Comparative evaluation of image reconstruction methods for the Siemens PET-MR scanner using the STIR library

Daniel Deidda, Nikos Efthimiou, Richard Manber, Kris Thielemans, Pawel Markiewicz, Robert G. Aykroyd, Charalampos Tsoumpas

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

Abstract

With the introduction of Positron Emission Tomography - Magnetic Resonance (PET-MR) scanners the development of new algorithms and the comparison of the performance of different iterative reconstruction algorithms and the characteristics of the reconstructed images data is relevant. In this work, we perform a quantitative assessment of the currently used ordered subset (OS) algorithms for low-counts PET-MR data taken from a Siemens Biograph mMR scanner using the Software for Tomographic Image Reconstruction (STIR, stir.sf.net). A comparison has been performed in terms of bias and coefficient of variation (CoV). Within the STIR library different algorithms are available, such as Order Subsets Expectation Maximization (OSEM), OS Maximum A Posteriori One Step Late (OSMA-POSL) with Quadratic Prior (QP) and with Median Root Prior (MRP), OS Separable Paraboloidal Surrogate (OSSPS) with QP and Filtered Back-Projection (FBP). In addition, List Mode (LM) reconstruction is available. Corrections for attenuation, scatter and random events are performed using STIR instead of using the scanner. Data from the Hoffman brain phantom are acquired, processed and reconstructed. Clinical data from the thorax of a patient have also been reconstructed with the same algorithms. The number of subsets does not appreciably affect the bias nor the coefficient of variation (CoV=11%) at a fixed sub-iteration number. The percentage relative bias and CoV maximum values for OSMAPOSL-MRP are 10% and 15% at 360 s acquisition and 12% and 15% for the 36 s, whilst for OSMAPOSL-QP they are 6% and 16% for 360 s acquisition and 11% and 23% at 36 s and for OSEM 6% and 11% for the 360 s acquisition and 10% and 15% for the 36 s. Our findings demonstrate that when it comes to low-counts, noise and bias become significant. The methodology for reconstructing Siemens mMR data with STIR is included in the CCP-PET-MR website (www.ccppetmr.ac.uk).

Original languageEnglish
Title of host publication2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509016426
DOIs
Publication statusPublished - 16-Oct-2017
Externally publishedYes
Event2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016 - Strasbourg, France
Duration: 29-Oct-20166-Nov-2016

Publication series

Name2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
Volume2017-January

Conference

Conference2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016
Country/TerritoryFrance
CityStrasbourg
Period29/10/201606/11/2016

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