Abstract
This paper presents a simulation framework for dynamic PET-MR. The main focus of this framework is to provide motion-resolved MR and PET data and ground truth motion information. This can be used in the optimisation and quantitative evaluation of image registration and in assessing the error propagation due to inaccuracies in motion estimation in complex motion-compensated reconstruction algorithms. Contrast and tracer kinetics can also be simulated and are available as ground truth information. To closely emulate medical examination, input and output of the simulation are files in standardised open-source raw data formats. This enables the use of existing raw data as a template input and ensures seamless integration of the output into existing reconstruction pipelines.The proposed framework was validated in PET-MR and image registration applications. It was used to simulate a FDG-PET-MR scan with cardiac and respiratory motion. Ground truth motion information could be utilised to optimise parameters for PET and synergistic PET-MR image registration. In addition, a free-breathing dynamic contrast enhancement (DCE) abdominal scan of a patient with hepatic lesions was simulated. In order to correct for breathing motion, a motion-corrected image reconstruction scheme was used and a Toft’s model was fit to the DCE data to obtain quantitative DCE-MRI parameters. Utilising the ground truth motion information, the dependency of quantitative DCE-MR images on the accuracy of the motion estimation was evaluated. We demonstrated that respiratory motion had to be available with an average accuracy of at least the spatial resolution of the DCE-MR images in order to ensure an improvement in lesions visualisation and quantification compared to no motion correction.The proposed framework provides a valuable tool with a wide range of scientific PET and MR applications and will be available as part of the open-source project Synergistic Image Reconstruction Framework (SIRF).
Highlights
Positron–emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used medical imaging techniques
Simultaneous PET-MR offers a range of possibilities to obtain physiological motion information and utilise it in order to minimise motion artefacts
The performance of the framework is demonstrated for three different MR and PET-MR applications: (a) optimisation of image registration parameters for cardiac and respiratory motion estimation in PET and PET-MR, (b) comparison of 5D motion-corrected image reconstruction of PET-MR data and (c) assessment of the effect of inaccuracies in the motion estimation on quantitative biophysical parameters obtained from motion-corrected free–breathing abdominal dynamic contrast enhancement (DCE) MR
Summary
Positron–emission tomography (PET) and magnetic resonance imaging (MRI) are two commonly used medical imaging techniques. Several approaches use high-resolution anatomical MR images or dedicated MR sequences such as tagging for accurate motion correction of PET data (Holman et al 2016, Rank et al 2016, Munoz et al 2016, Cal-Gonzalez et al 2018, Kustner 2017, Munoz et al 2018, Robson et al 2018) Other techniques combine both MR and PET information for image registration and use this to improve both MR and PET image quality (Kolbitsch et al 2018). Evaluation of motion correction schemes is often limited to assessing the improvement in image quality between uncorrected and motion–corrected images This gives an indication of the motion–correction scheme’s functionality, it does not necessarily provide information on how accurate it is
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