What is machine learning, artificial neural networks and deep learning?—Examples of practical applications in medicine
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all
topics that fall under the heading of artificial intelligence (AI) and have gained popularity in …
topics that fall under the heading of artificial intelligence (AI) and have gained popularity in …
QSM throughout the body
Magnetic materials in tissue, such as iron, calcium, or collagen, can be studied using
quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied …
quantitative susceptibility mapping (QSM). To date, QSM has been overwhelmingly applied …
Zero-shot self-supervised learning for MRI reconstruction
Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but
these methods often necessitate a database of fully-sampled measurements for training …
these methods often necessitate a database of fully-sampled measurements for training …
Neuro‐Heuristic Computational Intelligence Approach for Optimization of Electro‐Magneto‐Hydrodynamic Influence on a Nano Viscous Fluid Flow
In this investigative study, the electro‐magneto hydrodynamic (EMHD) influence on a nano
viscous fluid model is scrutinized by designing an artificial neural network (ANN) paradigm …
viscous fluid model is scrutinized by designing an artificial neural network (ANN) paradigm …
LARO: Learned acquisition and reconstruction optimization to accelerate quantitative susceptibility mapping
Quantitative susceptibility mapping (QSM) involves acquisition and reconstruction of a series
of images at multi-echo time points to estimate tissue field, which prolongs scan time and …
of images at multi-echo time points to estimate tissue field, which prolongs scan time and …
Multi-muscle deep learning segmentation to automate the quantification of muscle fat infiltration in cervical spine conditions
Muscle fat infiltration (MFI) has been widely reported across cervical spine disorders. The
quantification of MFI requires time-consuming and rater-dependent manual segmentation …
quantification of MFI requires time-consuming and rater-dependent manual segmentation …
A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T
Objective This study combines a deep image prior with low-rank subspace modeling to
enable real-time (free-breathing and ungated) functional cardiac imaging on a commercial …
enable real-time (free-breathing and ungated) functional cardiac imaging on a commercial …
Comparative review of algorithms and methods for chemical‐shift‐encoded quantitative fat‐water imaging
P Daudé, T Roussel, T Troalen, P Viout… - Magnetic …, 2024 - Wiley Online Library
Purpose To propose a standardized comparison between state‐of‐the‐art open‐source fat‐
water separation algorithms for proton density fat fraction (PDFF) and R 2* R _2^ ∗ …
water separation algorithms for proton density fat fraction (PDFF) and R 2* R _2^ ∗ …
GRASPNET: fast spatiotemporal deep learning reconstruction of golden‐angle radial data for free‐breathing dynamic contrast‐enhanced magnetic resonance …
R Jafari, RKG Do, MD LaGratta, M Fung… - NMR in …, 2023 - Wiley Online Library
The purpose of the current study was to develop a deep learning technique called Golden‐
angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic …
angle RAdial Sparse Parallel Network (GRASPnet) for fast reconstruction of dynamic …
Deep learning–based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi‐echo phase training data
J Hanspach, S Bollmann, J Grigo… - Magnetic …, 2022 - Wiley Online Library
Purpose To enable a fast and automatic deep learning–based QSM reconstruction of tissues
with diverse chemical shifts, relevant to most regions outside the brain. Methods A UNET …
with diverse chemical shifts, relevant to most regions outside the brain. Methods A UNET …