CT Based Attenuation Correction for PET Brain Imaging

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.

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.

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.

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), as an essential component of quantitative SPECT imaging, is highly challenging in the absence of CT or transmission scanning in dedicated cardiac SPECT devices. This study investigated the potential of direct attenuation correction (AC) of <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup> Tc-sestamibi SPECT myocardial perfusion imaging (MPI) in the image domain using residual (ResNet) and UNet deep convolutional neural networks. MPI-SPECT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">99m</sup> Tc-ses-tamibi images of 99 subjects were retrospectively used, considering non-attenuation corrected SPECT images as input and CT-based attenuation corrected (CT-AC) SPECT images as reference. Chang’s AC technique was also performed as a baseline for evaluating the performance of deep learning-based models. The quantitative accuracy of the attenuation corrected SPECT images was validated using the proposed methods considering the corresponding CT-AC SPECT images of 19 external datasets as reference. Image-derived metrics, including the voxel-wise mean absolute error (MAE), relative error (RE), structural similarity index measure (SSIM), as well as clinically relevant indices, such as total perfusion deficit (TPD), were calculated. The quantitative results obtained from the external validation dataset of the deep learning models exhibited good agreement with the reference CT-based attenuation corrected MPI-SPECT images. The deep learning models showed superior performance over Chang’s AC method. The ResNet and UNet models resulted in MAE of 20.24±17.63 and 13.65±11.23 and SSIM of 0.99±0.04 and 0.98±0.05, respectively. In comparison, Chang’s approach led to MAE and SSIM of 80.27±47.56 and 0.93±0.09, respectively. Similarly, the clinical evaluation showed a mean TPD of 12.78±9.22% and 12.57±8.93% for ResNet and UNet models, respectively, compared to TPD of 12.84±8.63% for reference SPECT CT-AC images. In contrast, Chang’s approach led to a mean TPD of 16.68±11.24%. The evaluation of deep learning approaches implemented in this work showed the promising potential of deep learning-based techniques in producing attenuation corrected MPI-SPECT images in the image domain without anatomical imaging.

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.

Our X-ray systems study group measured and examined the characteristics of four transportable inverter-type X-ray equipments. X-ray tube voltage and X-ray tube current were measured with the X-ray tube voltage and the X-ray tube current measurement terminals provided with the equipment. X-ray tube voltage, irradiation time, and dose were measured with a non-invasive X-ray tube voltage-measuring device, and X-ray output was measured by fluorescence meter. The items investigated were the reproducibility and linearity of X-ray output, error of pre-set X-ray tube voltage and X-ray tube current, and X-ray tube voltage ripple percentage. The waveforms of X-ray tube voltage, the X-ray tube current, and fluorescence intensity draw were analyzed using the oscilloscope gram and a personal computer. All of the equipment had a preset error of X-ray tube voltage and X-ray tube current that met JIS standards. The X-ray tube voltage ripple percentage of each equipment conformed to the tendency to decrease when X-ray tube voltage increased. Although the X-ray output reproducibility of system A exceeded the JIS standard, the other systems were within the JIS standard. Equipment A required 40 ms for X-ray tube current to reach the target value, and there was some X-ray output loss because of a trough in X-ray tube current. Owing to the influence of the ripple in X-ray tube current, the strength of the fluorescence waveform rippled in equipments B and C. Waveform analysis could not be done by aliasing of the recording device in equipment D. The maximum X-ray tube current of transportable inverter-type X-ray equipment is as low as 10-20 mA, and the irradiation time of chest X-ray photography exceeds 0.1 sec. However, improvement of the radiophotographic technique is required for patients who cannot move their bodies or halt respiration. It is necessary to make the irradiation time of the equipments shorter for remote medical treatment.

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 evaluate the accuracy of scaling CT images for attenuation correction of PET data measured for bone. While the standard tri-linear approach has been well tested for soft tissues, the impact of CT-based attenuation correction on the accuracy of tracer uptake in bone has not been reported in detail. We measured the accuracy of attenuation coefficients of bovine femur segments and patient data using a tri-linear method applied to CT images obtained at different kVp settings. Attenuation values at 511 keV obtained with a 68Ga/68Ge transmission scan were used as a reference standard. The impact of inaccurate attenuation images on PET standardized uptake values (SUVs) was then evaluated using simulated emission images and emission images from five patients with elevated levels of FDG uptake in bone at disease sites. The CT-based linear attenuation images of the bovine femur segments underestimated the true values by 2.9 ± 0.3% for cancellous bone regardless of kVp. For compact bone the underestimation ranged from 1.3% at 140 kVp to 14.1% at 80 kVp. In the patient scans at 140 kVp the underestimation was approximately 2% averaged over all bony regions. The sensitivity analysis indicated that errors in PET SUVs in bone are approximately proportional to errors in the estimated attenuation coefficients for the same regions. The variability in SUV bias also increased approximately linearly with the error in linear attenuation coefficients. These results suggest that bias in bone uptake SUVs of PET tracers ranges from 2.4% to 5.9% when using CT scans at 140 and 120 kVp for attenuation correction. Lower kVp scans have the potential for considerably more error in dense bone. This bias is present in any PET tracer with bone uptake but may be clinically insignificant for many imaging tasks. However, errors from CT-based attenuation correction methods should be carefully evaluated if quantitation of tracer uptake in bone is important.

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.

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

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.

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.

Purpose: The main goal of this study was to determine the optimal collimator in the absence of medium energy collimators along with the impact of Attenuation Correction (AC) and different iterative reconstruction protocols on the quantitative evaluation of Gallium-67 (67Ga) SPECT/CT imaging.&#x0D; Materials and Methods: A GE Discovery 670 dual-head SPECT/CT scanner and a NEMA phantom filled with 67Ga solution were used to scan the patients. The projections were acquired with both Low Energy High Resolution (LEHR) and High Energy General Purpose (HEGP) collimators, and CT images were acquired to evaluate the effect of attenuation correction. SPECT data were reconstructed using the ordered subset expectation maximization (OSEM) method with various combinations of iterations and subsets. The performance was quantified, and a clinical study validated the phantom study.&#x0D; Results: Acquired images by the HEGP collimator yielded higher Contrast Recovery (CR) and Contrast to Noise Ratio (CNR) in images with AC than those without non-AC (41.6% and 74.2%, respectively). The CNR in all spheres after AC was increased by 80.4% (82.1%) for the HEGP collimator against the LEHR collimator. Also, an increase in iterations × subsets from 16 to 48 led to the Coefficient of Variation (COV) increasing by 17.2%, 16.67%, 15.50%, 14.4%, 14.2%, and 14.1% for 10 mm to 37 mm sphere diameter, respectively.&#x0D; Conclusion: CT-based AC and HEGP collimators can yield improved 67Ga SPECT quantification compared to Non-AC and LEHR collimators. The choice of the optimal collimator with the reconstruction protocol led to changes in the image quality and quantitative accuracy, emphasizing the need to carefully select the appropriate combination of data acquisition factors.&#x0D;

Attenuation correction of PET data requires accurate determination of the attenuation map (mumap), which represents the spatial distribution of linear attenuation coefficients of different tissues at 511 keV. The presence of high-density metallic dental filling material in head and neck X-ray computed tomography (CT) scanning is known to generate streak artefacts in the resulting CT images and thus in the corresponding mumaps generated using CT-based attenuation correction. Consequently, an under/overestimation of activity concentration occurs in corresponding regions of the corrected PET images. The purpose of this study is to develop a simple yet practical approach for reduction of metallic dental implant artefacts in the generated mumaps. Currently available sinogram-based metal artefact reduction (MAR) algorithms operate directly on the raw sinograms. These usually consist of huge files stored in proprietary format not easily disclosed by the manufacturers and thus are not straightforward to read and manipulate. The proposed method uses the concept of virtual sinograms produced by forward projection of CT images in Dicom format for MAR. The projection data affected by metallic objects are detected in the sinogram space through segmentation of metallic objects in the CT image followed by forward projection of the metal-only image. Thereafter, the affected sinogram bins are replaced by interpolated values from adjacent projections using the spline interpolation technique. The algorithm was assessed using a polyethylene phantom containing materials simulating different tissues and a dedicated jaw phantom scanned before and after the insertion of metallic objects, where the corrected and noncorrected mumaps were compared with the artefact-free mumap. In addition, the Jaszczak and standard germanium phantoms including four metallic inserts were scanned on a PET/CT scanner to evaluate the impact of the MAR procedure on PET data through the comparison of uncorrected and corrected PET images to the actual activity concentrations in the phantoms. The proposed algorithm was also applied to head and neck CT images of 10 patients with metallic dental implants. The MAR method proved to be practical in a clinical setting and reduced substantially the visible metal induced artefacts. The mean relative error in regions close to metallic objects is reduced by approximately 90%. The statistical analysis of the Jaszczak and solid Ge-68 phantoms PET images did not reveal statistically significant differences between the corrected and artefact-free images (P>0.05). Moreover, the evaluation of clinical studies did not reveal statistically significant differences between the attenuation coefficients of the corrected mumaps and the expected theoretical values. The proposed MAR method provides a simple and convenient approach allowing correction for the presence of metal artefacts caused by dental implants without the need to manipulate the complex raw CT data. Further evaluation using a larger clinical PET/CT database is under way to evaluate the potential of the technique in a clinical setting.