Bridging the resolution gap in alpha therapy dosimetry: a space for quantitative MRI?

Area of Science:

• Nuclear medicine
• Medical imaging
• Computational modeling

Background:

• External beam radiotherapy heavily relies on pre-treatment imaging for planning and dosimetry.
• Systemic radiopharmaceutical therapies lack advanced tools for predicting local dose delivery.
• Targeted alpha particle therapies require specialized micro- and mesoscale dosimetry due to short range and high-energy deposition.

Purpose of the Study:

• To explore advancements in Magnetic Resonance Imaging (MRI) and computational modeling for radionuclide therapy.
• To bridge the gap between diagnostic imaging and personalized radionuclide treatment planning.
• To enable patient-specific predictions of radiation dose delivery at a biologically relevant scale.

Main Methods:

• Review of dynamic susceptibility contrast MRI for assessing tumor perfusion and vascular permeability.
• Exploration of diffusion MRI for microscale dosimetry insights (e.g., cell size, density).
• Integration of MRI data with computational modeling strategies for radionuclide transport.

Main Results:

• Dynamic susceptibility contrast MRI reveals patient-specific tumor heterogeneity impacting drug delivery.
• Diffusion MRI offers potential for microscale dosimetry relevant to celluar environment.
• Combined MRI and modeling approaches can enhance understanding of radionuclide transport in tumors.

Conclusions:

• MRI techniques show promise in improving personalized radionuclide treatment planning.
• Advanced MRI can provide crucial data for micro- and mesoscale dosimetry in targeted therapies.
• Integrating imaging and computational modeling is key to predicting patient-specific dose delivery.