AI-based reconstruction for fast MRI—a systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
Compressed sensing for body MRI
The introduction of compressed sensing for increasing imaging speed in magnetic
resonance imaging (MRI) has raised significant interest among researchers and clinicians …
resonance imaging (MRI) has raised significant interest among researchers and clinicians …
DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction
Compressed sensing magnetic resonance imaging (CS-MRI) enables fast acquisition, which
is highly desirable for numerous clinical applications. This can not only reduce the scanning …
is highly desirable for numerous clinical applications. This can not only reduce the scanning …
Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: database‐free deep learning for fast imaging
M Akçakaya, S Moeller, S Weingärtner… - Magnetic resonance …, 2019 - Wiley Online Library
Purpose To develop an improved k‐space reconstruction method using scan‐specific deep
learning that is trained on autocalibration signal (ACS) data. Theory Robust artificial‐neural …
learning that is trained on autocalibration signal (ACS) data. Theory Robust artificial‐neural …
Harnessing the therapeutic potential of extracellular vesicles for biomedical applications using multifunctional magnetic nanomaterials
Extracellular vesicles (eg, exosomes) carrying various biomolecules (eg, proteins, lipids,
and nucleic acids) have rapidly emerged as promising platforms for many biomedical …
and nucleic acids) have rapidly emerged as promising platforms for many biomedical …
[HTML][HTML] From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction
Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
Current applications and future directions of deep learning in musculoskeletal radiology
P Chea, JC Mandell - Skeletal radiology, 2020 - Springer
Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of
artificial intelligence that is ideally suited to solving image-based problems. There are an …
artificial intelligence that is ideally suited to solving image-based problems. There are an …
Techniques for minimizing sedation in pediatric MRI
SZ Dong, M Zhu, D Bulas - Journal of magnetic resonance …, 2019 - Wiley Online Library
MRI is used widely in infants and young children. However, in these young cases deep
sedation or general anesthesia is often required to minimize motion artifacts during MRI …
sedation or general anesthesia is often required to minimize motion artifacts during MRI …
Deep-learning-based multi-modal fusion for fast MR reconstruction
T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired
magnetic resonance (MR) modalities that can provide complementary information for clinical …
magnetic resonance (MR) modalities that can provide complementary information for clinical …
[HTML][HTML] Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging
J Herrmann, G Koerzdoerfer, D Nickel, M Mostapha… - Diagnostics, 2021 - mdpi.com
Magnetic Resonance Imaging (MRI) of the musculoskeletal system is one of the most
common examinations in clinical routine. The application of Deep Learning (DL) …
common examinations in clinical routine. The application of Deep Learning (DL) …