Median (interquartile range) reconstruction times for ML and NUFFT reconstruction for in-vivo MR-Linac data.

Median (interquartile range) reconstruction times for ML and NUFFT reconstruction for in-vivo MR-Linac data.

<p>Median (interquartile range) reconstruction times for ML and NUFFT reconstruction for in-vivo MR-Linac data.</p>

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Bibliographic Details
Main Author: Johanna Topalis (22633911) (author)
Other Authors: Jakob Dexl (22633914) (author), Katharina Jeblick (22633917) (author), Rabea Klaar (15281581) (author), Christopher Kurz (815721) (author), Timo Löhr (22633920) (author), Andreas Mittermeier (22633923) (author), Balthasar Schachtner (22633926) (author), Anna Theresa Stüber (22633929) (author), Tobias Weber (6330914) (author), Philipp Wesp (22633932) (author), Jens Ricke (74030) (author), Max Seidensticker (659591) (author), Guillaume Landry (815668) (author), Michael Ingrisch (465023) (author), Olaf Dietrich (629688) (author)
Published: 2025
Subjects:
Cell Biology
Biotechnology
Immunology
Science Policy
Space Science
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
mean squared error
high temporal resolution
time magnetic resonance
tesla mri system
linac dynamic measurements
radially undersampled k
mse distributions overlapped
iterative nufft techniques
median reconstruction time
fast image reconstruction
real mri data
interpolate radial k
iterative nufft reconstruction
image ml model
reconstruction time
image reconstruction
radial k
iterative zero
latency real
image quality
reconstruction times
synthetic data
phantom data
tesla mr
mr measurements
xlink ">
undersampling factors
three volunteers
reconstructing 0
r </
natural images
ml model
ml approach
machine learning
interquartile ranges
imaging applications
ideally suited
ground truth
diagnostic 1
conventional approach
compressed sensing
combined images
cartesian grid
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