Comparison of the scanning linear estimator (SLE) and ROI methods for quantitative SPECT imaging. (6th August 2015)

• Record Type: Journal Article
• Authors: Könik, Arda; Kupinski, Meredith; Pretorius, P Hendrik; King, Michael A; Barrett, Harrison H
• Is Part Of: Physics in medicine & biology. Volume 60:Number 16(2015:Aug.)
• Page Start: 6479
• Page End: 6494
• Publication Date: 2015-08-06
• Subjects: quantitative SPECT -- scanning linear estimation (SLE) -- region of interest method (ROI) -- SPECT -- simulation; Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/0031-9155/60/16/6479
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: In quantitative emission tomography, tumor activity is typically estimated from calculations on a region of interest (ROI) identified in the reconstructed slices. In these calculations, unpredictable bias arising from the null functions of the imaging system affects ROI estimates. The magnitude of this bias depends upon the tumor size and location. In prior work it has been shown that the scanning linear estimator (SLE), which operates on the raw projection data, is an unbiased estimator of activity when the size and location of the tumor are known. In this work, we performed analytic simulation of SPECT imaging with a parallel-hole medium-energy collimator. Distance-dependent system spatial resolution and non-uniform attenuation were included in the imaging simulation. We compared the task of activity estimation by the ROI and SLE methods for a range of tumor sizes (diameter: 1–3 cm) and activities (contrast ratio: 1–10) added to uniform and non-uniform liver backgrounds. Using the correct value for the tumor shape and location is an idealized approximation to how task estimation would occur clinically. Thus we determined how perturbing this idealized prior knowledge impacted the performance of both techniques. To implement the SLE for the non-uniform background, we used a novel iterative algorithm for pre-whitening stationary noise within a compact region. Estimation task performance was compared using the ensemble mean-squared error (EMSE) as the criterion. The SLE method performed substantially better than the ROI method (i.e. EMSE(SLE) was 23–174 times lower) when the background is uniform and tumor location and size are known accurately. The variance of the SLE increased when a non-uniform liver texture was introduced but the EMSE(SLE) continued to be 5–20 times lower than the ROI method. In summary, SLE outperformed ROI under almost all conditions that we tested.