AIMC Topic: Magnetic Resonance Spectroscopy

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Developing and deploying deep learning models in brain magnetic resonance imaging: A review.

NMR in biomedicine
Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models...

Deep learning pipeline for quality filtering of MRSI spectra.

NMR in biomedicine
With the rise of novel 3D magnetic resonance spectroscopy imaging (MRSI) acquisition protocols in clinical practice, which are capable of capturing a large number of spectra from a subject's brain, there is a need for an automated preprocessing pipel...

AI-designed NMR spectroscopy RF pulses for fast acquisition at high and ultra-high magnetic fields.

Nature communications
Nuclear magnetic resonance (NMR) spectroscopy is a powerful high-resolution tool for characterizing biomacromolecular structure, dynamics, and interactions. However, the lengthy longitudinal relaxation of the nuclear spins significantly extends the t...

Magnetic Resonance Spectroscopy Spectral Registration Using Deep Learning.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Deep learning-based methods have been successfully applied to MRI image registration. However, there is a lack of deep learning-based registration methods for magnetic resonance spectroscopy (MRS) spectral registration (SR).

A review of machine learning applications for the proton MR spectroscopy workflow.

Magnetic resonance in medicine
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 continues to grow, this review aims to provide the MRS community with a structured over...

DC-cycleGAN: Bidirectional CT-to-MR synthesis from unpaired data.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to so...

Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting.

Magnetic resonance in medicine
PURPOSE: To propose a novel end-to-end deep learning model to quantify absolute metabolite concentrations from in vivo J-point resolved spectroscopy (JPRESS) without using spectral fitting.

Muscle magnetic resonance characterization of STIM1 tubular aggregate myopathy using unsupervised learning.

PloS one
PURPOSE: Congenital myopathies are a heterogeneous group of diseases affecting the skeletal muscles and characterized by high clinical, genetic, and histological variability. Magnetic Resonance (MR) is a valuable tool for the assessment of involved m...

Comprehensive dose evaluation of a Deep Learning based synthetic Computed Tomography algorithm for pelvic Magnetic Resonance-only radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Magnetic Resonance (MR)-only radiotherapy enables the use of MR without the uncertainty of MR-Computed Tomography (CT) registration. This requires a synthetic CT (sCT) for dose calculations, which can be facilitated by a novel...