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Updated: Jul 6, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Speed optimized influence matrix processing in inverse treatment planning tools.

Peter Ziegenhein¹, Jan J Wilkens, Simeon Nill

¹German Cancer Research Center (DKFZ), Department of Medical Physics in Radiation Oncology, Heidelberg, Germany. p.ziegenhein@dkfz.de

Physics in Medicine and Biology

|April 11, 2008

Summary

This summary is machine-generated.

This study introduces a novel data reordering method to accelerate radiation therapy planning. By optimizing data transfer between memory and CPU, the algorithm achieves a 2.6x speedup, improving treatment planning efficiency.

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Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

Area of Science:

Medical Physics
Computational Biology
Radiotherapy

Background:

Modern radiation therapy planning relies on iterative optimization algorithms processing extensive patient data.
Calculating optimal treatment plans is time-consuming, limiting clinical and research applications.
The dose influence matrix (DIJ) is a common method for managing radiation dose data, but data transfer bottlenecks exist.

Purpose of the Study:

To introduce a new method for accelerating data transportation from stored dose data to the CPU.
To address the bottleneck in treatment planning optimization caused by data transfer limitations.
To improve the efficiency of radiation therapy treatment planning.

Main Methods:

A data cycle reordering method was developed to optimize data transfer between memory and CPU.
The method was implemented and tested using the dose influence matrix (DIJ) approach in the KonRad treatment planning tool.
A minimal eviction policy was employed to enhance memory hardware utilization.

Main Results:

The proposed method resulted in a 2.6 times faster algorithm compared to the naive implementation.
The data reordering significantly improved the efficiency of accessing stored dose data.
The optimization time for treatment planning was substantially reduced.

Conclusions:

The developed data reordering method effectively speeds up radiation therapy planning by optimizing data transfer.
This approach offers significant performance improvements for treatment planning tools utilizing dose influence matrices.
The method has the potential to benefit various planning tools with similar data storage strategies, enhancing clinical efficiency and research accuracy.