Attenuation Correction for Myocardial Perfusion SPECT Imaging in the Image Domain

PurposeAttenuation correction (AC) based on low-dose computed tomography (CT) could be more accurate in brain single-photon emission computed tomography (SPECT) than the widely used Chang method, and, therefore, has the potential to improve both semi-quantitative analysis and visual image interpretation. The present study evaluated CT-based AC for dopamine transporter SPECT with I-123-ioflupane.Materials and methodsSixty-two consecutive patients in whom I-123-ioflupane SPECT including low-dose CT had been performed were recruited retrospectively at 3 centres. For each patient, 3 different SPECT images were reconstructed: without AC, with Chang AC and with CT-based AC. Distribution volume ratio (DVR) images were obtained by scaling voxel intensities using the whole brain without striata as reference. For assessing the impact of AC on semi-quantitative analysis, specific-to-background ratios (SBR) in caudate and putamen were obtained by fully automated SPM8-based region of interest (ROI) analysis and tested for their diagnostic power using receiver-operator-characteristic (ROC) analysis. For assessing the impact of AC on visual image reading, screenshots of stereotactically normalized DVR images presented in randomized order were interpreted independently by two raters at each centre.ResultsCT-based AC resulted in intermediate SBRs about half way between no AC and Chang. Maximum area under the ROC curve was achieved by the putamen SBR, with negligible impact of AC (0.924, 0.935 and 0.938 for no, CT-based and Chang AC). Diagnostic accuracy of visual interpretation also did not depend on AC.ConclusionsThe impact of CT-based versus Chang AC on the interpretation of I-123-ioflupane SPECT is negligible. Therefore, CT-based AC cannot be recommended for routine use in clinical patient care, not least because of the additional radiation exposure.

This study aimed to develop deep learning (DL) models for CT-free attenuation correction and Monte Carlo-based scatter correction in 99mTc-macroagregated albumin (99mTc-MAA) SPECT imaging, with the goal of enhancing quantitative accuracy for improved treatment planning and pre-therapy dosimetry in 90Y-selctive internal radiation therapy (SIRT). Data from 222 patients who underwent 99mTc-MAA SPECT imaging prior to 90Y-SIRT were included in this study. Uncorrected SPECT images (without attenuation and/or scatter correction) were used as input to a modified 3D shifted-window UNet Transformer (Swin UNETR) architecture. Three separate models were trained to predict attenuation corrected (AC), scatter corrected (SC), and joint attenuation and scatter corrected (ASC) SPECT images. The dataset was split into a training set (~ 80%) and an independent test set (~ 20%). Model training was performed using a five-fold cross-validation framework, with final evaluation conducted on the blind test set. To clinically assess model performance, 3D voxel-wise dosimetry was performed on the test set using the local energy deposition method, assuming 99mTc-MAA as a surrogate for 90Y distribution. Quantitative evaluation included organ- and voxel-level metrics, along with Gamma analysis using three combinations of distance-to-agreement (DTA, mm) and dose-difference (DD, %) criteria. The average (± SD) of the voxel-wise mean error (ME) was ≤ 0.003Gy for all tasks. The Relative Error (RE (%)) for AC, SC, and ASC tasks were 4.64 ± 7.52%, 8.99 ± 26.35%, and 16.45 ± 25.83%, respectively. Voxel-level Gamma evaluations within the whole body using three different criteria sets, including "DTA: 4.79mm, DD: 1%"; "DTA: 10mm, DD: 5%"; and "DTA: 15mm, DD: 10%" yielded pass rates of over 99.60%. The mean absolute error (MAE) for lesions, normal liver and lungs across all tasks were 3.16 ± 3.39, 0.35 ± 0.36, 0.41 ± 0.47Gy for AC, 1.97 ± 2.79, 0.19 ± 0.16, 0.22 ± 0.20Gy, for SC and 5.16 ± 7.10, 0.45 ± 0.51, and 0.34 ± 0.37Gy for ASC, respectively. Multiple models were developed for key SPECT quantification tasks, with potential value in clinical setting lacking reliable CT data or sufficient computational resources for Monte Carlo simulations. The models look promising for potential clinical translation and integration into commercial reconstruction software.

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.

LVEF measurements from cardiac PET/CT images obtained using CT based attenuation correction

Somatostatin receptor scintigraphy (SRS) is well-established in neuroendocrine tumour (NET) imaging. This study evaluated the impact of attenuation correction (AC) on SRS SPECT data in patients examined by SPECT-CT. Planar scintigraphy and SPECT-CT of 17 patients (10 men, seven women; age, 40-74 years; mean, 62 years) suffering from NET were included. For the visual assessment of AC, the intensity and contrast of foci classified as pathological were rated in both the non-attenuation corrected (NAC) and the attenuation corrected (AC) SPECT images using a 5-point score. The change in signal intensity after AC was semiquantified two-fold for each focus in both SPECT(AC) and SPECT(NAC): firstly by using tumour-to-background (TB) ratios (defined as T(max)/B(mean)) for the determination of a TB(AC)/TB(NAC) ratio. Secondly, by a T(max,AC)/T(max,NAC) ratio. Both ratios were correlated to the focus depth. A total of 46 pathological foci were found. Focus contrast and intensity significantly increased in 14/46 foci (30%) after AC (mean, 3.7-4.0) in the visual analysis (P<0.001). While TB ratios increased only in 24/46 foci after AC and no correlation between the T(BAC)/T(BNAC) ratio and focus depth (r=0.027; P=0.856) was found, T(max) was higher after AC in all foci and the T(max,AC)/T(max,NAC) ratio showed the expected correlation to focus depth (r=0.650; P<0.01), indicating the superiority of the Tmax approach for the demonstration of the effects of attenuation correction on focal uptake. Attenuation correction of SRS SPECT data by SPECT-CT results in visually more clearly contrasted foci. Moreover, as focus intensity increases, especially in the more centrally localised foci, CT-based AC has a potential to further improve the sensitivity of SRS SPECT.

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.

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.

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.

BackgroundAccurate quantification of radioactivity, measured by an integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) system, is still a challenge. One aspect of such a challenge is to correct for the hardware attenuation, such as the patient table and radio frequency (RF) resonators. For PET/MRI systems, computed tomography (CT) is commonly used to produce hardware attenuation correction (AC) maps, by converting Hounsfield units (HU) to a linear attenuation coefficients (LAC) map at the PET energy level 511 keV, using a bilinear model. The model does not address beam hardening, nor higher density materials, which can lead to inaccurate corrections.PurposeIn this study, we introduce a transmission-based (TX-based) AC technique with a static Germanium-68 (Ge-68) transmission source to generate hardware AC maps using the PET/MRI system itself, without the need for PET or medical CT scanners. The AC TX-based maps were generated for a homogeneous cylinder, made of acrylic as a validator. The technique thereafter was applied to the patient table and posterior part of an RF-phased array used in cardiovascular PET/MRI imaging. The proposed TX-based, and the CT-based, hardware maps were used in reconstructing PET images of one cardiac patient, and the results were analysed and compared.ResultsThe LAC derived by the TX-based method for the acrylic cylinder is estimated to be 0.10851 ± 0.00380 cm−1 compared to the 0.10698 ± 0.00321 cm−1 theoretical value reported in the literature. The PET photon counts were reduced by 8.7 ± 1.1% with the patient table, at the region used in cardiac scans, while the CT-based map, used for correction, over-estimated counts by 4.3 ± 1.3%. Reconstructed in vivo images using TX-based AC hardware maps have shown 4.1 ± 0.9% mean difference compared to those reconstructed images using CT-based AC.ConclusionsThe LAC of the acrylic cylinder measurements using the TX-based technique was in agreement with those in the literature confirming the validity of the technique. The over-estimation of photon counts caused by the CT-based model used for the patient table was improved by the TX-based technique. Therefore, TX-based AC of hardware using the PET/MRI system itself is possible and can produce more accurate images when compared to the CT-based hardware AC in cardiac PET images.

Purpose To assess the patient-dependent accuracy of atlas-based attenuation correction (ATAC) for brain positron emission tomography (PET) in an integrated time-of-flight (TOF) PET/magnetic resonance (MR) imaging system. Materials and Methods Thirty recruited patients provided informed consent in this institutional review board-approved study. All patients underwent whole-body fluorodeoxyglucose PET/computed tomography (CT) followed by TOF PET/MR imaging. With use of TOF PET data, PET images were reconstructed with four different attenuation correction (AC) methods: PET with patient CT-based AC (CTAC), PET with ATAC (air and bone from an atlas), PET with ATACpatientBone (air and tissue from the atlas with patient bone), and PET with ATACboneless (air and tissue from the atlas without bone). For quantitative evaluation, PET mean activity concentration values were measured in 14 1-mL volumes of interest (VOIs) distributed throughout the brain and statistical significance was tested with a paired t test. Results The mean overall difference (±standard deviation) of PET with ATAC compared with PET with CTAC was -0.69 kBq/mL ± 0.60 (-4.0% ± 3.2) (P < .001). The results were patient dependent (range, -9.3% to 0.57%) and VOI dependent (range, -5.9 to -2.2). In addition, when bone was not included for AC, the overall difference of PET with ATACboneless (-9.4% ± 3.7) was significantly worse than that of PET with ATAC (-4.0% ± 3.2) (P < .001). Finally, when patient bone was used for AC instead of atlas bone, the overall difference of PET with ATACpatientBone (-1.5% ± 1.5) improved over that of PET with ATAC (-4.0% ± 3.2) (P < .001). Conclusion ATAC in PET/MR imaging achieves similar quantification accuracy to that from CTAC by means of atlas-based bone compensation. However, patient-specific anatomic differences from the atlas causes bone attenuation differences and misclassified sinuses, which result in patient-dependent performance variation of ATAC. © RSNA, 2017 Online supplemental material is available for this article.

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.

Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P<0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.

Monte Carlo (MC)-based estimation of dose-averaged linear energy transfer (LETd) in proton therapy is accurate but computationally intensive. Although deep learning (DL) models offer efficient alternatives, few have been optimized for multi-source inputs or validated on anatomically complex tumors, such as nasopharyngeal carcinoma (NPC). This study developed and evaluated a multi-source DL framework for voxel-level LETd prediction in proton therapy for NPC, aiming to identify optimal input configurations and assess both prediction accuracy and clinical reliability. This study analyzed 100 patients with NPC, yielding 500 proton therapy fields. Ground-truth LETd maps were generated using GPU-based Monte Carlo simulations. A deep learning model was trained with different combinations of four input features: dose distribution, CT image, beam mask, and radiological depth, resulting in eight configurations (Mdose, Mdose+ct, Mdose+beam, Mdose+depth, Mdose+beam+depth, Mdose+ct+depth, Mdose+ct+beam, and Mdose+ct+beam+depth). The dataset was randomly divided into training (300 fields, 60 patients), validation (100 fields, 20 patients), and testing (100 fields, 20 patients) sets. Model performance was evaluated using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), normalized cross-correlation (NCC), structural similarity index measure (SSIM), and 3D Gamma analysis. A 3D U-Net baseline was retrained under identical conditions for comparison. Model interpretability was examined with Gradient-weighted Class Activation Mapping (Grad-CAM), and prediction confidence was assessed using uncertainty maps. The Mdose+ct model achieved the best performance (MAE 0.21±0.03keV/µm, PSNR 28.99±1.33, NCC 0.98±0.01, SSIM 0.971±0.007, Gamma 3%/3mm: 94.69±3.69%), outperforming the 3D U-Net baseline (MAE 0.38±0.52keV/µm). Grad-CAM visualizations confirmed that the model focused on physically relevant regions, while uncertainty maps highlighted high-variability zones at LETd transitions. The model generated voxel-level LETd predictions in approximately 7 s per case, which was substantially faster than GPU-based MC simulations (30 s per case). The proposed multi-source deep learning framework enables rapid and accurate voxel-level LETd prediction for NPC. Combining dose with CT yielded the most reliable input configuration, and incorporating interpretability and uncertainty analyses enhances confidence in its suitability for biologically guided proton therapy planning.

Ischemic heart disease remains a leading cause of mortality worldwide. Myocardial perfusion imaging (MPI) using Rubidium-82 (82Rb) positron emission tomography (PET) is a cornerstone in its evaluation. However, conventional CT-based attenuation correction (AC) is prone to artifacts, with misalignment between PET emission data and the CT-AC being a common problem. This study evaluates the feasibility of introducing a deep learning approach to generate synthetic CT (sCT) images directly from non-attenuation-corrected 82Rb-PET images. To this end, we developed a cGAN using a conditional generative adversarial network (cGAN) with an Attention U-Net generator to produce sCT images to produce sCT-AC maps, based upon 544 PET/CT MPI scans. Image quality was assessed using structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and mean error (ME). Additionally, attenuation-corrected PET images based on sCT were evaluated in the cardiac region using relative mean error (RME) and relative mean absolute error (RMAE). Cardiac function and perfusion assessments, defined as the ischemic total perfusion deficit (iTPD) and the left ventricular ejection fraction reserve (LVEFR), were compared between sCT-based and conventional CT AC methods. Our sCT-images provided good correlation to the conventional CT-AC (SSIM = 0.91 ± 0.037, PSNR = 29.9 ± 3.2dB). For the PET images, we report a slight bias in the cardiac region (RME = 4.2 ± 7.8%, RMAE = 6.9 ± 5.9%), likely due to a uniform overestimation of the soft-tissue u-maps within the sCT. Despite the bias, the quantification metrics remained comparable to those obtained with CT AC (mean iTPD: CT 3.73 ± 5.19% vs. sCT 3.67 ± 5.13%; mean LVEFR: CT 5.88 ± 5.96% vs. sCT 5.90 ± 6.11%). Additionally, the sCT-based approach appeared to reduce motion and implant-related artifacts, providing further motivation for its use over CT. This observation was made through visual inspection on a case-by-case basis. These results demonstrate the potential of deep learning-based sCT generation to maintain integrity in PET MPI while helping to mitigate issues related to misalignments and metal-induced artifacts.

BackgroundIn integrated PET/MR hybrid imaging the evaluation of PET performance characteristics according to the NEMA standard NU 2–2007 is challenging because of incomplete MR-based attenuation correction (AC) for phantom imaging. In this study, a strategy for CT-based AC of the NEMA image quality (IQ) phantom is assessed. The method is systematically evaluated in NEMA IQ phantom measurements on an integrated PET/MR system.MethodsNEMA IQ measurements were performed on the integrated 3.0 Tesla PET/MR hybrid system (Biograph mMR, Siemens Healthcare). AC of the NEMA IQ phantom was realized by an MR-based and by a CT-based method. The suggested CT-based AC uses a template μ-map of the NEMA IQ phantom and a phantom holder for exact repositioning of the phantom on the systems patient table. The PET image quality parameters contrast recovery, background variability, and signal-to-noise ratio (SNR) were determined and compared for both phantom AC methods. Reconstruction parameters of an iterative 3D OP-OSEM reconstruction were optimized for highest lesion SNR in NEMA IQ phantom imaging.ResultsUsing a CT-based NEMA IQ phantom μ-map on the PET/MR system is straightforward and allowed performing accurate NEMA IQ measurements on the hybrid system. MR-based AC was determined to be insufficient for PET quantification in the tested NEMA IQ phantom because only photon attenuation caused by the MR-visible phantom filling but not the phantom housing is considered. Using the suggested CT-based AC, the highest SNR in this phantom experiment for small lesions (<= 13 mm) was obtained with 3 iterations, 21 subsets and 4 mm Gaussian filtering.ConclusionThis study suggests CT-based AC for the NEMA IQ phantom when performing PET NEMA IQ measurements on an integrated PET/MR hybrid system. The superiority of CT-based AC for this phantom is demonstrated by comparison to measurements using MR-based AC. Furthermore, optimized PET image reconstruction parameters are provided for the highest lesion SNR in NEMA IQ phantom measurements.