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• W3166259372 endingPage "e583" @default.
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• W3166259372 abstract "The use of low-field magnetic resonance imaging (LF-MRI) scanners has increased in recent years. The low economic cost in comparison to high-field (HF-MRI) scanners and the ease of maintenance make this type of scanner the best choice for nonmedical purposes. However, LF-MRI scanners produce low-quality images, which encourages the identification of optimization procedures to generate the best possible images. In this paper, optimization of the image acquisition procedure for an LF-MRI scanner is presented, and predictive models are developed. The MRI acquisition procedure was optimized to determine the physicochemical characteristics of pork loin in a nondestructive way using MRI, feature extraction algorithms and data processing methods. The most critical parameters (relaxation times, repetition time, and echo time) of the LF-MRI scanner were optimized, presenting a procedure that could be easily reproduced in other environments or for other purposes. In addition, two feature extraction algorithms (gray level co-occurrence matrix (GLCM) and one point fractal texture algorithm (OPFTA)) were evaluated. The optimization procedure was validated by using several evaluation metrics, achieving reliable and accurate results ( r > 0.85; weighted absolute percentage error (WAPE) lower than 0.1%; root mean square error of prediction (RMSEP) lower than 0.1%; true standard deviation (TSTD) lower than 2; and mean absolute error (MAE) lower than 2). These results support the high degree of feasibility and accuracy of the optimized procedure of LF-MRI acquisition. No other papers present a procedure to optimize the image acquisition process in LF-MRI. Eventually, the optimization procedure could be applied to other LF-MRI systems." @default.
• W3166259372 created "2021-06-22" @default.
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• W3166259372 date "2021-06-07" @default.
• W3166259372 modified "2023-10-14" @default.
• W3166259372 title "Optimization of the image acquisition procedure in low-field MRI for non-destructive analysis of loin using predictive models" @default.
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• W3166259372 doi "https://doi.org/10.7717/peerj-cs.583" @default.
• W3166259372 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8205300" @default.
• W3166259372 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34179451" @default.
• W3166259372 hasPublicationYear "2021" @default.
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