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Body matrix coil

Manufactured by Siemens
Sourced in Germany

The Body Matrix Coil is a diagnostic imaging device designed for use in magnetic resonance imaging (MRI) systems. It is a specialized radiofrequency (RF) coil that is placed around the patient's body to receive and transmit the signals required for the MRI process. The core function of the Body Matrix Coil is to enhance the quality and resolution of the MRI images by improving the signal-to-noise ratio during the imaging procedure.

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8 protocols using body matrix coil

1

Multi-Channel MRI Acquisition Setup

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All MRI experiments were performed on a 3T MRI scanner (TimTrio, Siemens Medical Solutions, Erlangen, Germany) with an eight-channel transmit array capability. The body birdcage coil in dual-drive mode was used as the transmit coil. For this, the quadrature splitter of the system was removed and the two ports of the birdcage coil were fed using two channels of the eight-channel transmit array system. A 12-channel head coil and a body matrix coil (Siemens Medical Solutions, Erlangen, Germany) were used for animal experiments as receiver coils; only the body matrix coil was used for phantom experiments.
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2

Pelvic MRI Imaging Protocol

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MR examinations were performed on a 1.5 T MR system (Siemens) with a body-matrix coil centred over the pelvis. High-resolution T2w turbo spin echo (TSE) scans were acquired in coronal and sagittal planes, followed by oblique-axial scans (perpendicular to the long axis of the rectum with 160 mm field of view (FOV), 3 mm slice thickness, no interslice gap, a matrix of 256×256 and a minimum of 4 number of signal averages (NSA)). No contrast was used during MRI examination. No diffusion-weighted images (DWI) were undertaken.
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3

Liver Fat Quantification by MRS

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Fat content in the liver was determined using 1H magnetic resonance spectroscopy (MRS). The liver spectra were obtained from a 2×2×2 cm3 voxel from two locations (right and left lobes) using a point-resolved spectroscopy (PRESS) sequence (TE=30 ms, TR=2000 ms) and a Siemens body matrix coil. The acquired spectra were fitted using the linear combination of model spectra (LCModel) (Sadananthan et al., 2015 (link); Provencher, 1993 (link)). The liver fat was determined from the concentration of methyl and methylene groups of lipids and the unsuppressed water signal and corrected for T2 losses (Cowin et al., 2008 (link)).
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4

Proton MRS for Intracellular Lipid Quantification

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Proton magnetic resonance spectroscopy (1H-MRS) was performed with a 3.0 Tesla MR system (Siemens, Tim Trio, Erlangen, Germany) using a body matrix coil and a spine matrix (Siemens, Erlangen, Germany) as shown by us previously (12 (link), 13 (link)). A total of 8 spectra (TR = 4000 ms, TE = 30 ms) were obtained in a measuring time of 32 sec without water suppression and averaged for the determination of intracellular water and lipid content. The AMARES algorithm in the Java-based magnetic resonance user interface (jMRUI) software package (22 (link)) was used to quantify the area under the curve of the methylene peak of lipid at 1.3 ppm and the water peak at 4.7 ppm. At baseline, four subjects (2 AE, 2 combined) could not complete 1H MRS due to claustrophobia. One subject in the RE group was excluded in the liver fat data analyses as this subject had 27.8% of liver fat (9 SDs above the mean). Excluding this subject did not alter the significance of our results.
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5

Automated Abdominal Fat Quantification

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The abdominal MR images were acquired from a 3T MR scanner (Tim Trio Siemens) using two-point Dixon sequence repetition time (TR)=5.28 ms, echo time (TE)1=2.45 ms, TE2=3.68 ms, FA=9°, bandwidth1=500 Hz Px−1, bandwidth2=780 Hz Px−1 and Siemens body matrix coil after anatomical localization. For the parent (I:1), 80 axial slices with 3 mm thickness, 0.6 mm interslice gap and in-plane resolution of 1.25×1.25 mm were acquired and 52 slices with in-plane resolution of 1.02×1.02 mm were acquired for two affected children (II:5 and II:6). A fully automated segmentation technique was employed to segment and quantify the abdominal fat volumes between the first (L1) and fifth (L5) lumbar vertebrae (Sadananthan et al., 2015 (link)). First, the fat tissues were separated from non-fat tissues by intensity thresholding. The extracted fat tissues were then classified into subcutaneous (SAT) and visceral (VAT) adipose tissues using graph theoretic segmentation.
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6

Non-Invasive Liver Triglyceride Quantification

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1H-MRS spectra were acquired with a 3.0 Tesla MR system (Siemens, Tim Trio, Erlangen, Germany) using a body matrix coil and a spine matrix (Siemens, Erlangen, Germany) as shown previously (14 (link), 15 (link)). Liver spectra were fitted using the AMARES algorithm in the Java-based magnetic resonance user interface (jMRUI) software package (20 (link)). The average of eight spectra was used for liver triglyceride calculation. NAFLD was defined as IHTG ≥5.0% as shown previously in adolescents (3 (link)).
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7

Cardiac MRI Assessment of Exercise-Induced Changes

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A cardiac MRI after the race was offered to all study participants with either ST-T-segment deviation or hsTnT elevation. 3T MRI (Magnetom Tim Trio; Siemens AG) was performed using a phased array receiver coil during breath-holds gated to the electrocardiogram (Body Matrix-coil #TATS; Siemens AG). Cine images of 3 long-axis and 14–18 short-axis views (slices of 4 mm) were created using a steady-state free precision technique.24 (link) Eight minutes after intravenous administration of 10–12 mL Gadovist (Bayer Schering Pharma AG) at a concentration of 1 mmol/mL, these views were repeated using a short inversion recovery sequence and continuously adjusting the inversion time.25 (link)
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8

Hepatic Fat Quantification by 1H-MRS

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1 H-MRS was performed with a 3.0 Tesla MR system (Siemens, Tim Trio, Erlangen, Germany) using a body matrix coil and a spine matrix (Siemens) using our established protocol described elsewhere (Lee et al. 2012; (link)Lee et al. 2013) (link). Fatty liver was defined as liver fat [(methylene lipid peak/methylene lipid peak + water peak) × 100] ≥ 5.0% as shown previously (Perseghin et al. 2006) (link).
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