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

With the recent introduction of the MR-LINAC, an MR-scanner combined with aradiotherapy LINAC, MR-based motion estimation has become of increasinginterest to (retrospectively) characterize tumor and organs-at-risk motionduring radiotherapy. To this extent, we introduce low-rank MR-MOTUS, aframework to retrospectively reconstruct time-resolved non-rigid 3D+tmotion-fields from a single low-resolution reference image and prospectivelyundersampled k-space data acquired during motion. Low-rank MR-MOTUS exploitsspatio-temporal correlations in internal body motion with a low-rank motionmodel, and inverts a signal model that relates motion-fields directly to areference image and k-space data. The low-rank model reduces thedegrees-of-freedom, memory consumption and reconstruction times by assuming afactorization of space-time motion-fields in spatial and temporal components.Low-rank MR-MOTUS was employed to estimate motion in 2D/3D abdominothoracicscans and 3D head scans. Data were acquired using golden-ratio radial readouts.Reconstructed 2D and 3D respiratory motion-fields were respectively validatedagainst time-resolved and respiratory-resolved image reconstructions, and thehead motion against static image reconstructions from fully-sampled dataacquired right before and right after the motion. Results show that 2D+trespiratory motion can be estimated retrospectively at 40.8motion-fields-per-second, 3D+t respiratory motion at 7.6motion-fields-per-second and 3D+t head-neck motion at 9.3motion-fields-per-second. The validations show good consistency with imagereconstructions. The proposed framework can estimate time-resolved non-rigid 3Dmotion-fields, which allows to characterize drifts and intra and inter-cyclepatterns in breathing motion during radiotherapy, and could form the basis forreal-time MR-guided radiotherapy.

Non-rigid 3D motion estimation at high temporal resolution from prospectively undersampled k-space data using low-rank MR-MOTUS