Maximum Likelihood Reconstruction Of Attenuation And Activity Research Articles

Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission tomography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of facial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET.

Data-driven respiratory phase-matched PET attenuation correction without CT

We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-body 18F-fluorodeoxyglucose (18F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUV90% was increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.

Practical joint reconstruction of activity and attenuation with autonomous scaling for time-of-flight PET

Recent research has showed that attenuation images can be determined from emission data, jointly with activity images, up to a scaling constant when utilizing the time-of-flight (TOF) information. We aim to develop practical CT-less joint reconstruction for clinical TOF PET scanners to obtain quantitatively accurate activity and attenuation images. In this work, we present a joint reconstruction of activity and attenuation based on MLAA (maximum likelihood reconstruction of attenuation and activity) with autonomous scaling determination and joint TOF scatter estimation from TOF PET data. Our idea for scaling is to use a selected volume of interest (VOI) in a reconstructed attenuation image with known attenuation, e.g. a liver in patient imaging. First, we construct a unit attenuation medium which has a similar, though not necessarily the same, support to the imaged emission object. All detectable LORs intersecting the unit medium have an attenuation factor of e −1≈ 0.3679, i.e. the line integral of linear attenuation coefficients is one. The scaling factor can then be determined from the difference between the reconstructed attenuation image and the known attenuation within the selected VOI normalized by the unit attenuation medium. A four-step iterative joint reconstruction algorithm is developed. In each iteration, (1) first the activity is updated using TOF OSEM from TOF list-mode data; (2) then the attenuation image is updated using XMLTR—a extended MLTR from non-TOF LOR sinograms; (3) a scaling factor is determined based on the selected VOI and both activity and attenuation images are updated using the estimated scaling; and (4) scatter is estimated using TOF single scatter simulation with the jointly reconstructed activity and attenuation images. The performance of joint reconstruction is studied using simulated data from a generic whole-body clinical TOF PET scanner and a long axial FOV research PET scanner as well as 3D experimental data from the PennPET Explorer scanner. We show that the proposed joint reconstruction with proper autonomous scaling provides low bias results comparable to the reference reconstruction with known attenuation.

Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps.

We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18F-FDG PET/CT scan data of 100 cancer patients (38 men and 62 women; age, 57.3 ± 14.1 y) were retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution (λ-MLAA) and μ-map (μ-MLAA). We used 1.3 million patches derived from 60 patients' data for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method (μ-CNN), μ-MLAA, and 4-segment method (μ-segment) were compared with the μ-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the μ-maps, and the SUVs of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between μ-CNN and μ-CT was 0.77, which was significantly higher than that between μ-MLAA and μ-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences in activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511-keV photons than the 4-segment method currently used in whole-body PET/MRI studies.

Joint activity and attenuation estimation for PET with TOF data and single events

Maximum likelihood reconstruction of activity and attenuation (MLAA) for PET data with time-of-flight (TOF) information can determine the activity distribution up to a scale, and the attenuation map Radon transform up to a related constant. Prior knowledge is widely used for the determination of the constant. However, prior knowledge could be inaccurate due to patient variation and may result in quantitation errors. Our goal is to develop a method that can determine the scale and the related constant in the TOF-MLAA algorithm to obtain quantitatively accurate activity and attenuation maps.Our idea is to utilize the single events which have depth dependent attenuation factors, contrary to coincidence events. We show that in a 2D case, with the combination of TOF information and single events, a unique solution of attenuation and activity can be achieved. A three-step iterative image reconstruction algorithm is developed. In each iteration, the activity distribution is first updated using the MLEM approach with TOF PET data; the attenuation map is then updated using MLTR with non-TOF data; finally, both activity distribution and attenuation map are updated using a scale estimated from single events. Noisy and noise-free projection data are generated for 2D XCAT phantoms through analytical simulation. Both scatter information and randoms information are assumed to be known. The proposed method and the conventional TOF-MLAA algorithm are used to reconstruct the simulated data. Conventional MLEM method with known attenuation map is implemented for comparison as well. Normalized root mean square error (NRMSE) and optimal constant are defined for the quantitative analysis of reconstructed images. Our proposed image reconstruction method achieves ~1% NRMSE for the activity map and ~5% NRMSE for the attenuation map with a correct scale after 150 iterations for noise-free data. Results of noisy simulations are consistent with noise-free data.In summary, we have proposed to use the single events for constant determination in TOF-MLAA algorithm to obtain a unique solution of attenuation map and activity distribution without prior information. Future work will be dedicated to the extension of our method to the 3D situation.

MR contingency supplement prior for joint estimation of activity and attenuation in non‐time‐of‐flight positron emission tomography/MR

Maximum likelihood reconstruction of activity and attenuation (MLAA) from emission data only suffered from the inherent cross-talk between the estimated attenuation and activity distributions. The authors proposed an improved MLAA algorithm by utilising tissue prior atlas (TPA) and a Gibbs prior as prior knowledge. TPA determines the plausible region for each of the typical attenuation coefficients; hence, it imposes statistical condition as a supplement for the exclusive magnetic resonance (MR) information on the reconstruction process of attenuation map. Therefore, along with the soft tissue distribution provided by the segmentation of MR images, an air mask and a bone probability map breakdown the MR low-signal class into four subclasses in order to favour recognition of air and bone. Estimations on attenuation coefficients are realised as a mixture of pseudo-Gaussian distributions. The proposed algorithm is evaluated using the simulated 3D emission data. The proposed MLAA-TPA algorithm is compared with the MR-MLAA algorithm proposed by Heuser et al. Their results demonstrate that the MR-MLAA algorithm performance depends heavily on the MR segmentation accuracy well handled by MLAA-TPA. The quantification results illustrated that the MLAA-TPA outperformed the MR-MLAA algorithm owing to reduction of misclassification and more precise tissue detection.

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suffers from several problems, including crosstalk artifacts, slow convergence speed, and noisy attenuation maps (μ-maps). In this work, we developed deep convolutional neural networks (CNNs) to overcome these MLAA limitations, and we verified their feasibility using a clinical brain PET dataset. Methods: We applied the proposed method to one of the most challenging PET cases for simultaneous image reconstruction (18F-fluorinated-N-3-fluoropropyl-2-β-carboxymethoxy-3-β-(4-iodophenyl)nortropane [18F-FP-CIT] PET scans with highly specific binding to striatum of the brain). Three different CNN architectures (convolutional autoencoder [CAE], Unet, and Hybrid of CAE) were designed and trained to learn a CT-derived μ-map (μ-CT) from the MLAA-generated activity distribution and μ-map (μ-MLAA). The PET/CT data of 40 patients with suspected Parkinson disease were used for 5-fold cross-validation. For the training of CNNs, 800,000 transverse PET and CT slices augmented from 32 patient datasets were used. The similarity to μ-CT of the CNN-generated μ-maps (μ-CAE, μ-Unet, and μ-Hybrid) and μ-MLAA was compared using Dice similarity coefficients. In addition, we compared the activity concentration of specific (striatum) and nonspecific (cerebellum and occipital cortex) binding regions and the binding ratios in the striatum in the PET activity images reconstructed using those μ-maps. Results: The CNNs generated less noisy and more uniform μ-maps than the original μ-MLAA. Moreover, the air cavities and bones were better resolved in the proposed CNN outputs. In addition, the proposed deep learning approach was useful for mitigating the crosstalk problem in the MLAA reconstruction. The Hybrid network of CAE and Unet yielded the most similar μ-maps to μ-CT (Dice similarity coefficient in the whole head = 0.79 in the bone and 0.72 in air cavities), resulting in only about a 5% error in activity and binding ratio quantification. Conclusion: The proposed deep learning approach is promising for accurate attenuation correction of activity distribution in time-of-flight PET systems.

MR-guided joint reconstruction of activity and attenuation in brain PET-MR

With the advent of time-of-flight (TOF) PET scanners, joint maximum-likelihood reconstruction of activity and attenuation (MLAA) maps has recently regained attention for the estimation of PET attenuation maps from emission data. However, the estimated attenuation and activity maps are scaled by unknown scaling factors. We recently demonstrated that in hybrid PET-MR, the scaling issue of this algorithm can be effectively addressed by imposing MR spatial constraints on the estimation of attenuation maps using a penalized MLAA (P-MLAA+) algorithm. With the advent of simultaneous PET-MR systems, MRI-guided PET image reconstruction has also gained attention for improving the quantitative accuracy of PET images, usually degraded by noise and partial volume effects. The aim of this study is therefore to increase the benefits of MRI information for improving the quantitative accuracy of PET images by exploiting MRI-based anatomical penalty functions to guide the reconstruction of both activity and attenuation maps during their joint estimation. We employed an anato-functional joint entropy penalty function for the reconstruction of activity and an anatomical quadratic penalty function for the reconstruction of attenuation. The resulting algorithm was referred to as P-MLAA++ since it exploits both activity and attenuation penalty functions. The performance of the P-MLAA algorithms were compared with MLAA and the widely used activity reconstruction algorithms such as maximum likelihood expectation maximization (MLEM) and penalized MLEM (P-MLEM) both corrected for attenuation using a conventional MRI segmentation-based attenuation correction (MRAC) method. The studied methods were evaluated using simulations and clinical studies taking the PET image reconstructed using reference CT-based attenuation maps as a reference. The simulation results showed that the proposed method can notably improve the visual quality of the PET images by reducing noise while preserving structural boundaries and at the same time improving the quantitative accuracy of the PET images. Our clinical reconstruction results showed that the MLEM-MRAC, P-MLEM-MRAC, MLAA, P-MLAA+ and P-MLAA++ algorithms result in, on average, quantification errors of −13.5 ± 3.1%, −13.4 ± 3.1%, −2.0 ± 6.5%, −3.0 ± 3.5% and −4.2 ± 3.6%, respectively, in different regions of the brain. In conclusion, whilst the P-MLAA+ algorithm showed the best overall quantification performance, the proposed P-MLAA++ algorithm provided simultaneous partial volume and attenuation corrections with only a minor compromise of PET quantification.

MLAA-based attenuation correction of flexible hardware components in hybrid PET/MR imaging

BackgroundAccurate PET quantification demands attenuation correction (AC) for both patient and hardware attenuation of the 511 keV annihilation photons. In hybrid PET/MR imaging, AC for stationary hardware components such as patient table and MR head coil is straightforward, employing CT-derived attenuation templates. AC for flexible hardware components such as MR-safe headphones and MR radiofrequency (RF) surface coils is more challenging. Registration-based approaches, aligning CT-based attenuation templates with the current patient position, have been proposed but are not used in clinical routine. Ignoring headphone or RF coil attenuation has been shown to result in regional activity underestimation values of up to 18%.We propose to employ the maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm to estimate the attenuation of flexible hardware components. Starting with an initial attenuation map not including flexible hardware components, the attenuation update of MLAA is applied outside the body outline only, allowing to estimate hardware attenuation without modifying the patient attenuation map. Appropriate prior expectations on the attenuation coefficients are incorporated into MLAA. The proposed method is investigated for non-TOF PET phantom and 18F-FDG patient data acquired with a clinical PET/MR device, using headphones or RF surface coils as flexible hardware components.ResultsAlthough MLAA cannot recover the exact physical shape of the hardware attenuation maps, the overall attenuation of the hardware components is accurately estimated. Therefore, the proposed algorithm significantly improves PET quantification. Using the phantom data, local activity underestimation when neglecting hardware attenuation was reduced from up to 25% to less than 3% under- or overestimation as compared to reference scans without hardware present or to CT-derived AC. For the patient data, we found an average activity underestimation of 7.9% evaluated in the full brain and of 6.1% for the abdominal region comparing the uncorrected case with MLAA.ConclusionsMLAA is able to provide accurate estimations of the attenuation of flexible hardware components and can therefore be used to significantly improve PET quantification. The proposed approach can be readily incorporated into clinical workflow.

Attenuation correction in emission tomography using the emission data--A review.

The problem of attenuation correction (AC) for quantitative positron emission tomography (PET) had been considered solved to a large extent after the commercial availability of devices combining PET with computed tomography (CT) in 2001; single photon emission computed tomography (SPECT) has seen a similar development. However, stimulated in particular by technical advances toward clinical systems combining PET and magnetic resonance imaging (MRI), research interest in alternative approaches for PET AC has grown substantially in the last years. In this comprehensive literature review, the authors first present theoretical results with relevance to simultaneous reconstruction of attenuation and activity. The authors then look back at the early history of this research area especially in PET; since this history is closely interwoven with that of similar approaches in SPECT, these will also be covered. We then review algorithmic advances in PET, including analytic and iterative algorithms. The analytic approaches are either based on the Helgason-Ludwig data consistency conditions of the Radon transform, or generalizations of John's partial differential equation; with respect to iterative methods, we discuss maximum likelihood reconstruction of attenuation and activity (MLAA), the maximum likelihood attenuation correction factors (MLACF) algorithm, and their offspring. The description of methods is followed by a structured account of applications for simultaneous reconstruction techniques: this discussion covers organ-specific applications, applications specific to PET/MRI, applications using supplemental transmission information, and motion-aware applications. After briefly summarizing SPECT applications, we consider recent developments using emission data other than unscattered photons. In summary, developments using time-of-flight (TOF) PET emission data for AC have shown promising advances and open a wide range of applications. These techniques may both remedy deficiencies of purely MRI-based AC approaches in PET/MRI and improve standalone PET imaging.

Joint Estimation of Activity and Attenuation in Whole-Body TOF PET/MRI Using Constrained Gaussian Mixture Models.

It has recently been shown that the attenuation map can be estimated from time-of-flight (TOF) PET emission data using joint maximum likelihood reconstruction of attenuation and activity (MLAA). In this work, we propose a novel MRI-guided MLAA algorithm for emission-based attenuation correction in whole-body PET/MR imaging. The algorithm imposes MR spatial and CT statistical constraints on the MLAA estimation of attenuation maps using a constrained Gaussian mixture model (GMM) and a Markov random field smoothness prior. Dixon water and fat MR images were segmented into outside air, lung, fat and soft-tissue classes and an MR low-intensity (unknown) class corresponding to air cavities, cortical bone and susceptibility artifacts. The attenuation coefficients over the unknown class were estimated using a mixture of four Gaussians, and those over the known tissue classes using unimodal Gaussians, parameterized over a patient population. To eliminate misclassification of spongy bones with surrounding tissues, and thus include them in the unknown class, we heuristically suppressed fat in water images and also used a co-registered bone probability map. The proposed MLAA-GMM algorithm was compared with the MLAA algorithms proposed by Rezaei and Salomon using simulation and clinical studies with two different tracer distributions. The results showed that our proposed algorithm outperforms its counterparts in suppressing the cross-talk and scaling problems of activity and attenuation and thus produces PET images of improved quantitative accuracy. It can be concluded that the proposed algorithm effectively exploits the MR information and can pave the way toward accurate emission-based attenuation correction in TOF PET/MRI.

Emission-based estimation of lung attenuation coefficients for attenuation correction in time-of-flight PET/MR

In standard segmentation-based MRI-guided attenuation correction (MRAC) of PET data on hybrid PET/MRI systems, the inter/intra-patient variability of linear attenuation coefficients (LACs) is ignored owing to the assignment of a constant LAC to each tissue class. This can lead to PET quantification errors, especially in the lung regions. In this work, we aim to derive continuous and patient-specific lung LACs from time-of-flight (TOF) PET emission data using the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm. The MLAA algorithm was constrained for estimation of lung LACs only in the standard 4-class MR attenuation map using Gaussian lung tissue preference and Markov random field smoothness priors. MRAC maps were derived from segmentation of CT images of 19 TOF-PET/CT clinical studies into background air, lung, soft tissue and fat tissue classes, followed by assignment of predefined LACs of 0, 0.0224, 0.0864 and 0.0975 cm−1, respectively. The lung LACs of the resulting attenuation maps were then estimated from emission data using the proposed MLAA algorithm. PET quantification accuracy of MRAC and MLAA methods was evaluated against the reference CT-based AC method in the lungs, lesions located in/near the lungs and neighbouring tissues. The results show that the proposed MLAA algorithm is capable of retrieving lung density gradients and compensate fairly for respiratory-phase mismatch between PET and corresponding attenuation maps. It was found that the mean of the estimated lung LACs generally follow the trend of the reference CT-based attenuation correction (CTAC) method. Quantitative analysis revealed that the MRAC method resulted in average relative errors of −5.2 ± 7.1% and −6.1 ± 6.7% in the lungs and lesions, respectively. These were reduced by the MLAA algorithm to −0.8 ± 6.3% and −3.3 ± 4.7%, respectively. In conclusion, we demonstrated the potential and capability of emission-based methods in deriving patient-specific lung LACs to improve the accuracy of attenuation correction in TOF PET/MR imaging, thus paving the way for their adaptation in the clinic.

PET/MR: improvement of the UTE μ-maps using modified MLAA.

For a quantitative analysis in positron emission tomography (PET) or single-photon emission computed tomography (SPECT), attenuation correction (AC) is mandatory. CTscans or transmission scans are common tools for determination of the attenuation μ-map, but in the case of a PET/MR hybrid system it is difficult to associate one of these scans. Many techniques have been developed in order to improve AC for PET/MR. Some methods are based on template- or atlas techniques, other methods apply a segmentation technique based on Dixon or UTE (Ultrashort Echo Time) MR to create the μ-map, followed by a standard OSEM reconstruction (OSEM/DIXON and OSEM/UTE). A different approach for AC has been developed by employing the emission sinogram data in the μ-map derivation. In this context, we modified the iterative MLAA (Maximum-Likelihood reconstruction of Attenuation and Activity) algorithm to improve the resulting emission image from the PET/MR system. We constrained the attenuation map update using the UTE μ-map and the T1-weighted (T1w) MR image in order to improve convergence towards a solution. Results show that the modified MLAA algorithm improved the estimated emission image compared to standard OSEM/UTE and OSEM/DIXON. In certain regions of the brain, in particular close to the skull and the air cavities, the modified MLAA algorithm generated less error than OSEM/UTE and OSEM/Dixon. The modified MLAA algorithm is able to compute an attenuation μ-map that is slightly more similar to the aligned CT μ-map than the UTE μ-map.

Simultaneous reconstruction of attenuation and activity for non-TOF PET/MR using MR prior information.

Accurate quantification of the activity distribution in positron emission tomography (PET) mandates attenuation correction (AC). Unlike in PET/CT, AC in PET/MR is, however, challenging, since information about the attenuation properties of the patient tissue distribution is not available directly. Standard MR-based AC (MRAC) does not account for the presence of bone and, thus, yields an underestimation of the activity distribution. We propose an algorithm to simultaneously reconstruct the activity and attenuation distribution using MR images as anatomical prior information for non time-of-flight PET/MR. The proposed algorithm is an extension of the existing maximum-likelihood reconstruction of attenuation and activity (MLAA). The MR images are used to obtain an initial attenuation map and to derive voxel-dependent expectations on the attenuation coefficients. These expectations are modeled using pre-defined attenuation values and Gaussian-like probability functions. An iterative reconstruction scheme incorporating the prior information on the attenuation coefficients is used to update attenuation and activity distribution in an alternating manner. The algorithm, called MR-MLAA, is evaluated for simulated 2D PET data for two patients with artificial lesions in the head region. The proposed algorithm helps recover bone attenuation information. However, for both patients, some misclassifications of air (considered as bone) and bone (considered as air or soft tissue) were observed. Nevertheless, PET quantification in lesions located close to bone tissue is greatly improved when using MR-MLAA. Errors in activity estimation are reduced to ranges of -9% to +1% whereas MRAC yields errors of -22% to -10%. In conclusion, MR-MLAA has the potential to improve quantification in hybrid PET/MR, especially in regions adjacent to dense bone tissue.

Clinical Assessment of Emission- and Segmentation-Based MR-Guided Attenuation Correction in Whole-Body Time-of-Flight PET/MR Imaging

The joint maximum-likelihood reconstruction of activity and attenuation (MLAA) for emission-based attenuation correction has regained attention since the advent of time-of-flight PET/MR imaging. Recently, we improved the performance of the MLAA algorithm using an MR imaging-constrained gaussian mixture model (GMM). In this study, we compare the performance of our proposed algorithm with standard 4-class MR-based attenuation correction (MRAC) implemented on commercial systems. Five head and neck (18)F-FDG patients were scanned on PET/MR imaging and PET/CT scanners. Dixon fat and water MR images were registered to CT images. MRAC maps were derived by segmenting the MR images into 4 tissue classes and assigning predefined attenuation coefficients. For MLAA-GMM, MR images were segmented into known tissue classes, including fat, soft tissue, lung, background air, and an unknown MR low-intensity class encompassing cortical bones, air cavities, and metal artifacts. A coregistered bone probability map was also included in the unknown tissue class. Finally, the GMM prior was constrained over known tissue classes of attenuation maps using unimodal gaussians parameterized over a patient population. The results showed that the MLAA-GMM algorithm outperformed the MRAC method by differentiating bones from air gaps and providing more accurate patient-specific attenuation coefficients of soft tissue and lungs. It was found that the MRAC and MLAA-GMM methods resulted in average standardized uptake value errors of -5.4% and -3.5% in the lungs, -7.4% and -5.0% in soft tissues/lesions, and -18.4% and -10.2% in bones, respectively. The proposed MLAA algorithm is promising for accurate derivation of attenuation maps on time-of-flight PET/MR systems.

Accurate PET/MR Quantification Using Time of Flight MLAA Image Reconstruction

Quantification of positron emission tomography/magnetic resonance imaging (PET/MRI) studies is hampered by inaccurate MR-based attenuation correction (MR-AC). To date, most studies on MR-AC have been performed using PET/MR systems without time of flight (TOF). Maximum likelihood reconstruction of attenuation and activity (MLAA), however, has the potential to improve MR-AC by exploiting TOF. The purpose of this study is to assess the impact of MR-AC on PET image quantification for TOF-PET/MR systems and to evaluate PET accuracy when using TOF in combination with MLAA (TOF-MLAA). Simulations were designed to evaluate (1) the impact of MR-AC on PET quantification for different TOF windows (667, 500, 333 and 167 ps) and (2) use of TOF-MLAA for improving PET quantification. TOF-ordered subset expectation maximisation (OSEM) and TOF-MLAA reconstructions using MR-AC were compared with those obtained using TOF-OSEM with computed tomography-based AC (CT-AC). OSEM reconstructions without TOF showed a negative MR-AC-induced bias of -50 % in the bone. TOF-OSEM was able to reduce this bias down to -15 %, with more accurate results for better TOF. TOF-MLAA was able to reduce the bias to within 5 % but at the cost of a ∼40 % increase in image variance. TOF-MLAA can improve quantitative PET accuracy of PET/MR studies. Further improvements are anticipated with improving TOF performance.

Out-of-field activity in the estimation of mean lung attenuation coefficient in PET/MR

In clinical PET/MR, photon attenuation is a source of potentially severe image artifacts. Correction approaches include those based on MR image segmentation, in which image voxels are classified and assigned predefined attenuation coefficients to obtain an attenuation map. In whole-body imaging, however, mean lung attenuation coefficients (LAC) can vary by a factor of 2, and the choice of inappropriate mean LAC can have significant impact on PET quantification. Previously, we proposed a method combining MR image segmentation, tissue classification and Maximum Likelihood reconstruction of Attenuation and Activity (MLAA) to estimate mean LAC values. In this work, we quantify the influence of out-of-field (OOF) accidental coincidences when acquiring data in a single bed position. We therefore carried out GATE simulations of realistic, whole-body activity and attenuation distributions derived from data of three patients. A bias of 15% was found and significantly reduced by removing OOF accidentals from our data, suggesting that OOF accidentals are the major contributor to the bias. We found approximately equal contributions from OOF scatter and OOF randoms, and present results after correction of the bias by rescaling of results. Results using temporal subsets suggest that 30-second acquisitions may be sufficient for estimation mean LAC with less than 5% uncertainty if mean bias can be corrected for.