Magnetic resonance fingerprinting: from evolution to clinical applications
JJL Hsieh, I Svalbe - Journal of Medical Radiation Sciences, 2020 - Wiley Online Library
Abstract In 2013, Magnetic Resonance Fingerprinting (MRF) emerged as a method for fast,
quantitative Magnetic Resonance Imaging. This paper reviews the current status of MRF up …
quantitative Magnetic Resonance Imaging. This paper reviews the current status of MRF up …
Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography
Deep convolutional neural networks (CNNs) have emerged as a new paradigm for
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …
[图书][B] Machine learning for tomographic imaging
The area of machine learning, especially deep learning, has exploded in recent years,
producing advances in everything from speech recognition and gaming to drug discovery …
producing advances in everything from speech recognition and gaming to drug discovery …
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 …
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 decomposition learning for inverse imaging problems
Deep learning is emerging as a new paradigm for solving inverse imaging problems.
However, the deep learning methods often lack the assurance of traditional physics-based …
However, the deep learning methods often lack the assurance of traditional physics-based …
Submillimeter MR fingerprinting using deep learning–based tissue quantification
Purpose To develop a rapid 2D MR fingerprinting technique with a submillimeter in‐plane
resolution using a deep learning–based tissue quantification approach. Methods A rapid …
resolution using a deep learning–based tissue quantification approach. Methods A rapid …
Accelerated white matter lesion analysis based on simultaneous T1 and T2∗ quantification using magnetic resonance fingerprinting and deep learning
I Hermann, E Martínez‐Heras, B Rieger… - Magnetic …, 2021 - Wiley Online Library
Purpose To develop an accelerated postprocessing pipeline for reproducible and efficient
assessment of white matter lesions using quantitative magnetic resonance fingerprinting …
assessment of white matter lesions using quantitative magnetic resonance fingerprinting …
Cramér–Rao bound‐informed training of neural networks for quantitative MRI
Purpose To improve the performance of neural networks for parameter estimation in
quantitative MRI, in particular when the noise propagation varies throughout the space of …
quantitative MRI, in particular when the noise propagation varies throughout the space of …
Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting
Magnetic resonance fingerprinting (MRF) provides a unique concept for simultaneous and
fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency …
fast acquisition of multiple quantitative MR parameters. Despite acquisition efficiency …