A review of machine learning applications for the proton MR spectroscopy workflow
DMJ van de Sande, JP Merkofer… - Magnetic …, 2023 - Wiley Online Library
This literature review presents a comprehensive overview of machine learning (ML)
applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS …
applications in proton MR spectroscopy (MRS). As the use of ML techniques in MRS …
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 …
[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 …
[HTML][HTML] Spectro-vit: A vision transformer model for gaba-edited mega-press reconstruction using spectrograms
This study investigated the use of a Vision Transformer (ViT) for reconstructing GABA-edited
Magnetic Resonance Spectroscopy (MRS) data from a reduced number of transients …
Magnetic Resonance Spectroscopy (MRS) data from a reduced number of transients …
[HTML][HTML] Simultaneous multi-region detection of GABA+ and Glx using 3D spatially resolved SLOW-editing and EPSI-readout at 7T
GABA+ and Glx (glutamate and glutamine) are widely studied metabolites, yet the commonly
used magnetic resonance spectroscopy (MRS) techniques have significant limitations …
used magnetic resonance spectroscopy (MRS) techniques have significant limitations …
TensorFit: A torch‐based tool for ultrafast metabolite fitting of large MRSI data sets
F Turco, M Capiglioni, G Weng… - Magnetic resonance in …, 2024 - Wiley Online Library
Purpose To introduce a tool (TensorFit) for ultrafast and robust metabolite fitting of MRSI
data based on Torch's auto‐differentiation and optimization framework. Methods TensorFit …
data based on Torch's auto‐differentiation and optimization framework. Methods TensorFit …
Simultaneous multi‐transient linear‐combination modeling of MRS data improves uncertainty estimation
Purpose The interest in applying and modeling dynamic MRS has recently grown. Two‐
dimensional modeling yields advantages for the precision of metabolite estimation in …
dimensional modeling yields advantages for the precision of metabolite estimation in …
Recurrent neural network-aided processing of incomplete free induction decays in 1H-MRS of the brain
In the case of limited sampling windows or truncation of free induction decays (FIDs) for
artifact removal in proton magnetic resonance spectroscopy (1 H‐MRS) and spectroscopic …
artifact removal in proton magnetic resonance spectroscopy (1 H‐MRS) and spectroscopic …
Application of a 1H brain MRS benchmark dataset to deep learning for out-of-voxel artifacts
AT Gudmundson, CW Davies-Jenkins… - Imaging …, 2023 - direct.mit.edu
Neural networks are potentially valuable for many of the challenges associated with MRS
data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains …
data. The purpose of this manuscript is to describe the AGNOSTIC dataset, which contains …
A Review of Machine Learning Applications for the Proton Magnetic Resonance Spectroscopy Workflow
This literature review presents a comprehensive overview of machine learning (ML)
applications in proton magnetic resonance spectroscopy (MRS). As the use of ML …
applications in proton magnetic resonance spectroscopy (MRS). As the use of ML …