CT-based pseudo-CT generation

CT-based pseudo-CT generation generates pseudo-computed tomography (pCT) images using the MRCAT commercial solution (Philips Healthcare) to enable MR-only radiotherapy dose calculations and direct comparison with CT-based IMRT dose calculations.


Key Features:

  • Pseudo-CT Image Generation: Generates pCT images using the MRCAT commercial solution (Philips Healthcare).
  • Comparison and Validation: Enables direct comparison of MR-based pCT dose calculations with CT-based IMRT dose calculations for validation.
  • Confounding Factor Analysis: Identifies and quantifies confounding factors affecting dosimetric accuracy, including different calibration curves used to convert pCT and CT into electron density and tissue stratification into a fixed number of classes.
  • Quantitative Assessment: Evaluates dose difference maps in high-dose regions (such as the clinical target volume, CTV) and body volumes to assess the impact of confounding factors on dosimetric accuracy.

Scientific Applications:

  • Radiotherapy Planning: Supports MR-only radiotherapy treatment planning and dose calculation workflows, particularly for prostate cancer patients.
  • Clinical Practice Transition: Provides a framework to assess and mitigate inaccuracies when transitioning institutions to MR-only planning.

Methodology:

Generates pCT images using MRCAT (Philips Healthcare), compares them with CT-based IMRT plans, and evaluates dose difference maps to quantify contributions of calibration curve differences and tissue stratification errors to dosimetric accuracy.

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Details

Cost:
Free of charge (with restrictions)
Tool Type:
library
Operating Systems:
Windows, Linux, Mac
Programming Languages:
MATLAB
Added:
5/5/2021
Last Updated:
11/24/2024

Operations

Publications

Maspero M, Seevinck PR, Schubert G, Hoesl MAU, van Asselen B, Viergever MA, Lagendijk JJW, Meijer GJ, van den Berg CAT. Quantification of confounding factors in MRI-based dose calculations as applied to prostate IMRT. Physics in Medicine and Biology. 2017;62(3):948-965. doi:10.1088/1361-6560/aa4fe7. PMID:28076338.

PMID: 28076338
Funding: - ZonMw IMDI: 104003010

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