Comparison template-based with CT-based attenuation correction for hybrid MR/PET scanners.

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.

The combination of Positron Emission Tomography (PET) with magnetic resonance imaging (MRI) in hybrid PET/MRI scanners offers a number of advantages in investigating brain structure and function. A critical step of PET data reconstruction is attenuation correction (AC). Accounting for bone in attenuation maps (μ-map) was shown to be important in brain PET studies. While there are a number of MRI-based AC methods, no systematic comparison between them has been performed so far. The aim of this work was to study the different performance obtained by some of the recent methods presented in the literature. To perform such a comparison, we focused on [18F]-Fluorodeoxyglucose-PET/MRI neurodegenerative dementing disorders, which are known to exhibit reduced levels of glucose metabolism in certain brain regions. Four novel methods were used to calculate μ-maps from MRI data of 15 patients with Alzheimer's dementia (AD). The methods cover two atlas-based methods, a segmentation method, and a hybrid template/segmentation method. Additionally, the Dixon-based and a UTE-based method, offered by a vendor, were included in the comparison. Performance was assessed at three levels: tissue identification accuracy in the μ-map, quantitative accuracy of reconstructed PET data in specific brain regions, and precision in diagnostic images at identifying hypometabolic areas. Quantitative regional errors of -20--10% were obtained using the vendor's AC methods, whereas the novel methods produced errors in a margin of ±5%. The obtained precision at identifying areas with abnormally low levels of glucose uptake, potentially regions affected by AD, were 62.9 and 79.5% for the two vendor AC methods, the former ignoring bone and the latter including bone information. The precision increased to 87.5-93.3% in average for the four new methods, exhibiting similar performances. We confirm that the AC methods based on the Dixon and UTE sequences provided by the vendor are inferior to alternative techniques. As a novel finding, there was no substantial difference between the recently proposed atlas-based, template-based and segmentation-based methods.

Attenuation correction is known as a necessary step in positron emission tomography (PET) system to have accurate and quantitative activity images. Emission-based method is known as a promising approach for attenuation map estimation on TOF-PET scanners. The proposed method in this study imposes additional histogram-based information as a mixture model prior on the emission-based approach using maximum a posteriori (MAP) framework to improve its performance and make such a nearly segmented attenuation map. To eliminate misclassification of histogram modeling, a Median root prior is incorporated on the proposed approach to reduce the noise between neighbor voxels and encourage spatial smoothness in the reconstructed attenuation map. The joint-MAP optimization is carried out as an iterative approach wherein an alteration of the activity and attenuation updates is followed by a mixture decomposition of the attenuation map histogram. Also, the proposed method can segment attenuation map during the reconstruction. The evaluation of the proposed method on the numerical, simulation and real contexts indicate that the presented method has the potential to be used as a stand-alone method or even combined with other methods for attenuation correction on PET/MR systems.

Attenuation correction is necessary for SPECT quantification. There are a variety of methods to create attenuation maps. For dedicated breast SPECT imaging, it is unclear if either SPECT- or CT-based attenuation map would provide the most accurate quantification and whether or not segmenting the different tissue types will have an effect on the qunatification. For these experiments, 99mTc diluted in methanol and water was filled into geometric and anthropomorphic breast phantoms and was imaged with a dedicated dual-modality SPECT-CT scanner. SPECT images were collected using a compact CZT camera with various 3D acquisitions including vertical and 30° tilted parallel beam, and complex sinusoidal trajectories. CT images were acquired using a quasi-monochromatic x-ray source and CsI(T1) flat panel digital detector in a half-cone beam geometry. Measured scatter correction for SPECT and CT were implemented. To compare photon attenuation correction in the reconstructed SPECT images, various volumetric attenuation matrices were derived from 1) uniform SPECT, 2) uniform CT, and 3) segmented CT, populated with different attenuation coefficient values. Comparisons between attenuation masks using phantoms consisting of materials with different attenuation values show that at 140 keV the differences in the attenuation between materials do not affect the quantification as much as the size and alignment of the attenuation map. The CT-based attenuation maps give quantitative values 30% below the actual value, but are consistent. While the SPECT-based attenuation maps can provide within 10% accurate quantitative values, but are less consistent.

CT images acquired during deep-inspiration breath-holding (DIBH) have higher image quality than free-breathing (FB) CT images as they are free from respiratory motion artifacts. However, there exists significant mismatch between DIBH CT images and FB PET images since the displacement of organs in the thorax and abdomen at DIBH is significantly larger than that during regular FB. Our goal is to correct for the large mismatch between FB PET and DIBH CT in PET/CT imaging. The list-mode PET data were divided into four equal-count frames using amplitude based data-driven respiratory gating technique. Frame 1 and Frame 4 corresponded to end-expiration and end-inspiration respectively, and they were reconstructed using OSEM image reconstruction algorithm. To avoid attenuation-activity mismatch artifacts, the attenuation map used in attenuation correction was modified by filling the lung region with attenuation coefficient of soft tissue. 3D motion vector field from Frame 4 to Frame 1 was calculated from the reconstructed PET images using B-spline image registration algorithm. Assuming the respiratory motion during FB is consistent during FB and DIBH, the motion vector field from DIBH to end-inspiration phase is modeled by scaling the calculated respiratory motion from Frame 4 to Frame 1. The scalar was determined by maximizing the mutual information between transformed DIBH attenuation map and PET image at Frame 4. DIBH attenuation map was transformed using the scaled motion vector field from Frame 4 to Frame 1 to obtain attenuation map at Frame 4. Attenuation maps for other frames were obtained by transforming attenuation map at Frame 4 using corresponding motion vector fields. The method was applied to clinical patient data acquired using DIBH CT and FB PET protocol. While DIBH attenuation map creates significant mismatch artifacts in PET images, the proposed method was able to generate attenuation maps matching FB PET frames and reduce mismatch artifacts.

Attenuation correction is essential for reliable interpretation of emission tomography, however, the use of transmission measurements to generate attenuation maps is limited by availability of equipment and potential mismatches between the transmission and emission measurements. The authors present a first step toward a method of estimating an attenuation map from measured scatter data without a transmission scan. A scatter model has been developed that accurately predicts the distribution of photons which have been scattered once. The scatter model has been used as the basis of a maximum likelihood gradient ascent method to estimate an attenuation map from measured scatter data. In order to estimate both the attenuation map and activity distribution, iterations of the derived scatter based algorithm have been alternated with the maximum likelihood expectation maximization algorithm in a joint estimation process. For each iteration of the attenuation map estimation, the activity distribution is fixed at the values estimated during the previous activity iteration, and in each iteration of the activity distribution estimation the attenuation map is fixed at the values estimated during the previous attenuation iteration. The use of photopeak data to enhance the estimation of the attenuation map compared to the use of scatter data alone has also been considered. The algorithm derived has been used to reconstruct data simulated for an idealized two-dimensional situation and using a physical phantom. The reconstruction of idealized data demonstrated good reconstruction of both the activity distribution and attenuation map. The inclusion of information recorded in the photopeak energy window in the attenuation map estimation step demonstrated an improvement in the accuracy of the reconstruction, enabling an accurate attenuation map to be recovered. Validation of the results with physical phantom data demonstrated that different regions of attenuation could be distinguished in a real situation and produces results that represent a promising first step toward the use of scatter data to estimate the activity distribution and attenuation map from single photon emission tomography (SPECT) data without a transmission scan. The technique presented shows promise as a method of attenuation correction for SPECT data without the need for a separate transmission scan. Further work is required to further improve the method and to compensate for the assumptions used in the scatter model.

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.

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.

Deep learning (DL)-based attenuation correction (AC) for dedicated myocardial perfusion (MP) SPECT has been proposed recently, based on attenuation map (µ-map) generation or direct AC. This study aims to provide a direct comparison of the effectiveness of these two AC methods using simulation. We used a population of 100 XCAT phantoms modelling various body and organ sizes, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup> Tc-sestamibi distributions, defect sizes and locations. An analytical projector of a LEHR collimator with attenuation, scatter and collimator-detector response modeling was used to simulate 64 noisy projections for 180° based on a standard clinical count level. Projections were then reconstructed by OS-EM method with and without AC (NAC) using 12 iterations and 6 subsets. A 3D conditional generative adversarial network was implemented and optimized for the two DL-based AC methods respectively with training based on: (i) NAC SPECT paired with the corresponding µ-map. The projections were then reconstructed with the DL-generated µ-map for AC (DL-ACµ); (ii) NAC SPECT paired with the corresponding AC SPECT to perform direct AC (DL-AC). We randomly used 70, 10 and 20 phantoms for training, validation and testing respectively. The relative defect size difference (RSD) on polar maps, normalized mean square error (NMSE) and structural similarity index measure (SSIM) on a 3D cardiac VOI (36×36×36) were compared for DL-ACµ and DL-AC, using AC as the gold standard. The NMSE and SSIM were significantly lower for DL-ACµ as compared to DL-AC (p<0.0001). The RSD is also significantly lower for DL-ACµ (p<0.05). We conclude that DL-ACµ is superior to DL-AC for MP SPECT.

A critical question in the development of combined MR/PET scanners is whether MR can provide the tissue attenuation data required for PET reconstruction. Unfortunately, MR images are often unable to encompass the entire patient. The resulting truncation in the transverse plane leads to incomplete attenuation maps, causing artifacts in the reconstructed PET image. This article describes the experiments performed to quantify these artifacts. A method to compensate the missing data was evaluated to determine whether software correction is possible or whether additional transmission hardware has to be included in the scanner. Three studies were made. First, simulated PET data were used to quantify the bias due to an incomplete attenuation map. A set of spherical lesions was simulated in the lungs and mediastinum of a patient. The data were reconstructed with complete and partial attenuation maps and the uptake differences were evaluated. Second, clinical data from PET/CT oncology patients were used. To reproduce the expected conditions in an MR/PET scanner, only patients scanned with the arms resting along the body were considered. These scans were then used to create maps of the reconstruction bias due to field of view (FOV) limitations. Lastly, a PET reconstruction with incomplete attenuation data was evaluated as a means to obtain attenuation information beyond the MR FOV. The patient outline was automatically segmented with a three-dimensional snake algorithm and used to fill the truncated data in the attenuation map. Average bias up to 15% and local biases up to 50% were estimated when PET data were reconstructed with incomplete attenuation information. Completing the attenuation map with data extracted from a PET prereconstruction globally reduced these biases to below 10%. This correction proved to be tolerant to inaccuracies in positioning and attenuation values. However, local artifacts up to 20% could still be found near the edges of the MR FOV. MR FOV restrictions can indeed make the reconstructed PET data unacceptable for diagnostic purposes. Biases can be globally compensated by automatic preprocessing of the attenuation map. However, inaccuracies in the correction will result in small artifacts near the periphery of the image that could lead to false-positive findings.

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.

Although post-injection transmission scan (POST-TS) after 2-[(18)F]fluoro-2-deoxy- D-glucose ((18)F-FDG) injection[A1] is useful for short examination times, the emission count of (18)F-FDG[A2] in the regional brain area was not completely subtracted with use of the POST-TS method. The purpose of this study was to investigate the effect of POST-TS and attenuation correction (AC) methods on the normal database (NDB). A 10 min pre-injection transmission scan (PRE-TS) was performed before (18)F-FDG[A3] was injected in eighteen normal volunteers. A 10 min POST-TS was then conducted beginning 40 min after (18)F-FDG[A4] injection, followed by a 10 min 2-dimentional emission scanning. To reconstruct each image of normal volunteers, the reconstruction was performed using the filtered back-projection (FBP) method and the ordered subsets expectation maximization (OSEM) method, with transmission-based measured attenuation correction (MAC) and the segmented attenuation correction (SAC) technique. Subtraction images of NDB with PRE-TS or POST-TS were evaluated using 3D-SSP. A phantom study was also performed in addition to a human study, and assessment was by region of interests and profile curves. NDB images with POST-TS were significantly lower in the bilateral frontal lobes and higher in the parietal lobes and occipital lobes, including the precuneus, than those with PRE-TS, regardless of the different AC and reconstruction algorithms. Therefore, we have to be careful to confirm not only emission scan methods and reconstruction algorithms, but also TS methods and AC methods in the NDB. It will be best to perform PET examinations using the same TS methods and AC methods between NDB and patients.

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.

We studied whether TOF reduces error propagation from attenuation correction to PET image reconstruction in PET/MR neuroimaging, by using imperfect attenuation maps in a clinical PET/MR system with 525 ps timing resolution. Ten subjects who had undergone 18F-FDG PET neuroimaging were included. Attenuation maps using a single value (0.100 cm−1) with and without air, and a 3-class attenuation map with soft tissue (0.096 cm−1), air and bone (0.151 cm−1) were used. CT-based attenuation correction was used as a reference. Volume-of-interest (VOI) analysis was conducted. Mean bias and standard deviation across the brain was studied. Regional correlations and concordance were evaluated. Statistical testing was conducted. Average bias and standard deviation were slightly reduced in the majority (23–26 out of 35) of the VOI with TOF. Bias was reduced near the cortex, nasal sinuses, and in the mid-brain with TOF. Bland–Altman and regression analysis showed small improvements with TOF. However, the overall effect of TOF to quantitative accuracy was small (3% at maximum) and significant only for two attenuation maps out of three at 525 ps timing resolution. In conclusion, TOF might reduce the quantitative errors due to attenuation correction in PET/MR neuroimaging, but this effect needs to be further investigated on systems with better timing resolution.

Current PET/MR imaging systems use methods based on MR image segmentation with subsequent assignment of empirical attenuation coefficients for attenuation correction in PET image reconstruction. Delineation of bone in MR images has been challenging, especially in the head and neck areas, due to the difficulty of separating bone from air. In this work, we study deep learning techniques to automatically generate attenuation maps directly from MR images, with focus on the head and neck areas. We use a generative adversarial network (GAN) in a conditional setting for this image translation task. GANs separate the deep learning network into a generator, which tries to generate fake examples, and a discriminator, which learns to distinguish between real and fake examples, and train the two networks simultaneously. The objective function of the conditional GAN is a combination of the generator loss, discriminator loss, and the L1 distance between the label image and the output image from the generator. Image pairs of PET/MR image (input) and corresponding PET/CT based 511 keV photon attenuation map (label) are used to train the network. The network is trained for 6k iterations. The generator loss is trained from 2.0 to 1.4. The discriminator loss is trained from 0.6 to 1.0. The L1 loss is trained from 0.2 to 0.1. In our previous work with a basic autoencoder network for the conversion from MR images to corresponding attenuation maps, if we convert the defined L2 loss for the network to the RMS pixel value prediction error, the autoencoder network, after training, has a pixel prediction error of around 0.2 when all pixel values are scaled from 0 to 1. In this study, the L1 loss also represents this pixel prediction error, which is around 0.1 after the network is trained. This indicates an average reduction of 50% of the pixel prediction error.