Respiratory motion correction is beneficial in positron emission tomography. Different strategies for handling attenuation correction in conjunction with motion correction exist. In clinical practice, usually a single attenuation map is available, derived from computed tomography in one respiratory state. This can introduce an unwanted bias (through misaligned anatomy) into the motion correction algorithm. This paper builds upon previous work which suggested that non-attenuation corrected data was suitable for motion estimation, through the use of motion models, if time-of-flight data are available. Here, the previous work is expanded upon by incorporating attenuation correction in an iterative process. Non-attenuation corrected volumes are reconstructed using ordered subset expectation maximisation and used as input for motion model estimation. A single attenuation map is then warped to the volumes, using the motion model, the volumes are attenuation corrected, after which another motion estimation and correction cycle is performed. For validation, 4-Dimensional Extended Cardiac Torso simulations are used, for one bed position, with a field of view including the base of the lungs and the diaphragm. The output from the proposed method is evaluated against a non-motion corrected reconstruction of the same data visually, using a profile as well as standardised uptake value analysis. Results indicate that motion correction of inter-respiratory cycle motion is possible with this method, while accounting for attenuation deformation.

Respiratory motion reduces image quality in Positron Emission Tomography (PET). Unless gated Computed Tomography (CT) or Magnetic Resonance (MR) data are available, motion correction relies on registration of the PET data. To avoid mis-registration due to attenuation mismatches, most existing methods rely on pair-wise registration of Non-Attenuation Corrected (NAC) PET volumes. This is a challenging problem due to the low contrast and high noise of these volumes. This paper investigates the possibility of using motion models for respiratory motion correction in PET, and in particular whether incorporating Time-of-Flight (TOF) information increases the accuracy of the motion models derived from the NAC reconstructed images. 4D Extended Cardiac-Torso (XCAT) phantom simulations are used for one bed position with a field of view including the base of the lungs and the diaphragm. A TOF resolution of 375ps is used. NAC images are reconstructed using Orded SubSet Expectation Maximisation (OSEM) and used as input for motion model estimation. Different motion models are compared using the original XCAT input volumes. The results indicate that TOF improves the accuracy of the motion model considerably.

Emission-based attenuation correction (AC) methods offer the possibility of overcoming quantification errors induced by conventional MR-based approaches in PET/MR imaging. However, the joint problem of determining AC and the activity of interest is strongly ill-posed in non-TOF PET. This can be improved by exploiting the extra information arising from low energy window photons, but the feasibility of this approach has only been studied with relatively simplistic analytic simulations so far. This manuscript aims to address some of the remaining challenges needed to handle realistic measurements; in particular, the detection efficiency ("normalisation") estimation for each energy window is investigated. An energy-dependent detection efficiency model is proposed, accounting for the presence of unscattered events in the lower energy window due to detector scatter. Geometric calibration factors are estimated prior to the reconstruction for both scattered and unscattered events. Different reconstruction methods are also compared. Results show that geometric factors differ markedly between the energy windows and that our analytical model correspond in good approximation to Monte Carlo simulation; the multiple energy window reconstruction appears sensitive to input/model mismatch. Our method applies to Monte Carlo generated data but can be extended to measured data. This study is restricted to single scatter events.

Involuntary patient motion can happen in dynamic whole body (DWB) PET due to long scanning times, which may cause inaccurate quantification of tissue parameters. To quantify the impact on Patlak parameters, we simulated dynamic data using patient-derived motion fields, systematically introducing the motion at different passes of the dynamic scan, both inter and intra-frame. Estimated parameters are compared against the ground truth. Results show that errors can be large, even for small motion. Caution is advised when quantitatively evaluating DWB-PET images, if any motion has been detected.

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