The SRT reconstruction algorithm for semiquantification in PET imaging. Issue 10 (22nd September 2015)

• Record Type: Journal Article
• Authors: Kastis, George A.; Gaitanis, Anastasios; Samartzis, Alexandros P.; Fokas, Athanasios S.
• Is Part Of: Medical physics. Volume 42:Issue 10(2015)
• Page Start: 5970
• Page End: 5982
• Publication Date: 2015-09-22
• Subjects: computerised tomography -- expectation‐maximisation algorithm -- Gaussian processes -- image reconstruction -- inverse transforms -- medical image processing -- phantoms -- positron emission tomography -- Radon transforms -- splines (mathematics); Positron emission tomography (PET) -- Computed tomography -- Reconstruction; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Scintigraphy -- Measuring half‐life of a radioactive substance; image reconstruction‐analytical methods -- spline reconstruction -- SRT -- PET -- ordered‐subsets expectation–maximization -- OSEM -- quantification; Medical image reconstruction -- Medical image contrast -- Computed tomography -- Image reconstruction -- Positron emission tomography -- Medical image noise -- Image sensors -- Germanium -- Image scanners; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4931409
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: The spline reconstruction technique (SRT) is a new, fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The mathematical details of this algorithm and comparisons with filtered backprojection were presented earlier in the literature. In this study, the authors present a comparison between SRT and the ordered‐subsets expectation–maximization (OSEM) algorithm for determining contrast and semiquantitative indices of 18 F‐FDG uptake. Methods: The authors implemented SRT in the software for tomographic image reconstruction (stir ) open‐source platform and evaluated this technique using simulated and real sinograms obtained from the GE Discovery ST positron emission tomography/computer tomography scanner. All simulations and reconstructions were performed instir . For OSEM, the authors used the clinical protocol of their scanner, namely, 21 subsets and two iterations. The authors also examined images at one, four, six, and ten iterations. For the simulation studies, the authors analyzed an image‐quality phantom with cold and hot lesions. Two different versions of the phantom were employed at two different hot‐sphere lesion‐to‐background ratios (LBRs), namely, 2:1 and 4:1. For each noiseless sinogram, 20 Poisson realizations were created at five different noise levels. In addition to making visual comparisons of the reconstructed images, the authors determined contrast and bias as a function of the background image roughness (IR). For the real‐data studies, sinograms of an image‐quality phantom simulating the human torso were employed. The authors determined contrast and LBR as a function of the background IR. Finally, the authors present plots of contrast as a function of IR after smoothing each reconstructed image with Gaussian filters of six different sizes. Statistical significance was determined by employing the Wilcoxon rank‐sum test. Results: In both simulated and real studies, SRT exhibits higher contrast and lower bias than OSEM at the cold lesions. This improvement is achieved at the expense of increasing the noise in the reconstructed images. For the hot lesions, SRT exhibits a small improvement in contrast and LBR over OSEM with 21 subsets and two iterations; however, this improvement is not statistically significant. As the number of iterations increases, the performance of OSEM improves over SRT but again without statistical significance. The curves of contrast and LBR as a function of IR after Gaussian blurring indicate that the advantage of SRT in the cold regions is maintained even after decreasing the noise level by Gaussian blurring. Conclusions: SRT, at the expense of slightly increased noise in the reconstructed images, reconstructs images of higher contrast and lower bias than the clinical protocol of OSEM. This improvement is particularly evident for images involving cold regions. Thus, it appears that SRT should be particularly useful for the quantification of low‐count and cold regions.