Modelling and simulation of dynamic contrast-enhanced MRI of abdominal tumours

  • Anita Banerji

Student thesis: Phd


Dynamic contrast-enhanced (DCE) time series analysis techniques are hard to fully validate quantitatively as ground truth microvascular parameters are difficult to obtain from patient data. This thesis presents a software application for generating synthetic image data from known ground truth tracer kinetic model parameters. As an object oriented design has been employed to maximise flexibility and extensibility, the application can be extended to include different vascular input functions, tracer kinetic models and imaging modalities. Data sets can be generated for different anatomical and motion descriptions as well as different ground truth parameters. The application has been used to generate a synthetic DCE-MRI time series of a liver tumour with non-linear motion of the abdominal organs due to breathing.The utility of the synthetic data has been demonstrated in several applications: in the development of an Akaike model selection technique for assessing the spatially varying characteristics of liver tumours; the robustness of model fitting and model selection to noise, partial volume effects and breathing motion in liver tumours; and the benefit of using model-driven registration to compensate for breathing motion.When applied to synthetic data with appropriate noise levels, the Akaike model selection technique can distinguish between the single-input extended Kety model for tumour and the dual-input Materne model for liver, and is robust to motion. A significant difference between median Akaike probability value in tumour and liver regions is also seen in 5/6 acquired data sets, with the extended Kety model selected for tumour. Knowledge of the ground truth distribution for the synthetic data was used to demonstrate that, whilst median Ktrans does not change significantly due to breathing motion, model-driven registration restored the structure of the Ktrans histogram and so could be beneficial to tumour heterogeneity assessments.
Date of Award31 Dec 2012
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorStephen Williams (Supervisor) & Geoff Parker (Supervisor)


  • liver metstases
  • synthetic data
  • model selection

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