PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library

Palak Wadhwa; Kris Thielemans; Nikos Efthimiou; Kristen Wangerin; Nicholas Keat; Elise Emond; Timothy Deller; Ottavia Bertolli; Daniel Deidda; Gaspar Delso; Michel Tohme; Floris Jansen; Roger N. Gunn; William Hallett; Charalampos Tsoumpas

doi:10.1016/j.ymeth.2020.01.005

PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library

Palak Wadhwa, Kris Thielemans, Nikos Efthimiou, Kristen Wangerin, Nicholas Keat, Elise Emond, Timothy Deller, Ottavia Bertolli, Daniel Deidda, Gaspar Delso, Michel Tohme, Floris Jansen, Roger N. Gunn, William Hallett, Charalampos Tsoumpas

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

This work demonstrates how computational and physical modelling of the positron emission tomography (PET) image acquisition process for a state-of-the-art integrated PET and magnetic resonance imaging (PET-MR) system can produce images comparable to the manufacturer. The GE SIGNA PET/MR scanner is manufactured by General Electric and has time-of-flight (TOF) capabilities of about 390 ps. All software development took place in the Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) library, which is a widely used open source software to reconstruct data as exported from emission tomography scanners. The new software developments will be integrated into STIR, providing the opportunity for researchers worldwide to establish and expand their image reconstruction methods. Furthermore, this work is of particular significance as it provides the first validation of TOF PET image reconstruction for real scanner datasets using the STIR library. This paper presents the methodology, analysis, and critical issues encountered in implementing an independent reconstruction software package. Acquired PET data were processed via several appropriate algorithms which are necessary to produce an accurate and precise quantitative image. This included mathematical, physical and anatomical modelling of the patient and simulation of various aspects of the acquisition. These included modelling of random coincidences using ‘singles’ rates per crystals, detector efficiencies and geometric effects. Attenuation effects were calculated by using the STIR's attenuation correction model. Modelling all these effects within the system matrix allowed the reconstruction of PET images which demonstrates the metabolic uptake of the administered radiopharmaceutical. These implementations were validated using measured phantom and clinical datasets. The developments are tested using the ordered subset expectation maximisation (OSEM) and the more recently proposed kernelised expectation maximisation (KEM) algorithm which incorporates anatomical information from MR images into PET reconstruction.

Original language	English
Pages (from-to)	110-119
Number of pages	10
Journal	Methods
Volume	185
DOIs	https://doi.org/10.1016/j.ymeth.2020.01.005
State	Published - Jan 2021
Externally published	Yes

Funding

P. Wadhwa is funded by a Medical Research Council Industrial CASE PhD Scholarship (MR/M01746X/1). Dr. Tsoumpas is sponsored by a Royal Society Industry Fellowship (IF170011). The project has been partially supported by the EPSRC Collaborative Computational Project (EP/M022587/1) and its flagship research grant (EP/P022200/1). We would like to thank Prof. Stefaan Vandenberghe, Prof. Michel Koole and Ms. Ester D'Hoe for their initial support and data for this work. We would also like to thank Prof. David Buckley for his substantial support for the entire course of this work. Ethics number 17/WM/0084 with permission from a clinical study performed at Invicro. P. Wadhwa is funded by a Medical Research Council Industrial CASE PhD Scholarship (MR/M01746X/1). Dr. Tsoumpas is sponsored by a Royal Society Industry Fellowship (IF170011). The project has been partially supported by the EPSRC Collaborative Computational Project (EP/M022587/1) and its flagship research grant (EP/P022200/1). We would like to thank Prof. Stefaan Vandenberghe, Prof. Michel Koole and Ms. Ester D’Hoe for their initial support and data for this work. We would also like to thank Prof. David Buckley for his substantial support for the entire course of this work. Ethics number 17/WM/0084 with permission from a clinical study performed at Invicro.

Funders	Funder number
Engineering and Physical Sciences Research Council	EP/P022200/1, 17/WM/0084, EP/M022587/1
Royal Society	IF170011

Keywords

Image reconstruction
PET/MR
Positron emission tomography
STIR
Tomography

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10.1016/j.ymeth.2020.01.005

Cite this

Wadhwa, P., Thielemans, K., Efthimiou, N., Wangerin, K., Keat, N., Emond, E., Deller, T., Bertolli, O., Deidda, D., Delso, G., Tohme, M., Jansen, F., Gunn, R. N., Hallett, W., & Tsoumpas, C. (2021). PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library. Methods, 185, 110-119. https://doi.org/10.1016/j.ymeth.2020.01.005

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title = "PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library",

abstract = "This work demonstrates how computational and physical modelling of the positron emission tomography (PET) image acquisition process for a state-of-the-art integrated PET and magnetic resonance imaging (PET-MR) system can produce images comparable to the manufacturer. The GE SIGNA PET/MR scanner is manufactured by General Electric and has time-of-flight (TOF) capabilities of about 390 ps. All software development took place in the Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) library, which is a widely used open source software to reconstruct data as exported from emission tomography scanners. The new software developments will be integrated into STIR, providing the opportunity for researchers worldwide to establish and expand their image reconstruction methods. Furthermore, this work is of particular significance as it provides the first validation of TOF PET image reconstruction for real scanner datasets using the STIR library. This paper presents the methodology, analysis, and critical issues encountered in implementing an independent reconstruction software package. Acquired PET data were processed via several appropriate algorithms which are necessary to produce an accurate and precise quantitative image. This included mathematical, physical and anatomical modelling of the patient and simulation of various aspects of the acquisition. These included modelling of random coincidences using {\textquoteleft}singles{\textquoteright} rates per crystals, detector efficiencies and geometric effects. Attenuation effects were calculated by using the STIR's attenuation correction model. Modelling all these effects within the system matrix allowed the reconstruction of PET images which demonstrates the metabolic uptake of the administered radiopharmaceutical. These implementations were validated using measured phantom and clinical datasets. The developments are tested using the ordered subset expectation maximisation (OSEM) and the more recently proposed kernelised expectation maximisation (KEM) algorithm which incorporates anatomical information from MR images into PET reconstruction.",

keywords = "Image reconstruction, PET/MR, Positron emission tomography, STIR, Tomography",

author = "Palak Wadhwa and Kris Thielemans and Nikos Efthimiou and Kristen Wangerin and Nicholas Keat and Elise Emond and Timothy Deller and Ottavia Bertolli and Daniel Deidda and Gaspar Delso and Michel Tohme and Floris Jansen and Gunn, {Roger N.} and William Hallett and Charalampos Tsoumpas",

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Wadhwa, P, Thielemans, K, Efthimiou, N, Wangerin, K, Keat, N, Emond, E, Deller, T, Bertolli, O, Deidda, D, Delso, G, Tohme, M, Jansen, F, Gunn, RN, Hallett, W & Tsoumpas, C 2021, 'PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library', Methods, vol. 185, pp. 110-119. https://doi.org/10.1016/j.ymeth.2020.01.005

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AU - Wadhwa, Palak

AU - Thielemans, Kris

AU - Efthimiou, Nikos

AU - Wangerin, Kristen

AU - Keat, Nicholas

AU - Emond, Elise

AU - Deller, Timothy

AU - Bertolli, Ottavia

AU - Deidda, Daniel

AU - Delso, Gaspar

AU - Tohme, Michel

AU - Jansen, Floris

AU - Gunn, Roger N.

AU - Hallett, William

AU - Tsoumpas, Charalampos

PY - 2021/1

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N2 - This work demonstrates how computational and physical modelling of the positron emission tomography (PET) image acquisition process for a state-of-the-art integrated PET and magnetic resonance imaging (PET-MR) system can produce images comparable to the manufacturer. The GE SIGNA PET/MR scanner is manufactured by General Electric and has time-of-flight (TOF) capabilities of about 390 ps. All software development took place in the Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) library, which is a widely used open source software to reconstruct data as exported from emission tomography scanners. The new software developments will be integrated into STIR, providing the opportunity for researchers worldwide to establish and expand their image reconstruction methods. Furthermore, this work is of particular significance as it provides the first validation of TOF PET image reconstruction for real scanner datasets using the STIR library. This paper presents the methodology, analysis, and critical issues encountered in implementing an independent reconstruction software package. Acquired PET data were processed via several appropriate algorithms which are necessary to produce an accurate and precise quantitative image. This included mathematical, physical and anatomical modelling of the patient and simulation of various aspects of the acquisition. These included modelling of random coincidences using ‘singles’ rates per crystals, detector efficiencies and geometric effects. Attenuation effects were calculated by using the STIR's attenuation correction model. Modelling all these effects within the system matrix allowed the reconstruction of PET images which demonstrates the metabolic uptake of the administered radiopharmaceutical. These implementations were validated using measured phantom and clinical datasets. The developments are tested using the ordered subset expectation maximisation (OSEM) and the more recently proposed kernelised expectation maximisation (KEM) algorithm which incorporates anatomical information from MR images into PET reconstruction.

AB - This work demonstrates how computational and physical modelling of the positron emission tomography (PET) image acquisition process for a state-of-the-art integrated PET and magnetic resonance imaging (PET-MR) system can produce images comparable to the manufacturer. The GE SIGNA PET/MR scanner is manufactured by General Electric and has time-of-flight (TOF) capabilities of about 390 ps. All software development took place in the Software for Tomographic Image Reconstruction (STIR: http://stir.sf.net) library, which is a widely used open source software to reconstruct data as exported from emission tomography scanners. The new software developments will be integrated into STIR, providing the opportunity for researchers worldwide to establish and expand their image reconstruction methods. Furthermore, this work is of particular significance as it provides the first validation of TOF PET image reconstruction for real scanner datasets using the STIR library. This paper presents the methodology, analysis, and critical issues encountered in implementing an independent reconstruction software package. Acquired PET data were processed via several appropriate algorithms which are necessary to produce an accurate and precise quantitative image. This included mathematical, physical and anatomical modelling of the patient and simulation of various aspects of the acquisition. These included modelling of random coincidences using ‘singles’ rates per crystals, detector efficiencies and geometric effects. Attenuation effects were calculated by using the STIR's attenuation correction model. Modelling all these effects within the system matrix allowed the reconstruction of PET images which demonstrates the metabolic uptake of the administered radiopharmaceutical. These implementations were validated using measured phantom and clinical datasets. The developments are tested using the ordered subset expectation maximisation (OSEM) and the more recently proposed kernelised expectation maximisation (KEM) algorithm which incorporates anatomical information from MR images into PET reconstruction.

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KW - PET/MR

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KW - Tomography

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PET image reconstruction using physical and mathematical modelling for time of flight PET-MR scanners in the STIR library

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Funding

Keywords

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