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Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.

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Title: Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.
Authors: Zhao, Ruiyang1,2 (AUTHOR), Hernando, Diego1,2 (AUTHOR), Harris, David T.1 (AUTHOR), Hinshaw, Louis A.3 (AUTHOR), Li, Ke1,2 (AUTHOR), Ananthakrishnan, Lakshmi4 (AUTHOR), Bashir, Mustafa R.5,6,7 (AUTHOR), Duan, Xinhui4 (AUTHOR), Ghasabeh, Mounes Aliyari8 (AUTHOR), Kamel, Ihab R.8 (AUTHOR), Lowry, Carolyn5 (AUTHOR), Mahesh, Mahadevappa8 (AUTHOR), Marin, Daniele5 (AUTHOR), Miller, Jessica9 (AUTHOR), Pickhardt, Perry J.1 (AUTHOR), Shaffer, Jean5,6 (AUTHOR), Yokoo, Takeshi4 (AUTHOR), Brittain, Jean H.10 (AUTHOR), Reeder, Scott B.1,2,3,11,12 (AUTHOR) sreeder@wisc.edu
Source: Medical Physics. Aug2021, Vol. 48 Issue 8, p4375-4386. 12p.
Subjects: General Electric Co., Magnetic resonance imaging, Computed tomography, Fatty liver, Fat, Four-dimensional imaging, Agar
Abstract: Purpose: Chemical shift‐encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat‐fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. Methods: An agar‐based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four‐site, three‐vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI‐PDFF (%) and CT number (HU). Mixed‐effects model, linear regression, and Bland‐Altman analysis were used for statistical analysis. Results: MRI‐PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = −0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI‐PDFF and CT number, replicating established in vivo signal behavior. Excellent test‐retest repeatability across vendors (MRI: mean = −0.04%, 95% limits of agreement = [−0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [−0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = −0.21%, 95% limits of agreement = [−1.47%, 1.06%]; CT: mean = −0.18HU, 95% limits of agreement = [−1.96HU, 1.6HU]) were demonstrated. Conclusions: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom.
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  Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Ruiyang%22">Zhao, Ruiyang</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hernando%2C+Diego%22">Hernando, Diego</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Harris%2C+David+T%2E%22">Harris, David T.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hinshaw%2C+Louis+A%2E%22">Hinshaw, Louis A.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Ke%22">Li, Ke</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ananthakrishnan%2C+Lakshmi%22">Ananthakrishnan, Lakshmi</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bashir%2C+Mustafa+R%2E%22">Bashir, Mustafa R.</searchLink><relatesTo>5,6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Duan%2C+Xinhui%22">Duan, Xinhui</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ghasabeh%2C+Mounes+Aliyari%22">Ghasabeh, Mounes Aliyari</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kamel%2C+Ihab+R%2E%22">Kamel, Ihab R.</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lowry%2C+Carolyn%22">Lowry, Carolyn</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mahesh%2C+Mahadevappa%22">Mahesh, Mahadevappa</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Marin%2C+Daniele%22">Marin, Daniele</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Miller%2C+Jessica%22">Miller, Jessica</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Pickhardt%2C+Perry+J%2E%22">Pickhardt, Perry J.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shaffer%2C+Jean%22">Shaffer, Jean</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yokoo%2C+Takeshi%22">Yokoo, Takeshi</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brittain%2C+Jean+H%2E%22">Brittain, Jean H.</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Reeder%2C+Scott+B%2E%22">Reeder, Scott B.</searchLink><relatesTo>1,2,3,11,12</relatesTo> (AUTHOR)<i> sreeder@wisc.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Aug2021, Vol. 48 Issue 8, p4375-4386. 12p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22General+Electric+Co%2E%22">General Electric Co.</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Computed+tomography%22">Computed tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Fatty+liver%22">Fatty liver</searchLink><br /><searchLink fieldCode="DE" term="%22Fat%22">Fat</searchLink><br /><searchLink fieldCode="DE" term="%22Four-dimensional+imaging%22">Four-dimensional imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Agar%22">Agar</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Purpose: Chemical shift‐encoded magnetic resonance imaging enables accurate quantification of liver fat content though estimation of proton density fat‐fraction (PDFF). Computed tomography (CT) is capable of quantifying fat, based on decreased attenuation with increased fat concentration. Current quantitative fat phantoms do not accurately mimic the CT number of human liver. The purpose of this work was to develop and validate an optimized phantom that simultaneously mimics the MRI and CT signals of fatty liver. Methods: An agar‐based phantom containing 12 vials doped with iodinated contrast, and with a granular range of fat fractions was designed and constructed within a novel CT and MR compatible spherical housing design. A four‐site, three‐vendor validation study was performed. MRI (1.5T and 3T) and CT images were obtained using each vendor's PDFF and CT reconstruction, respectively. An ROI centered in each vial was placed to measure MRI‐PDFF (%) and CT number (HU). Mixed‐effects model, linear regression, and Bland‐Altman analysis were used for statistical analysis. Results: MRI‐PDFF agreed closely with nominal PDFF values across both field strengths and all MRI vendors. A linear relationship (slope = −0.54 ± 0.01%/HU, intercept = 37.15 ± 0.03%) with an R2 of 0.999 was observed between MRI‐PDFF and CT number, replicating established in vivo signal behavior. Excellent test‐retest repeatability across vendors (MRI: mean = −0.04%, 95% limits of agreement = [−0.24%, 0.16%]; CT: mean = 0.16 HU, 95% limits of agreement = [−0.15HU, 0.47HU]) and good reproducibility using GE scanners (MRI: mean = −0.21%, 95% limits of agreement = [−1.47%, 1.06%]; CT: mean = −0.18HU, 95% limits of agreement = [−1.96HU, 1.6HU]) were demonstrated. Conclusions: The proposed fat phantom successfully mimicked quantitative liver signal for both MRI and CT. The proposed fat phantom in this study may facilitate broader application and harmonization of liver fat quantification techniques using MRI and CT across institutions, vendors and imaging platforms. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/mp.15038
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        Text: English
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      – SubjectFull: General Electric Co.
        Type: general
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      – SubjectFull: Computed tomography
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      – SubjectFull: Fatty liver
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      – SubjectFull: Fat
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      – SubjectFull: Four-dimensional imaging
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