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Related Experiment Videos

A model to predict bladder shapes from changes in bladder and rectal filling.

Heidi T Lotz1, Peter Remeijer, Marcel van Herk

  • 1The Netherlands Cancer Institute/Antoni van Leeuwenhoek Huis, Department of Radiotherapy, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.

Medical Physics
|July 21, 2004
PubMed
Summary

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This study developed a 3D model to predict bladder shape changes using cone beam CT scans. The model accurately quantifies bladder and rectal volume effects on bladder shape and position.

Area of Science:

  • Medical Imaging
  • Radiation Oncology
  • Computational Anatomy

Background:

  • Accurate bladder and rectal volume quantification is crucial for precise radiation therapy.
  • Predicting bladder shape changes between imaging and treatment delivery is essential for dose accuracy.
  • Existing models may not fully capture the dynamic nature of bladder shape and position.

Purpose of the Study:

  • To develop a 3D model quantifying bladder shape changes based on bladder and rectal volume.
  • To predict short-term bladder shape variations using cone beam CT (CBCT) data.
  • To improve accuracy in radiation therapy by accounting for organ motion.

Main Methods:

  • Utilized daily CBCT scans from 19 patients, with automated bladder and rectal wall delineation.

Related Experiment Videos

  • Developed a 3D model by mapping bladder wall distances from a reference point, creating a 2D scalar map.
  • Fitted bladder wall domain distances as a linear function of bladder and rectal volume.

    • Main Results:

    • The model predicts bladder shape changes with an average error of 0.2 cm, up to 0.5 cm.
    • A 150 cc bladder volume increase can displace the cranial bladder up to 2.5 cm.
    • Rectal filling shifts the bladder's position with minimal shape influence.

    Conclusions:

    • A novel model accurately describes bladder shape and position as a function of bladder and rectal volume.
    • The model reduces uncertainty in bladder wall position to a maximum of 0.5 cm with known volume changes.
    • This predictive model enhances precision in radiation oncology by accounting for organ dynamics.