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

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

doi:10.1016/j.ymeth.2020.01.005

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 390ps. 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.

Highlights

Positron emission tomography (PET) is an important tomographic imaging modality with particular deployment in cardiology [1], neurology [2] and oncology
The comparisons are demonstrated for images reconstructed with ordered subset expectation maximisation (OSEM), point spread function (PSF)-OSEM, TOF-OSEM and PSF-TOF-OSEM algorithms with vendor’s reconstruction toolbox and Software for Tomographic Image Reconstruction (STIR) over 28 subsets and 3 subsequent iterations
The TOF emission projection data extracted with STIR for all phantom and clinical datasets in the described implementations is identical to the acquired emission data

Summary

Introduction

Positron emission tomography (PET) is an important tomographic imaging modality with particular deployment in cardiology [1], neurology [2] and oncology. The successful utility of PET in these branches of medicine can be seen by its increasing use for various clinical indications. PET scans are non-invasive and helpful in early diagnosis and management of conditions such as in Parkinson’s disease [3], dementia [4,5], coronary artery disease [6] and oncology [7] by quantitatively assessing metabolic and functional alterations [8]. PET scans are clinically useful in monitoring the progression of diseases and the patient’s response to therapy [9].

Objectives

Methods

Discussion

Conclusion