Fast and accurate compensation of signal offset for T
Algorithms
Humans
Image Interpretation, Computer-Assisted
/ methods
Image Processing, Computer-Assisted
/ methods
Least-Squares Analysis
Magnetic Resonance Imaging
Male
Phantoms, Imaging
Probability
Prostatic Neoplasms
/ diagnostic imaging
Regression Analysis
Reproducibility of Results
Signal-To-Noise Ratio
Software
Time Factors
Algorithms
Least-squares analysis
Software
Journal
Magma (New York, N.Y.)
ISSN: 1352-8661
Titre abrégé: MAGMA
Pays: Germany
ID NLM: 9310752
Informations de publication
Date de publication:
Aug 2019
Aug 2019
Historique:
received:
23
07
2018
accepted:
07
01
2019
revised:
06
01
2019
pubmed:
8
2
2019
medline:
13
2
2020
entrez:
8
2
2019
Statut:
ppublish
Résumé
T Our approach gains speed by converting the 3-parameter minimisation problem into an empirically unimodal univariate problem, which is quickly minimised using the golden section line search (GS). To enable comparison, we propose a novel noise-masking algorithm. For clinical data, the agreement between the GS and the LM fit is excellent, yet the GS algorithm is two orders of magnitude faster. For synthetic data, the accuracy of the GS algorithm is on par with that of the LM fit, and the GS algorithm is significantly faster. The GS algorithm requires no parametrisation or initialisation by the user. The new GS T
Identifiants
pubmed: 30730022
doi: 10.1007/s10334-019-00737-3
pii: 10.1007/s10334-019-00737-3
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
423-436Subventions
Organisme : Ministerstvo Školství, Mládeže a Tělovýchovy
ID : LM2015062
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