Analysis of Cone-Beam CT using prior information
Moores, Matthew T., Hargrave, Catriona Elizabeth, Harden, Fiona, & Mengersen, Kerrie (2011) Analysis of Cone-Beam CT using prior information. In SSAI Young Statisticians’ Conference, July 14-15, 2011, UQ St Lucia, Queensland, Australia. (Unpublished)
Treatment plans for conformal radiotherapy are based on an initial CT scan. The aim is to deliver the prescribed dose to the tumour, while minimising exposure to nearby organs. Recent advances make it possible to also obtain a Cone-Beam CT (CBCT) scan, once the patient has been positioned for treatment. A statistical model will be developed to compare these CBCT scans with the initial CT scan. Changes in the size, shape and position of the tumour and organs will be detected and quantified.
Some progress has already been made in segmentation of prostate CBCT scans ,,. However, none of the existing approaches have taken full advantage of the prior information that is available. The planning CT scan is expertly annotated with contours of the tumour and nearby sensitive objects. This data is specific to the individual patient and can be viewed as a snapshot of spatial information at a point in time. There is an abundance of studies in the radiotherapy literature that describe the amount of variation in the relevant organs between treatments. The findings from these studies can form a basis for estimating the degree of uncertainty. All of this information can be incorporated as an informative prior into a Bayesian statistical model.
This model will be developed using scans of CT phantoms, which are objects with known geometry. Thus, the accuracy of the model can be evaluated objectively. This will also enable comparison between alternative models.
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|Item Type:||Conference Item (Poster)|
|Keywords:||Bayesian statistics, X-ray computed tomography, Image segmentation|
|Subjects:||Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Biostatistics (010402)|
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > ONCOLOGY AND CARCINOGENESIS (111200) > Radiation Therapy (111208)
|Divisions:||Current > Schools > School of Clinical Sciences|
Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||13 Nov 2012 08:21|
|Last Modified:||13 Nov 2012 10:40|
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