Quantification of intramuscular fat and subcutaneous adipose tissue in rainbow trout using MRI and vision. Quantification du gras intramusculaire et sous-cutané par IRM et vision dans la truite arc-en-ciel

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Collewet, G. | Bugeon, Jérôme | Idier, J. | Quellec, S. | Quittet, Benjamin | Cambert, M. | Haffray, Pierrick | Vandeputte, Marc | Dupont-Nivet, Mathilde

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[Departement_IRSTEA]Ecotechnologies [TR1_IRSTEA]SPEE. International audience. The aim of this work was to evaluate the potentiality of Magnetic Resonance imaging (MRI) to quantify both intramuscular lipids (that is mean fat content in flesh (IF)) and subcutaneous adipose tissue (SAT) on cutlets of rainbow trouts after freezing and thawing in a view to handling large number of samples needed in selective breeding programs for example. Images of cutlets from 84 rainbow trouts c1.7±0.29 kg) were acquired with a vision system (colour scanner Epson 10000 XL pro). Images were analysed and the surface of SAT was automatically measured. The cutlets were then frozen. After thawing, they were set horizontally in a 1.5T MRI (Avanto, Siemens) four by four. Acquisition parameters were set so as to obtain the best compromise between spatial resolution and acquisition time (7min 22sec). Flesh of 27 cutlets was removed, crushed and dried for fat quantification by NMR. This led to a calibration equation between the MRI signal and the fat content. This allowed the computation of fat cartographies from which IF and SAT were measured using automatic segmentation of flesh and subcutaneous areas. SAT was compared with the results obtained with the vision system. The comparison between MRI and NMR for IF quantification led to a calibration RMSE of 0.89%. High correlations were found between the measurement of SAT with MRI and vision (R²=0.77 et 0.87 for ventral and dorsal SAT respectively). This experiment shows the potentiality of MRI to quantify both IF and SAT on frozen cutlets with an acquisition time of less than 2 minutes per cutlet. This opens the way to experiments including high number of samples.

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