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Analysis of mice tumor models using dynamic MRI data and a dedicated software platform*.
1Department of Radiology, Philipps University Marburg. alfke@mailer.uni-marburg.de
Rofo : Fortschritte Auf Dem Gebiete Der Rontgenstrahlen Und Der Nuklearmedizin
|September 4, 2004
Summary
A new software platform, DynaVision, effectively analyzes tumor perfusion using dynamic contrast-enhanced MRI in mouse models. This tool differentiates tumor types based on vascular characteristics, aiding in cancer research.
Area of Science:
- Biomedical Imaging
- Medical Software Development
- Oncology Research
Background:
- Functional imaging of tumors provides critical data for understanding cancer biology and treatment response.
- Accurate analysis of dynamic contrast-enhanced imaging requires specialized software for parameter extraction and visualization.
- Pancreatic carcinoma xenografts in mice are valuable models for preclinical cancer studies.
Purpose of the Study:
- To develop and validate the DynaVision software platform for analyzing functional imaging data of tumors.
- To assess the software's capability in analyzing pancreatic carcinoma xenografts in severe combined immunodeficiency (SCID) mice.
- To explore different mathematical approaches for tumor data analysis using the DynaVision platform.
Main Methods:
- Development of DynaVision software for extracting and visualizing tissue perfusion parameters from dynamic contrast-enhanced MRI.
- Implementation of regional parameter calculation, parametric imaging (e.g., blood flow), 3D visualization, and motion correction.
- Analysis of xenograft tumors from BxPC3 and ASPC1 pancreatic carcinoma cell lines in SCID mice, correlating imaging data with histopathology.
Main Results:
- DynaVision enabled rapid image analysis (approx. 15 minutes per dataset) with user-friendly ROI selection for quantitative data extraction.
- Motion correction successfully addressed artifacts, allowing for continued data analysis.
- Dynamic MRI revealed heterogeneous contrast enhancement patterns correlating with histopathology, distinguishing hypervascular ASPC1 tumors from hypovascular BxPC3 tumors.
Conclusions:
- The DynaVision software effectively analyzes tissue perfusion parameters in small animal tumor models.
- Quantitative and qualitative perfusion parameters derived from DynaVision can differentiate tumor entities with distinct growth characteristics.
- The software's ability to correlate imaging data with histopathology enhances its utility in preclinical cancer research.