Differentiation of Myositis-Induced Models of Bacterial Infection and Inflammation with T2-Weighted, CEST, and DCE-MRI
| Autor: | Mark D. Pagel, Julio Cárdenas-Rodríguez, Alexander J. Berthusen, Joshua M. Goldenberg |
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| Rok vydání: | 2019 |
| Předmět: |
DCE-MRI
CEST-MRI Contrast Media Inflammation Sensitivity and Specificity imaging infection 030218 nuclear medicine & medical imaging Mice Random Allocation 03 medical and health sciences 0302 clinical medicine Nuclear magnetic resonance Normal muscle principal components analysis Image Interpretation Computer-Assisted T2-weighted MRI medicine Animals Radiology Nuclear Medicine and imaging Research Articles Escherichia coli Infections Myositis medicine.diagnostic_test Chemistry Area under the curve Magnetic resonance imaging medicine.disease Magnetic Resonance Imaging 3. Good health Disease Models Animal machine learning Saturation transfer Area Under Curve Mice Inbred CBA Classification methods Female medicine.symptom T2 weighted 030217 neurology & neurosurgery |
| Zdroj: | Tomography; Volume 5; Issue 3; Pages: 283-291 Tomography Volume 5 Issue 3 Pages 283-291 |
| ISSN: | 2379-139X |
| DOI: | 10.18383/j.tom.2019.00009 |
| Popis: | 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 histological 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. |
| Databáze: | OpenAIRE |
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