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Development and validation of a new MRI simulation technique that can reliably estimate optimal in vivo scanning parameters in a glioblastoma murine model
- Source :
- PLoS ONE, PLoS ONE, Vol 13, Iss 7, p e0200611 (2018)
- Publication Year :
- 2018
- Publisher :
- Public Library of Science (PLoS), 2018.
-
Abstract
- Background Magnetic Resonance Imaging (MRI) relies on optimal scanning parameters to achieve maximal signal-to-noise ratio (SNR) and high contrast-to-noise ratio (CNR) between tissues resulting in high quality images. The optimization of such parameters is often laborious, time consuming, and user-dependent, making harmonization of imaging parameters a difficult task. In this report, we aim to develop and validate a computer simulation technique that can reliably provide "optimal in vivo scanning parameters" ready to be used for in vivo evaluation of disease models. Methods A glioblastoma murine model was investigated using several MRI imaging methods. Such MRI methods underwent a simulated and an in vivo scanning parameter optimization in pre- and post-contrast conditions that involved the investigation of tumor, brain parenchyma and cerebrospinal fluid (CSF) CNR values in addition to the time relaxation values of the related tissues. The CNR tissues information were analyzed and the derived scanning parameters compared in order to validate the simulated methodology as a reliable technique for "optimal in vivo scanning parameters" estimation. Results The CNRs and the related scanning parameters were better correlated when spin-echo-based sequences were used rather than the gradient-echo-based sequences due to augmented inhomogeneity artifacts affecting the latter methods. "Optimal in vivo scanning parameters" were generated successfully by the simulations after initial scanning parameter adjustments that conformed to some of the parameters derived from the in vivo experiment. Conclusion Scanning parameter optimization using the computer simulation was shown to be a valid surrogate to the in vivo approach in a glioblastoma murine model yielding in a better delineation and differentiation of the tumor from the contralateral hemisphere. In addition to drastically reducing the time invested in choosing optimal scanning parameters when compared to an in vivo approach, this simulation program could also be used to harmonize MRI acquisition parameters across scanners from different vendors.
- Subjects :
- Gadolinium DTPA
Physiology
Computer science
lcsh:Medicine
Contrast Media
Signal-To-Noise Ratio
Nervous System
Diagnostic Radiology
030218 nuclear medicine & medical imaging
Mice
0302 clinical medicine
Cerebrospinal fluid
Medicine and Health Sciences
Blastomas
lcsh:Science
Neurological Tumors
Cerebrospinal Fluid
Multidisciplinary
medicine.diagnostic_test
Brain Neoplasms
Radiology and Imaging
Simulation and Modeling
Brain
Animal Models
Magnetic Resonance Imaging
Body Fluids
In Vivo Imaging
Experimental Organism Systems
Oncology
Neurology
Female
Anatomy
Preclinical imaging
Research Article
Imaging Techniques
Mouse Models
Neuroimaging
Research and Analysis Methods
03 medical and health sciences
Model Organisms
Diagnostic Medicine
In vivo
Cell Line, Tumor
Parenchyma
medicine
Animals
Humans
Computer Simulation
lcsh:R
Biology and Life Sciences
Cancers and Neoplasms
Relaxation (iterative method)
Magnetic resonance imaging
Image Enhancement
medicine.disease
Disease Models, Animal
Signal-to-noise ratio (imaging)
Murine model
lcsh:Q
Glioblastoma
Glioblastoma Multiforme
030217 neurology & neurosurgery
Neuroscience
Biomedical engineering
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....a46843300d12f5d641a5a17ae06fbd94