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Emission-based attenuation correction (AC) meth-ods 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.

Positron emission tomography/computed tomography (PET/CT) lung imaging is highly sensitive to motion. Although several techniques exist to diminish motion artifacts, a few accounts for both tissue displacement and changes in density due to the compression and dilation of the lungs, which cause quantification errors. This article presents an experimental framework for joint activity image reconstruction and motion estimation in PET/CT, where the PET image and the motion are directly estimated from the raw data. Direct motion estimation methods for motion-compensated PET/CT are preferable as they require a single attenuation map only and result in optimal signal-to-noise ratio (SNR). Previous implementations, however, failed to address changes in density during respiration. We propose to account for such changes using the Jacobian determinant of the deformation fields. In a feasibility study, we demonstrate on a modified extended cardiac-torso (XCAT) phantom with breathing motion—where the lung density and activity vary—that our approach achieved better quantification in the lungs than conventional PET/CT joint activity image reconstruction and motion estimation that does not account for density changes. The proposed method resulted in lower bias and variance in the activity images, reduced mean relative activity error in the lung at the reference gate (−4.84% to −3.22%) and more realistic Jacobian determinant values.

Estimation of attenuation from positron emission tomography (PET) data only is of interest for hybrid PET-MR and systems where CT is not available or recommended. However, when using data from a single energy window, emission-based non-time-of-flight (TOF) PET attenuation correction (AC) methods suffer from “cross-talk” artifacts. Based on earlier work, this article explores the hypothesis that cross-talk can be reduced by using more than one energy window. We propose an algorithm for the simultaneous estimation of both activity and attenuation images, as well as, the scatter component of the measured data from a PET acquisition, using multiple energy windows. The model for the measurements is 3-D and accounts for the finite energy resolution of PET detectors; it is restricted to single scatter. The proposed energy-based simultaneous maximum likelihood reconstruction of activity and attenuation with photopeak scatter re-estimation algorithm is compared with simultaneous estimation from a single energy window simultaneous maximum likelihood reconstruction of activity and attenuation with photopeak scatter re-estimation. The evaluation is based on simulations using the characteristics of the Siemens mMR scanner. Phantoms of different complexity were investigated. In particular, a 3-D XCAT torso phantom was used to assess the inpainting of attenuation values within the lung region. Results show that the cross-talk present in non-TOF maximum likelihood reconstruction of activity and attenuation reconstructions is significantly reduced when using multiple energy windows and indicate that the proposed approach warrants further investigation.

This study investigates the use of a synergistic edge-preserving prior for a maximum-likelihood reconstruction of PET activity and attenuation images. The effect of the prior was tested on different reconstruction methods: MLAA and our algorithm MLAA-EB-S. Simple simulations on cylindrical phantoms with an insert showed that the presence of a synergistic prior enforces cross-talk related edges when the images are reconstructed with non-TOF MLAA and increases the bias in both the reconstructed activity and attenuation images. However, benefits appear when the joint problem is less ill-posed, as in the case of our method MLAA-EB-S, which exploits scatter information to improve conditioning. Overall, MLAA-EB-S with synergistic prior achieved the lowest bias and variance in both the activity and the attenuation images amongst the compared methods. Therefore, the proposed synergistic prior could be a useful way for improving the performance of MLAA-EB-S without the need of relying on anatomical images.

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single-valued population-based lung LAC, and better estimation is needed to improve quantification. Given the under-appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single-valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission-based schemes. Potential strategies for future developments are also presented.

This study explores the feasibility of incorporating energy information into a maximum-likelihood reconstruction of activity and attenuation (MLAA) framework. The attenuation and activity distributions were reconstructed from multiple energy window data, and a scatter function was added to the system model of the algorithm. The proposed energy-based method (MLAA-EB) was evaluated with simulated 3D phantom data, using the geometry and characteristics of a Siemens mMR PET-MR scanner. Results showed that the proposed algorithm is able to compensate for errors in the activity image caused by the incorrect assignment of attenuation values to the segmented MR. This is effective for small objects only, for large objects further solutions need to be found.

This study explores the advantages of using a different energy window for each individual annihilation photon when estimating attenuation from scattered PET data. Several studies have shown that scattered data can be used to reconstruct the density distribution of the object, although they are limited to simple phantoms and ideal energy measurements. We have incorporated a realistic photon detection probability model into our algorithm, as well as the dependence of attenuation on photon energy. In the proposed method each detector can assign a photon to either energy window. Preliminary results on point source simulations have shown that using a different window for each annihilation photon allows to derive spatial information on both attenuation and activity distribution, and to isolate subsets of scatter angles depending on the energy window pair used. XCAT simulations demonstrated that it is possible to yield an attenuation map from scattered PET data only and that MR information can increase reconstruction accuracy. This method can also compensate for errors typically introduced from the MRAC, such as truncation artifacts.