Synthesis of Realistic Simultaneous Positron Emission Tomography and Magnetic Resonance Imaging Data

The investigation of the performance of different positron emission tomography (PET) reconstruction and motion compensation methods requires accurate and realistic representation of the anatomy and motion trajectories as observed in real subjects during acquisitions. The generation of well-controlled clinical datasets is difficult due to the many different clinical protocols, scanner specifications, patient sizes, and physiological variations. Alternatively, computational phantoms can be used to generate large data sets for different disease states, providing a ground truth. Several studies use registration of dynamic images to derive voxel deformations to create moving computational phantoms. These phantoms together with simulation software generate raw data. This paper proposes a method for the synthesis of dynamic PET data using a fast analytic method. This is achieved by incorporating realistic models of respiratory motion into a numerical phantom to generate datasets with continuous and variable motion with magnetic resonance imaging (MRI)-derived motion modeling and high resolution MRI images. In this paper, data sets for two different clinical traces are presented, ¹⁸F-FDG and ⁶⁸Ga-PSMA. This approach incorporates realistic models of respiratory motion to generate temporally and spatially correlated MRI and PET data sets, as those expected to be obtained from simultaneous PET-MRI acquisitions

Data-driven methods for respiratory signal detection in positron emission tomography

Positron Emission Tomography (PET) is a nuclear medicine imaging technique which allows quantitative assessment of functional processes, by determining the distribution of radioactive tracers inside the patient body. It is mainly used in oncology. Respiration during PET data acquisition of the chest leads to blurring and other artefacts in the images, lowering their quantitative accuracy. If a respiratory signal is available, these issues can be overcome by splitting the data into different motion states. In current clinical practice this signal is obtained using external devices. However, these are expensive, require prior setup and can cause patient discomfort. This thesis develops and evaluates Data-Driven (DD) techniques based on Principal Component Analysis (PCA) to generate the signal directly from the PET data. Firstly, the arbitrary relation between the sign of the PCA signal and the respiratory motion is addressed: a maximum in the signal could refer either to end-inspiration or end-expiration, possibly causing inaccurate motion correction. A new correction method is proposed and compared with two already existing methods. Subsequently, the methods are extended to Time-of-Flight (TOF) PET data, proposing a data processing step prior to using PCA, in order to benefit from the increased spatial information provided by TOF. The proposed methods are then extensively tested on lower lung patient data (non-TOF and TOF). The obtained respiratory signal is compared with that of an external device and with internal motion observed with Magnetic Resonance Imaging (MRI). Lastly, to investigate the performance of PCA where respiratory motion is minimal, the methods are applied to patient and simulation data of the upper lung, showing that they could potentially be utilised for detecting respiratory-induced density variations in the upper lung. This study shows that the presented methods could replace external devices for obtaining a respiratory signal, providing a simple and cost-effective tool for motion management in PET