title: Evaluation of a method for projection-based tissue-activity estimation within small volumes of interest.
abstract: A new method of compensating for tissue-fraction and count-spillover effects, which require tissue segmentation only within a small volume surrounding the primary lesion of interest, was evaluated for SPECT imaging. Tissue-activity concentration estimates are obtained by fitting the measured projection data to a statistical model of the segmented tissue projections. Multiple realizations of two simulated human-torso phantoms, each containing 20 spherical 'tumours', 1.6 cm in diameter, with tumour-to-background ratios of 8:1 and 4:1, were simulated. Estimates of tumour- and background-activity concentration values for homogeneous as well as inhomogeneous tissue activities were compared to the standard uptake value (SUV) metrics on the basis of accuracy and precision. For perfectly registered, high-contrast, superficial lesions in a homogeneous background without scatter, the method yielded accurate (<0.4% bias) and precise (<6.1%) recovery of the simulated activity values, significantly outperforming the SUV metrics. Tissue inhomogeneities, greater tumour depth and lower contrast ratios degraded precision (up to 11.7%), but the estimates remained almost unbiased. The method was comparable in accuracy but more precise than a well-established matrix inversion approach, even when errors in tumour size and position were introduced to simulate moderate inaccuracies in segmentation and image registration. Photon scatter in the object did not significantly affect the accuracy or precision of the estimates.
authors.affiliation: Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, 75 Francis St., Boston, MA 02115, USA. southekal@bwh.harvard.edu
authors.forename: Sudeepti
authors.initials: S
authors.lastname: Southekal
authors.forename: Sarah J
authors.initials: SJ
authors.lastname: McQuaid
authors.forename: Marie Foley
authors.initials: MF
authors.lastname: Kijewski
authors.forename: Stephen C
authors.initials: SC
authors.lastname: Moore
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003287:Contrast Media / Q000494:pharmacology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D019047:Phantoms, Imaging; D017785:Photons; D016012:Poisson Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015899:Tomography, Emission-Computed, Single-Photon / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods
chemical_list: D003287:Contrast Media