Deidda, D;
Efthimiou, N;
Manber, R;
Thielemans, K;
Markiewicz, P;
Aykroyd, RG;
Tsoumpas, C;
(2016)
Comparative Evaluation of Image Reconstruction Methods for the Siemens PET-MR Scanner Using the STIR Library.
In:
(Proceedings) IEEE Nuclear Science Symposium / Medical Imaging Conference / Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD).
IEEE
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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 (OSMAPOSL) 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.
| Type: | Proceedings paper |
|---|---|
| Title: | Comparative Evaluation of Image Reconstruction Methods for the Siemens PET-MR Scanner Using the STIR Library |
| Event: | IEEE Nuclear Science Symposium / Medical Imaging Conference / Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD) |
| Location: | Strasbourg, FRANCE |
| Dates: | 29 October 2016 - 06 November 2016 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/NSSMIC.2016.8069615 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Physical Sciences, Life Sciences & Biomedicine, Engineering, Electrical & Electronic, Nuclear Science & Technology, Physics, Applied, Radiology, Nuclear Medicine & Medical Imaging, Engineering, Physics, POSITRON-EMISSION-TOMOGRAPHY, ATTENUATION CORRECTION, MOTION CORRECTION, ORDERED SUBSETS, STATISTICS, ALGORITHMS, OSEM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10052085 |
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