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/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

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

Zendy Tools

Zendy Open

Purpose  To develop a scan‐specific model that estimates and corrects k‐space errors made when reconstructing accelerated MRI data.    Methods  Scan‐specific artifact reduction in k‐space (SPARK) trains a convolutional‐neural‐network to estimate and correct k‐space errors made by an input reconstruction technique by back‐propagating from the mean‐squared‐error loss between an auto‐calibration signal (ACS) and the input technique’s reconstructed ACS. First, SPARK is applied to generalized autocalibrating partially parallel acquisitions (GRAPPA) and demonstrates improved robustness over other scan‐specific models, such as robust artificial‐neural‐networks for k‐space interpolation (RAKI) and residual‐RAKI. Subsequent experiments demonstrate that SPARK synergizes with residual‐RAKI to improve reconstruction performance. SPARK also improves reconstruction quality when applied to advanced acquisition and reconstruction techniques like 2D virtual coil (VC‐) GRAPPA, 2D LORAKS, 3D GRAPPA without an integrated ACS region, and 2D/3D wave‐encoded imaging.    Results  SPARK yields SSIM improvement and 1.5 – 2× root mean squared error (RMSE) reduction when applied to GRAPPA and improves robustness to ACS size for various acceleration rates in comparison to other scan‐specific techniques. When applied to advanced reconstruction techniques such as residual‐RAKI, 2D VC‐GRAPPA and LORAKS, SPARK achieves up to 20% RMSE improvement. SPARK with 3D GRAPPA also improves RMSE performance by ~2×, SSIM performance, and perceived image quality without a fully sampled ACS region. Finally, SPARK synergizes with non‐Cartesian, 2D and 3D wave‐encoding imaging by reducing RMSE between 20% and 25% and providing qualitative improvements.    Conclusion  SPARK synergizes with physics‐based acquisition and reconstruction techniques to improve accelerated MRI by training scan‐specific models to estimate and correct reconstruction errors in k‐space.

Scan‐specific artifact reduction in k‐space (SPARK) neural networks synergize with physics‐based reconstruction to accelerate MRI