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

MR-based PET attenuation correction using segmented MR images is a promising approach to compensating for the lack of a transmission source in PET/MR and solving the problem of attenuation correction. However, the method is prone to errors in the choice of patient-individual lung attenuation coefficients (LAC), which are difficult to determine from MR data. Maximum Likelihood reconstruction of Attenuation and Activity (MLAA) can be applied to reconstruct attenuation maps from PET emission data. We present a constrained MLAA variant using segmentation of the lungs and tissue classification of the rest of the body, and focus on the evaluation of estimated mean LACs. In simple simulation studies, mean LACs can be estimated with errors as low as 5%, while in realistic ones, uncorrected out-of-field accidental coincidences seem to introduce bias.

BackgroundPET data-driven attenuation correction (AC) methods, including deep learning, are attractive options for quantitative brain imaging on CT-less brain PET systems and low-dose PET/CT. However, current schemes have performance and practical limitations. We previously developed a CT-less transmission-aided AC that combines coincidences from a weak positron source, and the patient, to estimate attenuation with physics alone. In this work, we aim to optimize and assess this new AC method during human [18F]FDG neuroimaging on whole-body PET/CT.MethodsOur approach, TRansmission-aided μ-map reconstruction (TRU) AC, includes 1) a low-profile and physically fixed transmission source filled with ~ 14 MBq of 18F, 2) a modified maximum likelihood reconstruction of attenuation and activity algorithm, and 3) scatter corrections using the exam data alone. We imaged N = 5 patients with the transmission source, immediately after their clinical [18F]FDG PET/CT. The clinically-consistent protocol included a CT and 10-minute brain-focused PET exam. Using this 10 minutes of patient PET data alone, radiotracer images were reconstructed with the vendor’s algorithm and TRU-AC or CT-AC (reference standard), with all else matched. For quantitative analysis, we placed brain-structure volumes of interest with an atlas, and computed error in mean standardized uptake values of TRU-AC relative to CT-AC.ResultsTRU-AC PET showed qualitatively strong agreement with CT-AC. For the VOI analysis, absolute relative error in standardized uptake values for TRU-AC was within 3.6%, across all brain structures and patients. Normalized root mean square error of activity bias for TRU-AC was 1.8%, and voxel-wise noise in the cerebellum showed a very minor increase of 0.2%. Bland–Altman analysis demonstrated that TRU-AC and CT-AC have statistically significant agreement, assuming a maximum allowed difference of ± 5%.ConclusionsTRU-AC enables quantitative PET for human neuroimaging. This approach may particularly benefit exams where deep learning-based AC schemes show reduced performance, including those focused on radiotracer development, new patient cohorts, and/or pathologies that often lack sufficient training data.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40658-025-00803-x.

Attenuation correction of emission PET images with average CT: Interpolation from breath-hold CT

Quantitative PET image reconstruction requires an accurate map of photon attenuation coefficients (μ-map) in order to correct the PET emission data. Current PET/MR imaging systems use methods based on MR image segmentation with subsequent assignment of empirical attenuation coefficients. In this study we examine the differences in the quantification of 18F-FDG standardized uptake values (SUV) in head and neck cancer, using two different MR imaging sequences for MR-based attenuation correction (MRAC): a zero echo time (ZTE) sequence which can image bone directly (ZTE-MRAC), and a vendor-provided 2-point Dixon sequence that neglects bone (Dixon-MRAC). The μ-maps from each MRAC techniques were compared to CT-based attenuation correction (CTAC) maps. Percent SUV-mean and SUV-max differences in relevant regions of interest (ROIs) were calculated for three patients. Relative to Dixon-MRAC, we observed 15±7% and 14±8% increase of SUV-mean and SUV-max, respectively, when ZTE-based bone information was incorporated in the attenuation map and using Dixon-based attenuation map, respectively. We also observed that use of Dixon-MRAC led to 7±7% and 8±8% underestimation of SUV-mean and SUV-max, respectively, whereas with ZTE-MRAC led to 6±8% and 5±8% higher SUV-mean and SUV-max, respectively, compared to CTAC. This study is the first demonstration of ZTE-based attenuation correction in the head and neck region and compared with CTAC as a gold standard with the goal of improving PET quantitation. The study shows that incorporation of bone information on μ-maps has a significant impact on SUV quantitation in head and neck cancer lesions.

BackgroundPositron emission tomography–magnetic resonance (PET-MR) attenuation correction is challenging because the MR signal does not represent tissue density and conventional MR sequences cannot image bone. A novel zero echo time (ZTE) MR sequence has been previously developed which generates signal from cortical bone with images acquired in 65 s. This has been combined with a deep learning model to generate a synthetic computed tomography (sCT) for MR-only radiotherapy. This study aimed to evaluate this algorithm for PET-MR attenuation correction in the pelvis.MethodsTen patients being treated with ano-rectal radiotherapy received a 18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F-FDG-PET-MR in the radiotherapy position. Attenuation maps were generated from ZTE-based sCT (sCTAC) and the standard vendor-supplied MRAC. The radiotherapy planning CT scan was rigidly registered and cropped to generate a gold standard attenuation map (CTAC). PET images were reconstructed using each attenuation map and compared for standard uptake value (SUV) measurement, automatic thresholded gross tumour volume (GTV) delineation and GTV metabolic parameter measurement. The last was assessed for clinical equivalence to CTAC using two one-sided paired t tests with a significance level corrected for multiple testing of p≤0.05/7=0.007\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p \\le 0.05/7 = 0.007$$\\end{document}. Equivalence margins of ±3.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 3.5\\%$$\\end{document} were used.ResultsMean whole-image SUV differences were −0.02% (sCTAC) compared to −3.0% (MRAC), with larger differences in the bone regions (−0.5% to −16.3%). There was no difference in thresholded GTVs, with Dice similarity coefficients ≥0.987\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 0.987$$\\end{document}. However, there were larger differences in GTV metabolic parameters. Mean differences to CTAC in SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\max}$$\\end{document} were 1.0±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.0 \\pm 0.8\\%$$\\end{document} (± standard error, sCTAC) and -4.6±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-4.6 \\pm 0.9\\%$$\\end{document} (MRAC), and 1.0±0.7%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.0 \\pm 0.7\\%$$\\end{document} (sCTAC) and -4.3±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-4.3 \\pm 0.8\\%$$\\end{document} (MRAC) in SUVmean\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\rm mean}$$\\end{document}. The sCTAC was statistically equivalent to CTAC within a ±3.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm 3.5\\%$$\\end{document} equivalence margin for SUVmax\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\max}$$\\end{document} and SUVmean\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathrm {SUV}}_{\\rm mean}$$\\end{document} (p=0.007\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.007$$\\end{document} and p=0.002\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.002$$\\end{document}), whereas the MRAC was not (p=0.88\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.88$$\\end{document} and p=0.83\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p = 0.83$$\\end{document}).ConclusionAttenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.

For PET/CT systems, PET image reconstruction requires corresponding CT images for anatomical localization and attenuation correction. In the case of PET respiratory gating, multiple gated CT scans can offer phase-matched attenuation and motion correction, at the expense of increased radiation dose. We aim to minimize the dose of the CT scan, while preserving adequate image quality for the purpose of PET attenuation correction by introducing sparse view CT data acquisition.We investigated sparse view CT acquisition protocols resulting in ultra-low dose CT scans designed for PET attenuation correction. We analyzed the tradeoffs between the number of views and the integrated tube current per view for a given dose using CT and PET simulations of a 3D NCAT phantom with lesions inserted into liver and lung. We simulated seven CT acquisition protocols with {984, 328, 123, 41, 24, 12, 8} views per rotation at a gantry speed of 0.35 s. One standard dose and four ultra-low dose levels, namely, 0.35 mAs, 0.175 mAs, 0.0875 mAs, and 0.043 75 mAs, were investigated. Both the analytical Feldkamp, Davis and Kress (FDK) algorithm and the Model Based Iterative Reconstruction (MBIR) algorithm were used for CT image reconstruction. We also evaluated the impact of sinogram interpolation to estimate the missing projection measurements due to sparse view data acquisition. For MBIR, we used a penalized weighted least squares (PWLS) cost function with an approximate total-variation (TV) regularizing penalty function. We compared a tube pulsing mode and a continuous exposure mode for sparse view data acquisition. Global PET ensemble root-mean-squares-error (RMSE) and local ensemble lesion activity error were used as quantitative evaluation metrics for PET image quality.With sparse view sampling, it is possible to greatly reduce the CT scan dose when it is primarily used for PET attenuation correction with little or no measureable effect on the PET image. For the four ultra-low dose levels simulated, sparse view protocols with 41 and 24 views best balanced the tradeoff between electronic noise and aliasing artifacts. In terms of lesion activity error and ensemble RMSE of the PET images, these two protocols, when combined with MBIR, are able to provide results that are comparable to the baseline full dose CT scan. View interpolation significantly improves the performance of FDK reconstruction but was not necessary for MBIR. With the more technically feasible continuous exposure data acquisition, the CT images show an increase in azimuthal blur compared to tube pulsing. However, this blurring generally does not have a measureable impact on PET reconstructed images.Our simulations demonstrated that ultra-low-dose CT-based attenuation correction can be achieved at dose levels on the order of 0.044 mAs with little impact on PET image quality. Highly sparse 41- or 24- view ultra-low dose CT scans are feasible for PET attenuation correction, providing the best tradeoff between electronic noise and view aliasing artifacts. The continuous exposure acquisition mode could potentially be implemented in current commercially available scanners, thus enabling sparse view data acquisition without requiring x-ray tubes capable of operating in a pulsing mode.

BackgroundMRI does not offer a direct method to obtain attenuation correction maps as its predecessors (stand-alone PET and PET/CT), and bone visualisation is particularly challenging. Recently, zero-echo-time (ZTE) was suggested for MR-based attenuation correction (AC). The aim of this work was to evaluate ZTE- and atlas-AC by comparison to 68Ge-transmission scan-based AC.Nine patients underwent brain PET/MR and stand-alone PET scanning using the dopamine transporter ligand 11C-PE2I. For each of them, two AC maps were obtained from the MR images: an atlas-based, obtained from T1-weighted LAVA-FLEX imaging with cortical bone inserted using a CT-based atlas, and an AC map generated from proton-density-weighted ZTE images. Stand-alone PET 68Ge-transmission AC map was used as gold standard. PET images were reconstructed using the three AC methods and standardised uptake value (SUV) values for the striatal, limbic and cortical regions, as well as the cerebellum (VOIs) were compared. SUV ratio (SUVR) values normalised for the cerebellum were also assessed. Bias, precision and agreement were calculated; statistical significance was evaluated using Wilcoxon matched-pairs signed-rank test.ResultsBoth ZTE- and atlas-AC showed a similar bias of 6–8% in SUV values across the regions. Correlation coefficients with 68Ge-AC were consistently high for ZTE-AC (r 0.99 for all regions), whereas they were lower for atlas-AC, varying from 0.99 in the striatum to 0.88 in the posterior cortical regions. SUVR showed an overall bias of 2.9 and 0.5% for atlas-AC and ZTE-AC, respectively. Correlations with 68Ge-AC were higher for ZTE-AC, varying from 0.99 in the striatum to 0.96 in the limbic regions, compared to atlas-AC (0.99 striatum to 0.77 posterior cortex).ConclusionsAbsolute SUV values showed less variability for ZTE-AC than for atlas-AC when compared to 68Ge-AC, but bias was similar for both methods. This bias is largely caused by higher linear attenuation coefficients in atlas- and ZTE-AC image compared to 68Ge-images. For SUVR, bias was lower when using ZTE-AC than for atlas-AC. ZTE-AC shows to be a more robust technique than atlas-AC in terms of both intra- and inter-patient variability.

The goal of this study was to compare MR-based PET patient attenuation correction (AC) to CT-based AC in the head using clinical whole-body FDG-PET patient data obtained from a tri-modality PET/CT & MR setup. The MR-based AC utilizes an atlas-based approach, registering the patient's MR images to a CT-based atlas, producing `pseudoCT' images. Thirteen clinical whole-body FDG patients were included in this study. PET mean activity concentration values were measured and compared in six ~ 15 ml volumes-of-interest throughout the brain tissue. The AC methods compared to CT-based AC were segmentation of the CT (air, fat, soft tissue) and atlas-based MR-AC. Results: PET activity concentration was systematically under-estimated on average by 1.32 kBq/ml (4.9%) when using the segmented CT-based AC, mainly due to lack of attenuation correction for skull bone. Using the atlas-based method, the error was reduced to 0.03 kBq/ml (0.2%) on average. PET image visualization demonstrated spatial variations in activity concentration accuracy induced by the AC methods that were consistent with the approximations in each method. Conclusion: The results demonstrate that the atlas-based AC in the head provides adequate PET quantitation and image quality as compared to methods that do not account for bone.

We describe a new MR-based attenuation correction (MRAC) method for neurological studies performed using integrated PET/MR scanners. The method, combining the advantages of image segmentation and atlas-based approaches to generate a high-resolution template, is based on the widely available SPM8 software and provides robust and accurate linear attenuation coefficients (LACs) for head while requiring minimal user interaction. Atlas generation: 3T MR and CT images from 15 glioblastoma subjects were used to generate the high-resolution atlas. MR images were segmented into 6 tissue classes: GM, WM, CSF, soft tissue, bone and air)[1]. Tissue classes were then coregistered using an iterative diffeomorphic image registration algorithm [2] to form the template. Atlas validation: The template was validated on 16 subjects. SyN [3] and IRTK [4], considered state-of-the-art for non-rigid image registration[5], were used for comparison. Final attenuation maps were created from the warped CT atlas following [6]. PET images were then reconstructed using the proposed methods as well as the manufacturer’s built-in method (dual-echo Dixon-VIBE sequence) [7] and compared to the gold standard CT-based attenuation correction (CTAC). The qualitative and quantitative analysis of the attenuation maps revealed that the SPM8-based method produces very robust results (Figure ​(Figure1).1). In terms of the PET data quantification, we observed improvements of > 70% compared to the VIBE-based method (Table ​(Table11 and Figure ​Figure2).2). When compared to SyN-based image registration, the SPM8 approach showed improved global results on the brain area (Figures ​(Figures11 and ​and22). Figure 1 Comparison of LACs from a validation subject for our proposed method (A), the SyN method (B) and the manufacturer’s built-in Dixon method (C) to the gold standard CTAC (D). Image differences with respect to the gold standard CTAC of our method ... Table 1 Summary of voxel- and ROI-based results between our method (atlas) and the current manufacturer’s method (Dixon) Figure 2 PET images from a validation subject reconstructed with our proposed method (A), with the SyN method (B) and with the manufacturer’s built-in Dixon method (C), compared with the gold standard CTAC (D). Relative changes (in % with respect to gold ... We presented a new MRAC technique for brain images acquired on simultaneous PET/MR scanners. The new approach relies on segmentation- and atlas-based features to provide robust and more accurate LACs than using state-of-art non-rigid image registration while avoiding sophisticated user input or interaction.

Attenuation correction (AC) of PET data acquired in hybrid MR/PET scanners is still a challenge, even for brain imaging. A correction method previously proposed by our group and obtained with template-based attenuation maps (AM) used MR and ECAT HR+ images. In this study we investigated the influence of both template-based and CT-based AC methods on reconstructed images. The data of two volunteers, measured in the Siemens 3T MR-BrainPET scanner, were used. Corresponding CT images were acquired. Due to the different resolution of ECAT and CT-derived AMs 3D Gaussian kernels with 3, 5, 7, and 9 mm filter width were applied to the latter ones. The CT-based AM with 3 mm filter was considered as reference. The BrainPET emission data were reconstructed with the five AMs: AMtemplate and AMCTxmm (with x = 3, 5, 7, and 9). The reconstructed PET images were normalized to the MNI brain for using the AAL-VOI Atlas analysis. The final brain regions considered were the frontal, temporal, parietal, occipital, and cerebellum, whith the corresponding AAL-VOIs merged to one single VOI. Furthermore, caudate nucleus, putamen, and thalamus were considered. The relative differences between AMCT3mm and all other four AMs were calculated. Considering first only the CT-derived AMs the relative differences ranged between 0.11% (putamen area with AMCT9mm) and 2.70% (merged cerebellum area with AMCT9mm). The comparison of AMCT3mm with AMtemplate over all VOIs showed an overall, equally distributed underestimation of 8.0%. The template-based AC method proposed by our group does not show any remarkable differences when compared to ECAT data (results not shown here). At the same time provides a reliable AC method for brain imaging measured in MR-PET scanners. Nevertheless, the number of tested subjects is still low and an analysis of a larger dataset is an ongoing running project.

Attenuation correction (AC) is an important methodical step in positron emission tomography/magnetic resonance imaging (PET/MRI) to correct for attenuated and scattered PET photons. The overall quality of magnetic resonance (MR)-based AC in whole-body PET/MRI was evaluated in direct comparison to computed tomography (CT)-based AC serving as reference. The quantitative impact of isolated tissue classes in the MR-AC was systematically investigated to identify potential optimization needs and strategies. Data of n=60 whole-body PET/CT patients with normal lung tissue and without metal implants/prostheses were used to generate six different AC-models based on the CT data for each patient, simulating variations of MR-AC. The original continuous CT-AC (CT-org) is referred to as reference. A pseudo MR-AC (CT-mrac), generated from CT data, with four tissue classes and a bone atlas represents the MR-AC. Relative difference in linear attenuation coefficients (LAC) and standardized uptake values were calculated. From the results two improvements regarding soft tissue AC and lung AC were proposed and evaluated. The overall performance of MR-AC is in good agreement compared to CT-AC. Lungs, heart, and bone tissue were identified as the regions with most deviation to the CT-AC (myocardium -15%, bone tissue -14%, and lungs ±20%). Using single-valued LACs for AC in the lung only provides limited accuracy. For improved soft tissue AC, splitting the combined soft tissue class into muscles and organs each with adapted LAC could reduce the deviations to the CT-AC to <±1%. For improved lung AC, applying a gradient LAC in the lungs could remarkably reduce over- or undercorrections in PET signal compared to CT-AC (±5%). The AC is important to ensure best PET image quality and accurate PET quantification for diagnostics and radiotherapy planning. The optimized segment-based AC proposed in this study, which was evaluated on PET/CT data, inherently reduces quantification bias in normal lung tissue and soft tissue compared to the CT-AC reference.

For attenuation correction (AC) in PET/MRI systems, segmentation-based methods are most often used. However, the standardized uptake value (SUV) of lesions in the bone and liver, which have higher attenuation coefficients than other organs, can be underestimated, potentially leading to misinterpretation of clinical cases. Errors in SUV estimation are also dependent on the segmentation schemes used in the segmentation-based AC. In this study, this potential bias in SUV estimation using 4 different segmentation-based AC methods was evaluated for the PET/CT data of cancer patients with bone and liver lesions. Forty patients who had spine or liver lesions and underwent (18)F-FDG PET/CT participated (18 women and 22 men; 20 spine lesions and 20 liver lesions; mean age (± SD), 60.5 ± 11.4 y; mean body weight, 57.7 ± 10.4 kg). The patient body region was extracted from the CT image and categorized into 5 tissue groups (air, lungs, fat, water, and bone) using Hounsfield unit thresholds, which were determined from the CT histogram. Four segmentation-based AC methods (SLA [soft-tissue/lung/air], WFLA [water/fat/lung/air], SLAB [soft-tissue/lung/air/bone], and WFLAB [water/fat/lung/air/bone]) were compared with CT-based AC. The mean attenuation coefficient for each group was calculated from 40 CT images and assigned to the attenuation maps. PET sinograms were reconstructed using segmentation- and CT-based AC maps, and mean SUV in the lesions was compared. Mean attenuation coefficients for air, lungs, fat, water, and bone were 0.0058, 0.0349, 0.0895, 0.0987, and 0.1178 cm(-1), respectively. In the spine lesions, the SUVs were underestimated by 16.4% ± 8.5% (SLA AC) and 14.7% ± 7.5% (WFLA AC) but not to a statistically significant extent for SLAB and WFLAB AC relative to CT AC. In the liver lesions, the SUVs were underestimated by 11.1% ± 2.6%, 8.1% ± 3.0%, 6.8% ± 3.8%, and 4.1% ± 3.8% with SLA, SLAB, WFLA, and WFLAB AC, respectively. Without bone segmentation, the SUVs of spine lesions were considerably underestimated; however, the bias was acceptable with bone segmentation. In liver lesions, the segmentation-based AC methods yielded a negative bias in SUV; however, inclusion of the bone and fat segments reduced the SUV bias. The results of this study will be useful for understanding organ-dependent bias in SUV between PET/CT and PET/MRI.

Attenuation correction (AC) for both patient and hardware is required for accurate PET quantification. The current clinical value of hybrid PET/MR imaging is limited because MR-based AC (MRAC) does not properly account for bone attenuation and neglects attenuation of flexible hardware components (headphones, flexible body coils). To improve PET quantification for PET/MR, we developed a new reconstruction approach combining ideas of our recently published methods for emission-based patient and hardware AC. The new algorithm, xMR-MLAA, is based on maximum-likelihood reconstruction of attenuation and activity (MLAA), and estimates patient and hardware attenuation in an interleaved manner. The proposed method was evaluated using clinical brain scans acquired with a Siemens Biograph mMR scanner. Conventional T1-weighted MR images were used to derive the patient support as well as anatomical prior information on the patient attenuation distribution. Standard MRAC results in activity underestimation in the brain, caused both by neglecting headphone attenuation and by treating bone as soft tissue during AC. Across three FDG patients included in this study, average brain activity underestimation was 14.8% compared to CTAC. Using xMR- LAA, bone attenuation information could be accurately recovered while preserving air cavities like the paranasal sinuses and the inner ears. Moreover, accurate estimates of the headphone attenuation were obtained. Average brain activity underestimation with xMR- LAA was reduced to 3.1% compared to CTAC.

MR-based attenuation correction (AC) in PET/MR imaging is currently evaluated by many groups. The aim of this study is to investigate the feasibility of using the non-attenuated PET images (PET-NAC) as a means for the AC of PET images in PET/MR systems. A water fillable torso phantom with hollow spheres was scanned on a GE Discovery-RX PET/CT scanner. The activity concentration in the spheres and background was 37.2 and 6.27 kBq/cc respectively. A PET scan was acquired in 3D using 2 FOV with 5min / FOV. CT-AC PET (PET-CTAC), PET-NAC, and CT images were generated. In addition the PET-NAC images were used to create an attenuation map through an iterative segmentation process in three steps. In the first step of the process, the phantom body contour was segmented from the PET-NAC image. Voxels inside the contour were then assigned a value of 0.094 cm−1 to represent the attenuation coefficient of soft tissue at 511 keV. This segmented attenuation map was then used to attenuate correct the raw PET data and the resulting PET images were used as the input to the second step of the process. The lung region was segmented in the second step and voxels were assigned a value of 0.024 cm−1 representing the attenuation coefficients of lung tissue at 511 keV. The updated attenuation map was then used for a second time to attenuate correct the raw PET data, and the resulting PET images were used as the input to the third step. The purpose of the third step is to delineate parts of the heart and liver from the lung contour since these parts were unavoidably included in the lung contour of the second step. These parts were then corrected by using a value of 0.094 cm−1 in the attenuation map. Finally the attenuation coefficients of the scanner couch were included based on the CT images to eliminate the impact of the couch on the accuracy of AC. This final attenuation map was then used to attenuate correct the raw PET data and resulted in a final PET image (PET-IAC). Visual inspection and SUV measurements of the spheres between PET-IAC and the PET-CTAC were performed to assess the accuracy of image quantification. Furthermore, our proposed approach was used on two patients with lung and liver tumors to assess its feasibility in clinical studies. The results show that there is very small difference between the PET-IAC and PET-CTAC from visual inspection. PET-IAC SUVs are on average equal to 94±3% compared to the SUVs from the PET-CTAC in phantom study. For the two patient studies, this value is 111±12%. In conclusion, the use of PET-NAC as a means for the AC of PET images is feasible in the clinic. Such approach can potentially be an alternative method of MR-based AC in PET/MR imaging.

Generally, the Chang method depends on counts for the attenuation correction (AC) method in brain perfusion single-photon emission computed tomography (SPECT), because the head outlined for a uniform attenuation coefficient map is set to the sinogram of the projection data by the threshold (Sinogram Threshold Chang method). Magnetic resonance imaging (MRI) is a routine examination in our hospital. Patients who underwent N-isopropyl-p-[(123)I] iodoamphetamine ((123)I-IMP) SPECT are undergoing MRI. Therefore, we thought it help AC accuracy to set an accurate head outline by using the image. We jointly made "Software for an attenuation coefficient map using MRI" for trial purposes. This paper investigated whether the AC method using MRI promotes the accuracy of brain perfusion SPECT in some clinical samples. With AC methods using gamma ray transmission computed tomography (TCT) or X-ray CT (CT) also being taken into account, the AC method using MRI was compared with the Sinogram Threshold Chang method. As a result, count dependency was excluded by an accurate head outline setting that used MRI, and the AC method using MRI approached the effect of the AC method using TCT and CT more than the Sinogram Threshold Chang method. Therefore, it is suggested that the AC method using MRI is useful for the accuracy of brain perfusion SPECT.