition at 1x1x3mm3 resolution. This has also allowed high-resolutiondiffusion imaging with high geometric fidelity using 5-shots at Rinplane x MB =9x2-fold acceleration. To make these possible, we extended the state-of-the-artMUSSELS reconstruction technique to Simultaneous MultiSlice (SMS) encoding andused it as an input to our ML network.  Conclusion: Combination of ML and JVC-SENSE enabled navigator-free msEPI athigher accelerations than previously possible while using fewer shots, withreduced vulnerability to poor generalizability and poor acceptance ofend-to-end ML approaches.

Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction