title: Optimising delineation accuracy of tumours in PET for radiotherapy planning using blind deconvolution.
abstract: Positron emission tomography (PET) imaging has been proven to be useful in radiotherapy planning for the determination of the metabolically active regions of tumours. Delineation of tumours, however, is a difficult task in part due to high noise levels and the partial volume effects originating mainly from the low camera resolution. The goal of this work is to study the effect of blind deconvolution on tumour volume estimation accuracy for different computer-aided contouring methods. The blind deconvolution estimates the point spread function (PSF) of the imaging system in an iterative manner in a way that the likelihood of the given image being the convolution output is maximised. In this way, the PSF of the imaging system does not need to be known. Data were obtained from a NEMA NU-2 IQ-based phantom with a GE DSTE-16 PET/CT scanner. The artificial tumour diameters were 13, 17, 22, 28 and 37 mm with a target/background ratio of 4:1. The tumours were delineated before and after blind deconvolution. Student's two-tailed paired t-test showed a significant decrease in volume estimation error (p < 0.001) when blind deconvolution was used in conjunction with computer-aided delineation methods. A manual delineation confirmation demonstrated an improvement from 26 to 16 % for the artificial tumour of size 37 mm while an improvement from 57 to 15 % was noted for the small tumour of 13 mm. Therefore, it can be concluded that blind deconvolution of reconstructed PET images may be used to increase tumour delineation accuracy.
authors.affiliation: Institute of Biomedical Engineering, Bogazici University, Kandilli Kampus, Cengelkoy, Istanbul 34684, Turkey albert.guvenis@ieee.org.
authors.forename: A
authors.initials: A
authors.lastname: Guvenis
authors.affiliation: Institute of Biomedical Engineering, Bogazici University, Kandilli Kampus, Cengelkoy, Istanbul 34684, Turkey University of Kirklareli, Vocational College of Health Services, Kirklareli, Turkey.
authors.forename: A
authors.initials: A
authors.lastname: Koc
mesh_terms: D000465:Algorithms; D019788:Fluorodeoxyglucose F18; D006801:Humans; D021621:Imaging, Three-Dimensional; D009369:Neoplasms / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D019047:Phantoms, Imaging; D049268:Positron-Emission Tomography / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D019788:Fluorodeoxyglucose F18
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