Literature DB >> 23228200

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

P V Kremer1, M Förster, A M Scholz.   

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 (R 2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R 2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R 2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R 2 = 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 (R 2 = 0.96; √MSE = 1784 g, and R 2 = 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.

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Year:  2012        PMID: 23228200     DOI: 10.1017/S1751731112002340

Source DB:  PubMed          Journal:  Animal        ISSN: 1751-7311            Impact factor:   3.240


  5 in total

Review 1.  Segmentation and quantification of adipose tissue by magnetic resonance imaging.

Authors:  Houchun Harry Hu; Jun Chen; Wei Shen
Journal:  MAGMA       Date:  2015-09-04       Impact factor: 2.310

2.  Non-invasive methods for the determination of body and carcass composition in livestock: dual-energy X-ray absorptiometry, computed tomography, magnetic resonance imaging and ultrasound: invited review.

Authors:  A M Scholz; L Bünger; J Kongsro; U Baulain; A D Mitchell
Journal:  Animal       Date:  2015-03-06       Impact factor: 3.240

Review 3.  Use of Magnetic Resonance Imaging in Food Quality Control: A Review.

Authors:  Hamed Ebrahimnejad; Hadi Ebrahimnejad; A Salajegheh; H Barghi
Journal:  J Biomed Phys Eng       Date:  2018-03-01

4.  Growth hormone receptor-deficient pigs resemble the pathophysiology of human Laron syndrome and reveal altered activation of signaling cascades in the liver.

Authors:  Arne Hinrichs; Barbara Kessler; Mayuko Kurome; Andreas Blutke; Elisabeth Kemter; Maren Bernau; Armin M Scholz; Birgit Rathkolb; Simone Renner; Sebastian Bultmann; Heinrich Leonhardt; Martin Hrabĕ de Angelis; Hiroshi Nagashima; Andreas Hoeflich; Werner F Blum; Martin Bidlingmaier; Rüdiger Wanke; Maik Dahlhoff; Eckhard Wolf
Journal:  Mol Metab       Date:  2018-03-15       Impact factor: 7.422

5.  Metabolic syndrome and extensive adipose tissue inflammation in morbidly obese Göttingen minipigs.

Authors:  Simone Renner; Andreas Blutke; Britta Dobenecker; Georg Dhom; Timo D Müller; Brian Finan; Christoffer Clemmensen; Maren Bernau; Istvan Novak; Birgit Rathkolb; Steffanie Senf; Susanne Zöls; Mirjam Roth; Anna Götz; Susanna M Hofmann; Martin Hrabĕ de Angelis; Rüdiger Wanke; Ellen Kienzle; Armin M Scholz; Richard DiMarchi; Mathias Ritzmann; Matthias H Tschöp; Eckhard Wolf
Journal:  Mol Metab       Date:  2018-06-28       Impact factor: 7.422

  5 in total

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