Low‐field MRI of stroke: challenges and opportunities
Stroke is a leading cause of death and disability worldwide. The reasons for increased
stroke burden in developing countries are inadequately controlled risk factors resulting from …
stroke burden in developing countries are inadequately controlled risk factors resulting from …
Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends
Quantitative mapping of MR tissue parameters such as the spin‐lattice relaxation time (T1),
the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ) …
the spin‐spin relaxation time (T2), and the spin‐lattice relaxation in the rotating frame (T1ρ) …
Quantitative imaging of apoptosis following oncolytic virotherapy by magnetic resonance fingerprinting aided by deep learning
Non-invasive imaging methods for detecting intratumoural viral spread and host responses
to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or …
to oncolytic virotherapy are either slow, lack specificity or require the use of radioactive or …
[HTML][HTML] pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis
Abstract Background and Objective: Deep learning enables tremendous progress in medical
image analysis. One driving force of this progress are open-source frameworks like …
image analysis. One driving force of this progress are open-source frameworks like …
[HTML][HTML] Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging
Novel methods for quantitative, transient-state multiparametric imaging are increasingly
being demonstrated for assessment of disease and treatment efficacy. Here, we build on …
being demonstrated for assessment of disease and treatment efficacy. Here, we build on …
Deep model-based magnetic resonance parameter mapping network (DOPAMINE) for fast T1 mapping using variable flip angle method
Quantitative tissue characteristics, which provide valuable diagnostic information, can be
represented by magnetic resonance (MR) parameter maps using magnetic resonance …
represented by magnetic resonance (MR) parameter maps using magnetic resonance …
Magnetic resonance fingerprinting using recurrent neural networks
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic
resonance imaging that allows simultaneous measurement of multiple tissue properties in a …
resonance imaging that allows simultaneous measurement of multiple tissue properties in a …
Deep learning for brain disorders: from data processing to disease treatment
In order to reach precision medicine and improve patients' quality of life, machine learning is
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …
increasingly used in medicine. Brain disorders are often complex and heterogeneous, and …
Quantitative imaging metrics derived from magnetic resonance fingerprinting using ISMRM/NIST MRI system phantom: An international multicenter repeatability and …
Purpose To compare the bias and inherent reliability of the quantitative (T1 and T2) imaging
metrics generated from the magnetic resonance fingerprinting (MRF) technique using the …
metrics generated from the magnetic resonance fingerprinting (MRF) technique using the …
Geometry of deep learning for magnetic resonance fingerprinting
Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are
bottlenecked by the heavy storage and computation requirements of a dictionary-matching …
bottlenecked by the heavy storage and computation requirements of a dictionary-matching …