Physics-driven synthetic data learning for biomedical magnetic resonance: The imaging physics-based data synthesis paradigm for artificial intelligence
Deep learning (DL) has driven innovation in the field of computational imaging. One of its
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
bottlenecks is unavailable or insufficient training data. This article reviews an emerging …
Review and prospect: deep learning in nuclear magnetic resonance spectroscopy
Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
impact on academic research and industry. Nowadays, DL provides an unprecedented way …
Accelerated MR spectroscopic imaging—a review of current and emerging techniques
Over more than 30 years in vivo MR spectroscopic imaging (MRSI) has undergone an
enormous evolution from theoretical concepts in the early 1980s to the robust imaging …
enormous evolution from theoretical concepts in the early 1980s to the robust imaging …
DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning
Purpose To improve the estimation of coil sensitivity functions from limited auto‐calibration
signals (ACS) in SENSE‐based reconstruction for brain imaging. Methods We propose to …
signals (ACS) in SENSE‐based reconstruction for brain imaging. Methods We propose to …
Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and …
R Rizzo, M Dziadosz, SP Kyathanahally… - Magnetic resonance …, 2023 - Wiley Online Library
Purpose The aims of this work are (1) to explore deep learning (DL) architectures,
spectroscopic input types, and learning designs toward optimal quantification in MR …
spectroscopic input types, and learning designs toward optimal quantification in MR …
Denoising single MR spectra by deep learning: Miracle or mirage?
M Dziadosz, R Rizzo… - Magnetic resonance in …, 2023 - Wiley Online Library
Purpose The inherently poor SNR of MRS measurements presents a significant hurdle to its
clinical application. Denoising by machine or deep learning (DL) was proposed as a …
clinical application. Denoising by machine or deep learning (DL) was proposed as a …
Model‐informed unsupervised deep learning approaches to frequency and phase correction of MRS signals
Purpose A supervised deep learning (DL) approach for frequency and phase correction
(FPC) of MRS data recently showed encouraging results, but obtaining transients with labels …
(FPC) of MRS data recently showed encouraging results, but obtaining transients with labels …
[HTML][HTML] Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data
Purpose While the recommended analysis method for magnetic resonance spectroscopy
data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach …
data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach …
Machine learning-enabled high-resolution dynamic deuterium MR spectroscopic imaging
Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been
recognized as a potentially powerful tool for noninvasive imaging of brain energy …
recognized as a potentially powerful tool for noninvasive imaging of brain energy …
Deep tomographic image reconstruction: yesterday, today, and tomorrow—editorial for the 2nd special issue “Machine Learning for Image Reconstruction”
As a follow-up to the first IEEE Transactions on Medical Imaging (TMI) special issue on the
theme of deep tomographic reconstruction, the second special issue is assembled to reflect …
theme of deep tomographic reconstruction, the second special issue is assembled to reflect …