Journal article
Implementation and evaluation of a dynamic contrast enhanced mr perfusion protocol for glioblastoma using a 0.35T mri-Linac system
ArXiv.org, 103316
2023-04-19
PMID: 37131875
MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3DT1w) and dynamic contrast enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35T MRI-Linac. The protocol implemented was used to acquire 3DT1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35T-MRI-Linac. The detection of post-contrast enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35T-MRI-Linac to images obtained using a 3T-standalone scanner. The DCE data were tested temporally and spatially using data from the flow phantom and patients. K-trans maps were derived from DCE at three time points (a week before treatment: Pre RT, four weeks through treatment: Mid RT, and three weeks after treatment: Post RT) and were validated with patients treatment outcomes. The 3D-T1 contrast enhancement volumes were visually and volumetrically similar (+/- 0.6-3.6%) between 0.35T MRI-Linac and 3T. DCE images showed temporal stability, and associated K-trans maps were consistent with patient response to treatment. On average, K-trans values showed a 54% decrease and 8.6% increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. Our findings support the feasibility of obtaining post-contrast 3DT1w and DCE data from patients with glioblastoma using a 0.35T MRI-Linac system.
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Details
- Title
- Implementation and evaluation of a dynamic contrast enhanced mr perfusion protocol for glioblastoma using a 0.35T mri-Linac system
- Creators
- Danilo MazieroGregory AzzamMacarena de La FuenteRadka StoyanovaJohn Chetley FordEric Albert Mellon
- Publication Details
- ArXiv.org, 103316
- Grant note
- National Center for Advancing Translational Sciences of the National Institutes of Health (NIH): UL1TR002736 National Cancer Institute of the NIH: R37CA262510, K12CA226330 Dwoskin Cancer Research Fund
Acknowledgements This work was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH) under Award Number UL1TR002736, and the National Cancer Institute of the NIH under Award Number R37CA262510 and K12CA226330 as well as Sylvester Comprehensive Cancer Center intramural support, and the Dwoskin Cancer Research Fund. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other supporters. The authors would like to thank Dr. Yu-Cherng Chang for sharing the MATLAB codes used as base for analyzing the perfusion data and Dr. Brunno M. Campos for helping with the atlas -based volumetric analysis.
- Academic Unit
- Miller School of Medicine; UMMG Dept of Radiation Oncology
- Language
- English
- Resource Type
- Journal article
- PMID
- 37131875
- Record Identifier
- 991031800010502976