sMLACF: a generalized expectation-maximization algorithm for TOF-PET to reconstruct the activity and attenuation simultaneously

Lutetium background radiation in total-body PET-A simulation study on opportunities and challenges in PET attenuation correction

The current generation of total-body positron emission tomography (PET) scanners offer significant sensitivity increase with an extended axial imaging extent. With the large volume of lutetium-based scintillation crystals that are used as detector elements in these scanners, there is an increased flux of background radiation originating from ¹⁷⁶Lu decay in the crystals and higher sensitivity for detecting it. Combined with the ability of scanning the entire body in a single bed position, this allows more effective utilization of the lutetium background as a transmission source for estimating 511 keV attenuation coefficients. In this study, utilization of the lutetium background radiation for attenuation correction in total-body PET was studied using Monte Carlo simulations of a 3D whole-body XCAT phantom in the uEXPLORER PET scanner, with particular focus on ultralow-dose PET scans that are now made possible with these scanners. Effects of an increased acceptance angle, reduced scan durations, and Compton scattering on PET quantification were studied. Furthermore, quantification accuracy of lutetium-based attenuation correction was compared for a 20-min scan of the whole body on the uEXPLORER, a one-meter-long, and a conventional 24-cm-long scanner. Quantification and lesion contrast were minimally affected in both long axial field-of-view scanners and in a whole-body 20-min scan, the mean bias in all analyzed organs of interest were within a ±10% range compared to ground-truth activity maps. Quantification was affected in certain organs, when scan duration was reduced to 5 min or a reduced acceptance angle of 17° was used. Analysis of the Compton scattered events suggests that implementing a scatter correction method for the transmission data will be required, and increasing the energy threshold from 250 keV to 290 keV can reduce the computational costs and data rates, with negligible effects on PET quantification. Finally, the current results can serve as groundwork for transferring lutetium-based attenuation correction into research and clinical practice.

A CT-less approach to quantitative PET imaging using the LSO intrinsic radiation for long-axial FOV PET scanners

Purpose

Long‐axial field‐of‐view (FOV) positron emission tomography (PET) scanners have gained a lot of interest in the recent years. Such scanners provide increased sensitivity and enable unique imaging opportunities that were not previously feasible. Benefiting from the high sensitivity of a long‐axial FOV PET scanner, we studied a computed tomography (CT)–less reconstruction algorithm for the Siemens Biograph Vision Quadra with an axial FOV of 106 cm.

Methods

In this work, the background radiation from radioisotope lutetium‐176 in the scintillators was used to create an initial estimate of the attenuation maps. Then, joint activity and attenuation reconstruction algorithms were used to create an improved attenuation map of the object. The final attenuation maps were then used to reconstruct quantitative PET images, which were compared against CT‐based PET images. The proposed method was evaluated on data from three patients who underwent a flurodeoxyglucouse PET scan.

Results

Segmentation of the PET images of the three studied patients showed an average quantitative error of 6.5%–8.3% across all studied organs when using attenuation maps from maximum likelihood estimation of attenuation and activity and 5.3%–6.6% when using attenuation maps from maximum likelihood estimation of activity and attenuation correction coefficients.

Conclusions

Benefiting from the background radiation of lutetium‐based scintillators, a quantitative CT‐less PET imaging technique was evaluated in this work.

Algorithms for joint activity-attenuation estimation from positron emission tomography scatter

Background

Attenuation correction in positron emission tomography remains challenging in the absence of measured transmission data. Scattered emission data may contribute missing information, but quantitative scatter-to-attenuation (S2A) reconstruction needs to input the reconstructed activity image. Here, we study S2A reconstruction as a building block for joint estimation of activity and attenuation.

Methods

We study two S2A reconstruction algorithms, maximum-likelihood expectation maximization (MLEM) with one-step-late attenuation (MLEM-OSL) and a maximum-likelihood gradient ascent (MLGA). We study theoretical properties of these algorithms with a focus on convergence and convergence speed and compare convergence speeds and the impact of object size in simulations using different spatial scale factors. Then, we propose joint estimation of activity and attenuation from scattered and nonscattered (true) emission data, combining MLEM-OSL or MLGA with scatter-MLEM as well as trues-MLEM and the maximum-likelihood transmission (MLTR) algorithm.

Results

Shortcomings of MLEM-OSL inhibit convergence to the true solution with high attenuation; these shortcomings are related to the linearization of a nonlinear measurement equation and can be linked to a new numerical criterion allowing geometrical interpretations in terms of low and high attenuation. Comparisons using simulated data confirm that while MLGA converges largely independent of the attenuation scale, MLEM-OSL converges if low-attenuation data dominate, but not with high attenuation. Convergence of MLEM-OSL can be improved by isolating data satisfying the aforementioned low-attenuation criterion. In joint estimation of activity and attenuation, scattered data helps avoid local minima that nonscattered data alone cannot. Combining MLEM-OSL with trues-MLEM may be sufficient for low-attenuation objects, while MLGA, scatter-MLEM, and MLTR may additionally be needed with higher attenuation.

Conclusions

The performance of S2A algorithms depends on spatial scales. MLGA provides lower computational complexity and convergence in more diverse setups than MLEM-OSL. Finally, scattered data may provide additional information to joint estimation of activity and attenuation through S2A reconstruction.

Joint Reconstruction of Activity and Attenuation in Time-of-Flight PET: A Quantitative Analysis

Methods for joint activity reconstruction and attenuation reconstruction of time-of-flight (TOF) PET data provide an effective solution to attenuation correction when no (or incomplete or inaccurate) information on attenuation is available. One of the main barriers limiting use of these methods in clinical practice is their lack of validation in a relatively large patient database. In this contribution, we aim to validate reconstruction performed with maximum-likelihood activity reconstruction and attenuation registration (MLRR) in a whole-body patient dataset. Furthermore, a partial validation (because the scale problem of the algorithm is avoided for now) of reconstruction performed with maximum-likelihood activity and attenuation (MLAA) is also provided. We present a quantitative comparison between these 2 methods of joint reconstruction and the current clinical gold standard, maximum-likelihood expectation maximization (MLEM) with CT-based attenuation correction. Methods: The whole-body TOF PET emission data of each patient dataset were processed as a whole to reconstruct an activity volume covering all the acquired bed positions, helping reduce the problem of a scale per bed position in MLAA to a global scale for the entire activity volume. Three reconstruction algorithms were used: MLEM, MLRR, and MLAA. A maximum-likelihood scaling of the single-scatter simulation estimate to the emission data was used for scatter correction. The reconstruction results for various regions of interest were then analyzed. Results: The joint reconstructions of the whole-body patient dataset provided better quantification than the gold standard in cases of PET and CT misalignment caused by patient or organ motion. Our quantitative analysis showed a difference of -4.2% ± 2.3% between MLRR and MLEM and a difference of -7.5% ± 4.6% between MLAA and MLEM, averaged over all regions of interest. Conclusion: Joint reconstruction of activity and attenuation provides a useful means to estimate tracer distribution when CT-based-attenuation images are subject to misalignment or are not available. With an accurate estimate of the scatter contribution in the emission measurements, the joint reconstructions of TOF PET data are within clinically acceptable accuracy.

Joint estimation of activity and attenuation for PET using pragmatic MR-based prior: application to clinical TOF PET/MR whole-body data for FDG and non-FDG tracers

Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as 68Ga-DOTATOC and 18F-Fluoride.