Optimizing radiotherapy plans for cancer treatment with Tensor Networks

Area of Science:

• Computational physics and medical physics
• Application of quantum computing in healthcare

Background:

• Radiotherapy dose optimization is crucial for effective cancer treatment.
• Intensity-modulated radiation therapy (IMRT) requires complex optimization of beamlet intensities.
• Balancing tumor dose delivery with sparing of healthy organs is a significant challenge.

Purpose of the Study:

• To introduce Tensor Network methods as a novel tool for radiotherapy dose optimization.
• To map the radiotherapy dose optimization problem to a quantum-like Hamiltonian.
• To explore the potential of quantum technologies in medical treatments.

Main Methods:

• Formulating the radiotherapy dose optimization problem as a cost function.
• Mapping the cost function to an Ising-like Hamiltonian representing interacting qubits.
• Utilizing a Tree Tensor Network algorithm to find the Hamiltonian's ground-state.

Main Results:

• Successfully mapped the dose optimization problem to a Hamiltonian solvable by Tensor Networks.
• Demonstrated the method's application in a prostate cancer treatment scenario.
• Validated the potential for Tensor Network algorithms in solving complex medical optimization problems.

Conclusions:

• Tensor Network methods provide a promising computational approach for radiotherapy dose optimization.
• This work paves the way for integrating quantum computing in advanced cancer treatment strategies.
• The proposed method could enhance precision and reduce side effects in radiation therapy.