Improving labeling efficiency in automatic quality control of MRSI data-Reference-Cited by-同舟云学术

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Improving labeling efficiency in automatic quality control of MRSI data

Published:2017-02-07 Issue:6 Volume:78 Page:2399-2405
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magn. Reson. Med.

Author:

Pedrosa de Barros Nuno¹²,McKinley Richard¹²,Wiest Roland¹²,Slotboom Johannes¹²

Affiliation:

1. Support Center for Advanced Neuroimaging; Inselspital; Bern Switzerland

2. University of Bern; Bern Switzerland

Funder

EU Marie Curie FP7-PEOPLE-2012-ITN project TRANSACT

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Reference24 articles.

1. Issues of spectral quality in clinical 1H-magnetic resonance spectroscopy and a gallery of artifacts;Kreis;NMR Biomed,2004

2. Reliability testing of in vivo magnetic resonance spectroscopy (MRS) signals and signal artifact reduction by order statistic filtering;Slotboom;Meas Sci Technol,2009

3. Cramér-Rao bounds: an evaluation tool for quantitation;Cavassila;NMR Biomed,2001

4. Error images for spectroscopic imaging by LCModel using Cramer-Rao bounds;Jiru;MAGMA,2006

5. The trouble with quality filtering based on relative Cramér-Rao lower bounds;Kreis;Magn Reson Med,2016

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning pipeline for quality filtering of MRSI spectra;NMR in Biomedicine;2023-07-30

2. Continuous Automated Analysis Workflow for MRS Studies;Journal of Medical Systems;2023-07-07

3. A review of machine learning applications for the proton MR spectroscopy workflow;Magnetic Resonance in Medicine;2023-07-04

4. Denoising single MR spectra by deep learning: Miracle or mirage?;Magnetic Resonance in Medicine;2023-06-18

5. Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias;Magnetic Resonance in Medicine;2022-12-19

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