Diffusion tensor transformation for personalizing target volumes in radiation therapy.

Area of Science:

• Medical imaging
• Computational modeling
• Radiotherapy

Background:

• Diffusion tensor imaging (DTI) provides crucial data on tumor cell infiltration pathways in brain tissue.
• Patient-specific DTI is often unavailable, necessitating the use of template images and image registration.
• Accurate tumor growth modeling is essential for effective radiotherapy treatment planning.

Purpose of the Study:

• To investigate the invariance under coordinate transform (ICT) model for transforming diffusion tensors from template images to patient anatomy.
• To enable the mapping of tumor cell densities and fronts for radiotherapy planning using ICT.
• To simulate tumor growth in deformed patient anatomy without requiring patient-specific DTI.

Main Methods:

• Developed and applied the invariance under coordinate transform (ICT) model to transform diffusion tensors.
• Mapped tumor cell densities and tumor fronts from a template DTI atlas to patient anatomy.
• Simulated tumor growth dynamics and cell density distribution over time in locally deformed anatomy.

Main Results:

• The ICT model accurately simulated tumor cell densities in the deformed image space of brain tumor patients.
• Comparative analysis confirmed the model's accuracy against original template image data.
• The approach successfully transformed diffusion tensors to model tumor growth in patient-specific anatomical contexts.

Conclusions:

• The ICT model offers a validated framework for simulating tumor growth and cell density distribution.
• This method facilitates personalized radiotherapy treatment planning by defining target volumes based on simulated tumor fronts.
• The ICT model enables personalized treatment planning without the need for additional patient-specific imaging data.