Deep learning for retrospective motion correction in MRI: a comprehensive review
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
the MR signal is acquired in frequency space, any motion of the imaged object leads to …
Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives
Radiofrequency (RF) coils for magnetic resonance imaging (MRI) applications serve to
generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF …
generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF …
Submillimeter lung MRI at 0.55 T using balanced steady‐state free precession with half‐radial dual‐echo readout (bSTAR)
Purpose To demonstrate the feasibility of high‐resolution morphologic lung MRI at 0.55 T
using a free‐breathing balanced steady‐state free precession half‐radial dual‐echo …
using a free‐breathing balanced steady‐state free precession half‐radial dual‐echo …
A comprehensive set of ultrashort echo time magnetic resonance imaging biomarkers to assess cortical bone health: A feasibility study at clinical field strength
Introduction Conventional bone imaging methods primarily use X-ray techniques to assess
bone mineral density (BMD), focusing exclusively on the mineral phase. This approach lacks …
bone mineral density (BMD), focusing exclusively on the mineral phase. This approach lacks …
Stop moving: MR motion correction as an opportunity for artificial intelligence
Subject motion is a long-standing problem of magnetic resonance imaging (MRI), which can
seriously deteriorate the image quality. Various prospective and retrospective methods have …
seriously deteriorate the image quality. Various prospective and retrospective methods have …
Knowledge‐driven deep learning for fast MR imaging: Undersampled MR image reconstruction from supervised to un‐supervised learning
Deep learning (DL) has emerged as a leading approach in accelerating MRI. It employs
deep neural networks to extract knowledge from available datasets and then applies the …
deep neural networks to extract knowledge from available datasets and then applies the …
Comparison of weighting algorithms to mitigate respiratory motion in free-breathing neonatal pulmonary radial UTE-MRI
Background. Thoracoabdominal MRI is limited by respiratory motion, especially in
populations who cannot perform breath-holds. One approach for reducing motion blurring in …
populations who cannot perform breath-holds. One approach for reducing motion blurring in …
[PDF][PDF] ADVANCED TECHNIQUE DEVELOPMENT AND CHARACTERIZATION FOR DYNAMIC PULMONARY MAGNETIC RESONANCE IMAGING
WJ Garrison - 2023 - libraetd.lib.virginia.edu
Magnetic resonance imaging (MRI) of the lung is challenging due to the low proton density
of lung parenchymal tissue relative to that of other soft tissue structures within the body, the …
of lung parenchymal tissue relative to that of other soft tissue structures within the body, the …