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Use of magnetic resonance imaging to predict the body composition of pigs in vivo

Published online by Cambridge University Press:  11 December 2012

P. V. Kremer ,
M. Förster  and
A. M. Scholz
P. V. Kremer
Affiliation:
Faculty of Agriculture, University of Applied Sciences Weihenstephan-Triesdorf, Steingruberstr. 2, D-91746 Weidenbach, Germany Livestock Center of the Veterinary Faculty, University of Munich, St. Hubertusstr. 12, D-85764 Oberschleissheim, Germany
M. Förster
Affiliation:
Livestock Center of the Veterinary Faculty, University of Munich, St. Hubertusstr. 12, D-85764 Oberschleissheim, Germany Chair for Animal Breeding and Husbandry, Department of Veterinary Sciences, University of Munich, D-80539 München, Germany
A. M. Scholz*
Affiliation:
Livestock Center of the Veterinary Faculty, University of Munich, St. Hubertusstr. 12, D-85764 Oberschleissheim, Germany
*
†E-mail: [email protected], [email protected], [email protected]
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Abstract

The objective of the study was to evaluate whether magnetic resonance imaging (MRI) offers the opportunity to reliably analyze body composition of pigs in vivo. Therefore, the relation between areas of loin eye muscle and its back fat based on MRI images were used to predict body composition values measured by dual energy X-ray absorptiometry (DXA). During the study, a total of 77 pigs were studied by MRI and DXA, with a BW ranging between 42 and 102 kg. The pigs originated from different extensive or conventional breeds or crossbreds such as Cerdo Iberico, Duroc, German Landrace, German Large White, Hampshire and Pietrain. A Siemens Magnetom Open was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the loin eye area (MRI-LA) and the above back fat area (MRI-FA) of both body sides, whereas a whole body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (DXA-FM; DXA-%FM) and lean tissue mass (DXA-LM; DXA-%LM). A linear single regression analysis was performed to quantify the linear relationships between MRI- and DXA-derived traits. In addition, a stepwise regression procedure was carried out to calculate (multiple) regression equations between MRI and DXA variables (including BW). Single regression analyses showed high relationships between DXA-%FM and MRI-FA (R2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R2 = 0). As a result of the multiple regression analysis, DXA-LM and DXA-FM were both highly related to MRI-LA, MRI-FA and BW (R2 = 0.96; √MSE = 1784 g, and R2 = 0.95, √MSE = 1496 g). Therefore, it can be concluded that the use of MRI-derived images provides exact information about important ‘carcass-traits’ in pigs and may be used to reliably predict the body composition in vivo.

Keywords

MRIin vivo body compositionpigs
Type
Breeding and genetics
Information
animal , Volume 7 , Issue 6 , June 2013 , pp. 879 - 884
Copyright
Copyright © The Animal Consortium 2012

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References

Baulain, U 1997. Magnetic resonance imaging for the in vivo determination of body composition in animal science. Computers and Electronics in Agriculture 17, 189–203.CrossRefGoogle Scholar
Davenel, A, Seigneurin, F, Collewet, G, Rémignon, H 2000. Estimation of poultry breastmeat yield: magnetic resonance imaging as a tool to improve the positioning of ultrasonic scanners. Meat Science 56, 153–158.Google Scholar
Houghton, PL, Turlington, LM 1992. Application of ultrasound for feeding and finishing animals: a review. Journal of Animal Science 70, 930–941.CrossRefGoogle ScholarPubMed
Kernerová, N, Václavovský, J, Matoušek, V, Hanyková, Z 2006. The use of performance test parameters for selection of gilts before their placement into breeding. Czech Journal of Animal Science 51, 253–261.Google Scholar
Kongsro, J, Røe, M, Kvaal, K, Aastveit, AH, Egelandsdal, B 2009. Prediction of fat, muscle and value in Norwegian lamb carcasses using EUROP classification, carcass shape and length measurements, visible light reflectance and computer tomography (CT). Meat Science 81, 102–107.Google Scholar
Kremer, PV, Fernández-Fígares, I, Förster, M, Scholz, AM 2012. In vivo body composition in autochthonous and conventional pig breeding groups by dual-energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) under special consideration of Cerdo Iberico. Animal, in print. http://dx.doi.org/10.1017/S1751731112001267.Google Scholar
Lösel, D, Kremer, PV, Albrecht, E, Scholz, AM 2010. Comparison of a GE Lunar DPX-IQ and a Norland XR-26 dual energy X-ray absorptiometry scanner for body composition measurements in pigs – in vivo. Archives Animal Breeding 53, 162–175.Google Scholar
Lukaski, HC, Marchello, MJ, Hall, CB, Schafer, DM, Siders, WA 1999. Soft tissue composition of pigs measured with dual X-ray absorptiometry: comparison with chemical analyses and effects of carcass thicknesses. Nutrition 15, 697–703.CrossRefGoogle ScholarPubMed
Marcoux, M, Faucitano, L, Pomar, C 2005. The accuracy of predicting carcass composition of three different pig genetic lines by dual-energy X-ray absorptiometry. Meat Science 70, 655–663.Google Scholar
Mitchell, AD, Conway, JM, Potts, WJE 1996. Body composition analysis of pigs by dual energy X-ray absorptiometry. Journal of Animal Science 74, 2663–2671.CrossRefGoogle ScholarPubMed
Mitchell, AD, Scholz, AM, Conway, JM 1998. Body composition analysis of small pigs by dual-energy X-ray absorptiometry. Journal of Animal Science 76, 2392–2398.Google Scholar
Mitchell, AD, Scholz, AM, Conway, JM 1998a. Body composition analysis of pigs from 5 to 97 kg by dual energy X-ray absorptiometry. Applied Radiation and Isotopes 49, 521–523.Google Scholar
Mitchell, AD, Scholz, AM, Pursel, VG 2000. Dual energy X-ray absorptiometry measurements of the body composition of pigs of 90- to 130-kilograms body weight. Annals of the New York Academy of Sciences 904, 85–93.Google Scholar
Mitchell, AD, Scholz, AM, Wang, PC, Song, H 2001. Body composition analysis of the pig by magnetic resonance imaging. Journal of Animal Science 79, 1800–1813.Google Scholar
Monziols, M, Collewet, G, Bonneau, M, Mariette, F, Davenel, A, Kouba, M 2006. Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Science 72, 146–154.CrossRefGoogle ScholarPubMed
Moon, KL Jr, Genant, HK, Helms, CA, Chafetz, NI, Crooks, LE, Kaufman, L 1983. Musculoskeletal applications of nuclear magnetic resonance. Radiology 147, 161–171.Google Scholar
Müller, S, Polten, S 2004. Comparative investigations for ultrasonic fat thickness measurements of pigs at the performance testing. Archives Animal Breeding 47, 249–263.CrossRefGoogle Scholar
Pietrobelli, A, Formica, C, Wang, Z, Heymsfield, SB 1996. Dual-energy X-ray absorptiometry body composition model: review of physical concepts. American Journal of Physiology – Endocrinology and Metabolism 271, E941–E951.CrossRefGoogle ScholarPubMed
Ruge, A 2006. Evaluation of the accuracy of a Norland XR26 DXA system in comparison with a GE Lunar DPX-IQ applying a modified variable composition phantom. Doctoral thesis, University Munich, Munich, Germany. http://edoc.ub.uni-muenchen.de/6605/1/Ruge_Anja.pdfGoogle Scholar
Scholz, AM 2002. In vivo techniques for the analysis of muscle metabolism and body composition in pigs of different genotypes. Habilitation thesis, University Munich, Germany. http://epub.ub.uni-muenchen.de/418/1/Scholz_Armin.pdfGoogle Scholar
Scholz, AM, Baulain, U 2009. Methods of determination of body composition in living animals. Zuechtungskunde 81, 86–96.Google Scholar
Scholz, AM, Baulain, U, Kallweit, E 1993. Quantitative analysis of magnetic resonance tomography images. Zuechtungskunde 65, 206–215.Google Scholar
Scholz, A, Soffner, P, Littmann, E, Peschke, W, Foerster, M 2002. Accuracy of dual energy X-ray absorptiometry (DXA) measurements for the determination of the composition of carcass halfs (cold, 30 – 39 kg) from swine in comparison to the EU reference dissection. Zuechtungskunde 74, 376–391.Google Scholar
Scholz, AM, Förster, M 2006. Accuracy of dual energy X-ray absorptiometry (DXA) for the determination of the body composition of pigs in vivo. Archives Animal Breeding 49, 462–476.CrossRefGoogle Scholar
Streitz, E, Baulain, U, Kallweit, E 1995. Untersuchungen zur Koerperzusammensetzung wachsender Laemmer mit Hilfe der Magnet-Resonanz-Tomographie (MRT). Zuechtungskunde 67, 392–403.Google Scholar
Suster, D, Leury, BJ, Ostrowska, E, Butler, KL, Kerton, DJ, Wark, JD, Dunschea, FR 2003. Accuracy of dual energy X-ray absorptiometry (DXA), weight and P2 back fat to predict whole body and carcass composition in pigs within and across experiments. Livestock Production Science 84, 231–242.Google Scholar
Szabo, C, Babinszky, L, Verstegen, MWA, Vangen, O, Jansman, AJM, Kanis, E 1999. The application of digital imaging techniques in the in vivo estimation of the body composition of pigs: a review. Livestock Production Science 60, 1–11.Google Scholar