Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study

Acceleration of knee magnetic resonance imaging using a combination of compressed sensing and commercially available deep learning reconstruction: a preliminary study

Abstract Purpose To evaluate whether deep learning reconstruction (DLR) accelerates the acquisition of 1.5-T magnetic resonance imaging (MRI) knee data without image deterioration. Materials and methods Twenty-one healthy volunteers underwent MRI of the right knee on a 1.5-T MRI scanner. Proton-dens...

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Bibliographic Details
Main Authors: Hiroyuki Akai, Koichiro Yasaka, Haruto Sugawara, Taku Tajima, Masaru Kamitani, Toshihiro Furuta, Masaaki Akahane, Naoki Yoshioka, Kuni Ohtomo, Osamu Abe, Shigeru Kiryu
Format: Article
Language:English
Published: BMC 2023-01-01
Series:BMC Medical Imaging
Subjects:
Artificial intelligence
Deep learning
Magnetic resonance imaging
Knee
Online Access:https://doi.org/10.1186/s12880-023-00962-2

Internet

https://doi.org/10.1186/s12880-023-00962-2

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