TY - JOUR
T1 - Differentiation of myositis-induced models of bacterial infection and inflammation with t2-weighted, cest, and dce-mri
AU - Goldenberg, Joshua M.
AU - Berthusen, Alexander J.
AU - Cárdenas-Rodríguez, Julio
AU - Pagel, Mark D.
N1 - Funding Information:
We extend special thanks to Drs. Sanjay Jain and Alvaro Ordoñez of Johns Hopkins University for providing advice on bacterial culturing methods for in vivo imaging studies. We would also like to thank Gillian Paine-Murrieta and Betsy Dennison of the TACMASR facility for assisting us with tissue sample harvesting and H&E-and Gram-stained microscope slides. This work was supported by NIH grants R01CA169774,
Funding Information:
We extend special thanks to Drs. Sanjay Jain and Alvaro Ordo?ez of Johns Hopkins University for providing advice on bacterial culturing methods for in vivo imaging studies. We would also like to thank Gillian Paine-Murrieta and Betsy Dennison of the TACMASR facility for assisting us with tissue sample harvesting and H&E-and Gram-stained microscope slides. This work was supported by NIH grants R01CA169774, P30CA023074, and P50CA95060, and by the Institutional Research Grant number 128749-IRG-16-124-37-IRG from the American Cancer Society.
Funding Information:
P30CA023074, and P50CA95060, and by the Institutional Research Grant number 128749-IRG-16-124-37-IRG from the American Cancer Society.
Publisher Copyright:
© 2019 The Authors. Published by Grapho Publications, LLC This is an open access article under the CC BY-NC-ND license.
PY - 2019/9
Y1 - 2019/9
N2 - We used T2 relaxation, chemical exchange saturation transfer (CEST), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to assess whether bacterial infection can be differentiated from inflammation in a myositis-induced mouse model. We measured the T2 relaxation time constants, %CEST at 5 saturation frequencies, and area under the curve (AUC) from DCE-MRI after maltose injection from infected, inflamed, and normal muscle tissue models. We applied principal component analysis (PCA) to reduce dimensionality of entire CEST spectra and DCE signal evolutions, which were analyzed using standard classification methods. We extracted features from dimensional reduction as predictors for machine learning classifier algorithms. Normal, inflamed, and infected tissues were evaluated with H&E and gram-staining his-tological studies, and bacterial-burden studies. The T2 relaxation time constants and AUC of DCE-MRI after injection of maltose differentiated infected, inflamed, and normal tissues. %CEST amplitudes at =1.6 and =3.5 ppm differentiated infected tissues from other tissues, but these did not differentiate inflamed tissue from normal tissue. %CEST amplitudes at 3.5, 3.0, and 2.5 ppm, AUC of DCE-MRI for shorter time periods, and relative Ktrans and kep values from DCE-MRI could not differentiate tissues. PCA and machine learning of CEST-MRI and DCE-MRI did not improve tissue classifications relative to traditional analysis methods. Similarly, PCA and machine learning did not further improve tissue classifications relative to T2 MRI. Therefore, future MRI studies of infection models should focus on T2-weighted MRI and analysis of T2 relaxation times.
AB - We used T2 relaxation, chemical exchange saturation transfer (CEST), and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) to assess whether bacterial infection can be differentiated from inflammation in a myositis-induced mouse model. We measured the T2 relaxation time constants, %CEST at 5 saturation frequencies, and area under the curve (AUC) from DCE-MRI after maltose injection from infected, inflamed, and normal muscle tissue models. We applied principal component analysis (PCA) to reduce dimensionality of entire CEST spectra and DCE signal evolutions, which were analyzed using standard classification methods. We extracted features from dimensional reduction as predictors for machine learning classifier algorithms. Normal, inflamed, and infected tissues were evaluated with H&E and gram-staining his-tological studies, and bacterial-burden studies. The T2 relaxation time constants and AUC of DCE-MRI after injection of maltose differentiated infected, inflamed, and normal tissues. %CEST amplitudes at =1.6 and =3.5 ppm differentiated infected tissues from other tissues, but these did not differentiate inflamed tissue from normal tissue. %CEST amplitudes at 3.5, 3.0, and 2.5 ppm, AUC of DCE-MRI for shorter time periods, and relative Ktrans and kep values from DCE-MRI could not differentiate tissues. PCA and machine learning of CEST-MRI and DCE-MRI did not improve tissue classifications relative to traditional analysis methods. Similarly, PCA and machine learning did not further improve tissue classifications relative to T2 MRI. Therefore, future MRI studies of infection models should focus on T2-weighted MRI and analysis of T2 relaxation times.
KW - CEST-MRI
KW - DCE-MRI
KW - Imaging infection
KW - Machine learning
KW - Principal components analysis
KW - T2-weighted MRI
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U2 - 10.18383/j.tom.2019.00009
DO - 10.18383/j.tom.2019.00009
M3 - Article
C2 - 31572789
AN - SCOPUS:85072783504
VL - 5
SP - 283
EP - 291
JO - Tomography
JF - Tomography
SN - 2379-1381
IS - 3
ER -