Quantitative Multi Energy Computed Tomography (MECT) for the Characterization of Composition and Density Maps of Artificial and Human Materials in Proton Therapy



A novel multi-energy computed tomography (MECT) simulation framework to determine the densities of human tissues and material compositions of implant material maps for proton Monte Carlo does performs the calculation.

Key Benefits

  • Provides a multi-image computer tomography based alternative methodology of providing complete maps of material composition and mass densities that are required for patients with implants in MC-based proton treatment.
  • May reduce toxicity to healthy tissues.

Market Summary

Multi-energy computed tomography (MECT) enables the differentiation and classification of tissues by exposing the tissues to two different X-ray spectra or using a combination detector with two different energy ranges. It’s used to enhance the characterization of human tissues and is essential for treatment planning and dosing for radiotherapy. However, a fundamental limitation of MECT is that it has difficulty distinguishing human tissues from surgical implants inside the body, leading to challenges in delivering optimal therapy to patients with implants. New algorithms are needed to help radiologists accurately differentiate tissues from implants.

Technical Summary

MECT is a novel simulation framework for determining the densities of human tissues as well as material composition of implant material maps during proton therapy utilizing the Monte Carlo calculation method. Using a simulated phantom study researchers found the MECT framework predicted the mass densities and proton counting to a superior degree than single energy commutated tomography simulator; demonstrating that the framework can provide complete maps of material composition and mass densities necessary for surgical implants patients in Monte Carlo-based proton therapy.

Developmental Stage

Simulation framework designed and validated by using a phantom study.

Patent Information

Tech ID: 20215
Published: 7/6/2022