Ordered Subset Expectation Maximization Image Research Articles

Reduced count pediatric whole-body 18F-FDG PET imaging reconstruction with a Bayesian penalized likelihood algorithm.

Advanced positron emission tomography (PET) image reconstruction methods promise to allow optimized PET/CT protocols with improved image quality,decreased administered activity and/or acquisition times. To evaluate the impact of reducing counts(simulating reduced acquisition time) in block sequential regularized expectation maximization (BSREM) reconstructed pediatric whole-body 18F-fluorodeoxyglucose (FDG) PET images, and to compare BSERM with ordered-subset expectation maximization (OSEM) reconstructed reduced-count images. Twenty children (16 male) underwent clinical whole-body 18F-FDG PET/CT examinations using a 25-cm axial field-of-view (FOV) digital PET/CT system at 90s per bed (s/bed) with BSREM reconstruction (β=700). Reduced count simulations with varied BSREM β levels were generated from list-mode data: 60s/bed, β=800; 50s/bed, β=900; 40s/bed, β=1000; and 30s/bed, β=1300. In addition, a single OSEM reconstruction was created at 60s/bed based on prior literature. Qualitative (Likert scores) and quantitative (standardized uptake value [SUV]) analyses were performed to evaluate image quality and quantitation across simulated reconstructions. The mean patient age was 9.0 ± 5.5 (SD) years, mean weight was 38.5 ± 24.5kg, and mean administered 18F-FDG activity was 4.5 ± 0.7 (SD) MBq/kg. Between BSREM reconstructions, no qualitative measure showed a significant difference versus the 90s/bed β=700 standard (all P>0.05). SUVmax values for lesions were significantly lower from 90s/bed, β=700 only at a simulated acquisition time of 30s/bed, β=1300 (P=0.001). In a side-by-side comparison of BSREM versus OSEM reconstructions, 40s/bed, β=1000 images were generally preferred over 60s/bed TOF OSEM images. In children who undergo whole-body 18F-FDG PET/CT on a 25-cm FOV digital PET/CT scanner, reductions in acquisition time or, by corollary, administered radiopharmaceutical activity of >50% from a clinical standard of 90s/bed may be possible while maintaining diagnostic quality when a BSREM reconstruction algorithm is used.

Verification of optimal conditions for the scattering correction of 123I-FP-CIT SPECT on a single-photon emission tomography system with a two-detector whole-body cadmium-zinc-telluride semiconductor detector.

To identify the optimal scattering correction for 123I-FP-CIT SPECT (DAT-SPECT) using a two-detector whole-body cadmium-zinc-telluride semiconductor detector (C-SPECT) system with a medium-energy high-resolution sensitivity (MEHRS) collimator. The C-SPECT system with the MEHRS collimator assessed image quality and quantification using a striated phantom. Different reconstruction methods and scattering correction settings were compared, including filtered back projection (FBP) and ordered subset expectation maximization (OSEM). Higher %contrast and %CV values were observed > 10% subwindow (SW) for all conditions, with no significant differences between images without scattering correction and those < 7% SW. The FBP images show a greater increase in %CV > 10% SW than the OSEM images. The specific binding ratio in the radioactivity ratio of 8:1 was higher than the true value under all conditions. The C-SPECT system with an MEHRS collimator provided accurate scattering suppression and enabled high-quality imaging for DAT-SPECT. Careful setting of the scattering correction is essential for total count accuracy.

Comparison of post reconstruction- and reconstruction-based deep learning denoising methods in cardiac SPECT.

Objective. The quality of myocardial perfusion SPECT (MPS) images is often hampered by low count statistics. Poor image quality might hinder reporting the studies and in the worst case lead to erroneous diagnosis. Deep learning (DL)-based methods can be used to improve the quality of the low count studies. DL can be applied in several different methods, which might affect the outcome. The aim of this study was to investigate the differences between post reconstruction- and reconstruction-based denoising methods.Approach. A UNET-type network was trained using ordered subsets expectation maximization (OSEM) reconstructed MPS studies acquired with half, quarter and eighth of full-activity. The trained network was applied as a post reconstruction denoiser (OSEM+DL) and it was incorporated into a regularized reconstruction algorithm as a deep learning penalty (DLP). OSEM+DL and DLP were compared against each other and against OSEM images without DL denoising in terms of noise level, myocardium-ventricle contrast and defect detection performance with signal-to-noise ratio of a non-prewhitening matched filter (NPWMF-SNR) applied to artificial perfusion defects inserted into defect-free clinical MPS scans. Comparisons were made using half-, quarter- and eighth-activity data.Main results. OSEM+DL provided lower noise level at all activities than other methods. DLP's noise level was also always lower than matching activity OSEM's. In addition, OSEM+DL and DLP outperformed OSEM in defect detection performance, but contrary to noise level ranking DLP had higher NPWMF-SNR overall than OSEM+DL. The myocardium-ventricle contrast was highest with DLP and lowest with OSEM+DL. Both OSEM+DL and DLP offered better image quality than OSEM, but visually perfusion defects were deeper in OSEM images at low activities.Significance. Both post reconstruction- and reconstruction-based DL denoising methods have great potential for MPS. The preference between these methods is a trade-off between smoother images and better defect detection performance.

Newly-designed adaptive non-blind deconvolution with structural similarity index in single-photon emission computed tomography

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

3D-OSEM versus FORE + OSEM: Optimal Reconstruction Algorithm for FDG PET with a Short Acquisition Time.

Objective In this study, we investigated the optimal reconstruction algorithm in fluorodeoxyglucose (FDG) positron emission tomography (PET) with a short acquisition time. Materials and Methods In the phantom study, six spheres filled with FDG solution (sphere size: 6.23-37 mm; radioactivity ratio of spheres to background = 8:1) and placed in a National Electrical Manufacturers Association phantom were evaluated. Image acquisition time was 15 to 180 seconds, and the obtained image data were reconstructed using each of the Fourier rebinning (FORE) + ordered subsets expectation-maximization (OSEM) and 3D-OSEM algorithms. In the clinical study, mid-abdominal images of 19 patients were evaluated using regions of interest placed on areas of low, intermediate, and high radioactivity. All obtained images were investigated visually, and quantitatively using maximum standardized uptake value (SUV) and coefficient of variation (CV). Results In the phantom study, FORE + OSEM images with a short acquisition time had large CVs (poor image quality) but comparatively constant maximum SUVs. 3D-OSEM images showed comparatively constant CVs (good image quality) but significantly low maximum SUVs. The results of visual evaluation were well correlated with those of quantitative evaluation. Small spheres were obscured on 3D-OSEM images with short acquisition time, but image quality was not greatly deteriorated. The clinical and phantom studies yielded similar results. Conclusion FDG PET images with a short acquisition time reconstructed by FORE + OSEM showed poorer image quality than by 3D-OSEM. However, images obtained with a short acquisition time and reconstructed with FORE + OSEM showed clearer FDG uptake and more useful than 3D-OSEM in the light of the detection of lesions.

Impact of Region-of-Interest Delineation on Stability and Reproducibility of Liver SNR Measurements in 68 Ga-PSMA PET/CT.

Objective This study aims to assess the impact of various regions of interest (ROIs) and volumes of interest (VOIs) delineations on the reproducibility of liver signal-to-noise-ratio (SNRliver) measurements, as well as to find the most reproducible way to estimate it in gallium-68 positron emission tomography ( 68 Ga-PET) imaging. We also investigated the SNRliver-weight relationship for these ROIs and VOIs delineations. Methods A cohort of 40 patients (40 males; mean weight: 76.5 kg [58-115 kg]) with prostate cancer were included. 68 Ga-PET/CT imaging (mean injected activity: 91.4 MBq [51.2 MBq to 134.1 MBq] was performed on a 5-ring bismuth germanium oxide-based Discovery IQ PET/CT using ordered subset expectation maximization image reconstruction algorithm. Afterward, circular ROIs and spherical VOIs with two different diameters of 30 and 40 mm were drawn on the right lobe of the livers. The performance of the various defined regions was evaluated by the average standardized uptake value (SUV mean ), standard deviation (SD) of the SUV (SUV SD ), SNR liver , and SD of the SNR liver metrics. Results There were no significant differences in SUV mean among the various ROIs and VOIs ( p > 0.05). On the other hand, the lower SUV SD was obtained by spherical VOI with diameter of 30 mm. The largest SNR liver was obtained by ROI (30 mm). The SD of SNR liver with ROI (30 mm) was also the largest, while the lowest SD of SNR liver was observed for VOI (40 mm). There is a higher correlation coefficient between the patient-dependent parameter of weight and the image quality parameter of SNRliver for both VOI (30 mm) and VOI (40 mm) compared to the ROIs. Conclusion Our results indicate that SNR liver measurements are affected by the size and shape of the respective ROIs and VOIs. The spherical VOI with a 40 mm diameter leads to more stable and reproducible SNR measurement in the liver.

Impact of Bayesian penalized likelihood reconstruction on quantitative and qualitative aspects for pulmonary nodule detection in digital 2-[18F]FDG-PET/CT

To evaluate the impact of block sequential regularized expectation maximization (BSREM) reconstruction on quantitative and qualitative aspects of 2-[18F]FDG-avid pulmonary nodules compared to conventional ordered subset expectation maximization (OSEM) reconstruction method. Ninety-one patients with 144 2-[18F]FDG-avid pulmonary nodules (all ≤ 20 mm) undergoing PET/CT for oncological (re-)staging were retrospectively included. Quantitative parameters in BSREM and OSEM (including point spread function modelling) were measured, including maximum standardized uptake value (SUVmax). Nodule conspicuity in BSREM and OSEM images was evaluated by two readers. Wilcoxon matched pairs signed-rank test was used to compare quantitative and qualitative parameters in BSREM and OSEM. Pulmonary nodule SUVmax was significantly higher in BSREM images compared to OSEM images [BSREM 5.4 (1.2–20.7), OSEM 3.6 (0.7–17.4); p = 0.0001]. In a size-based analysis, the relative increase in SUVmax was more pronounced in smaller nodules (≤ 7 mm) as compared to larger nodules (8–10 mm, or > 10 mm). Lesion conspicuity was higher in BSREM than in OSEM (p < 0.0001). BSREM reconstruction results in a significant increase in SUVmax and a significantly improved conspicuity of small 2-[18F]FDG-avid pulmonary nodules compared to OSEM reconstruction. Digital 2-[18F]FDG-PET/CT reading may be enhanced with BSREM as small lesion conspicuity is improved.

Effects of New Bayesian Penalized Likelihood Reconstruction Algorithm on Visualization and Quantification of Upper Abdominal Malignant Tumors in Clinical FDG PET/CT Examinations.

PurposeThis study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximization (OSEM) reconstruction algorithm. Metabolic tumor volume (MTV) and texture features (TFs), as well as SUV-related metrics, were evaluated to clarify the BPL effects on quantification.Materials and MethodsA total of 153 upper abdominal lesions (82 liver metastatic and 71 pancreatic cancers) were included in this study. FDG PET/CT images were acquired with a GE Discovery 710 scanner equipped with a time-of-flight system. Images were reconstructed using OSEM and BPL (beta 700) algorithms. In 58 lesions <1.5 cm in greatest diameter (small-lesion group), visual image quality of each lesion was evaluated using a four-point scale. SUVmax was obtained for quantitative metrics. Visual scores and SUVmax were compared between OSEM and BPL images. In 95 lesions >2.0 cm in greatest diameter (larger-lesion group), SUVmax, SUVpeak, MTV, and six TFs were compared between OSEM and BPL images. In addition to the size-based analyses, an increase of SUVmax with BPL was evaluated according to the original SUVmax in OSEM images.ResultsIn the small-lesion group, both visual score and SUVmax were significantly higher in the BPL than OSEM images. The increase in visual score was observed in 20 (34%) of all 58 lesions. In the larger-lesion group, no statistical difference was observed in SUVmax, SUVpeak, or MTV between OSEM and BPL images. BPL increased high gray-level zone emphasis and decreased low gray-level zone emphasis among six TFs compared to OSEM with statistical significance. No statistical differences were observed in other TFs. SUVmax-based analysis demonstrated that BPL increased and decreased SUVmax in lesions with low (<5) and high (>10) SUVmax in original OSEM images, respectively.ConclusionThis study demonstrated that BPL improved conspicuity of small or low-count upper abdominal malignant lesions in clinical FDG PET/CT examinations. Only two TFs represented significant differences between OSEM and BPL images of all quantitative metrics in larger lesions.

Comparison of ordered-subset expectation maximization and filtered back projection reconstruction based on quantitative outcome from dynamic [18F]NaF PET images.

[18F]NaF PET imaging is a useful tool for measuring regional bone metabolism. However, due to tracer in urine, [18F]NaF PET images of the hip reconstructed using filtered back projection (FBP) frequently show streaking artifacts in slices through the bladder leading to noisy time-activity curves unsuitable for quantification. This study compares differences between quantitative outcomes at the hip derived from images reconstructed using the FBP and ordered-subset expectation maximization (OSEM) methods. Dynamic [18F]NaF PET data at the hip for four postmenopausal women were reconstructed using FBP and nine variations of the OSEM algorithm (all combinations of 1, 5, 15 iterations and 10, 15, 21 subsets). Seven volumes of interest were placed in the hip. Bone metabolism was measured using standardized uptake values, Patlak analysis (Ki-PAT) and Hawkins model Ki-4k. Percentage differences between the standardized uptake values and Ki values from FBP and OSEM images were assessed. OSEM images appeared visually smoother and without the streaking artifacts seen with FBP. However, due to loss of counts, they failed to recover the quantitative values in VOIs close to the bladder, including the femoral head and femoral neck. This was consistent for all quantification methods. Volumes of interest farther from the bladder or larger and receiving greater counts showed good convergence with 5 iterations and 21 subsets. For VOIs close to the bladder, including the femoral neck and femoral head, 15 iterations and 10, 15 or 21 subsets were not enough to obtain OSEM images suitable for measuring bone metabolism and showed no improvement compared to FBP.

Influence of Reconstruction FOV and Matrix Size on the Quantitative Accuracy of FDG-PET: Comparison between OSEM and Bayesian Penalized Likelihood

Field of view (FOV) and matrix size determine the pixel size of positron emission tomography (PET) images; however, the effect of any variation in these parameters on the quantitative accuracy is unclear. The FOV and matrix size of PET images are adjusted as per each clinical objective. Therefore, this study aimed to evaluate the quantitative accuracy of PET images under different FOV and matrix sizes. A National Electrical Manufacturers Association (NEMA) body phantom set was filled with 18F-FDG solution, and imaging data were acquired for 30 min. Images were reconstructed using ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL), both of which were combined with point spread function (PSF) and time of flight (TOF). In each reconstruction method, the image parameters were set to the following: FOV, 20-70 cm; matrix size, 128×128 to 384×384; and pixel size, 1-3 mm. The images were evaluated by physical assessment of the recovery coefficient (RC) and maximum standardized uptake value ratio (SUVmax ratio). The RC of OSEM images was not affected by changes in FOV, whereas the RC of BPL images decreased in small spheres, when FOV was 20 and 30 cm. The SUVmax ratio of the OSEM images was not affected by the difference in pixel size. However, the SUVmax ratio of BPL images degraded in the 1-mm pixel size; this influence was observed only when the FOV was changed.Conclusion: BPL images reconstructed using a small FOV might degrade the quantitative accuracy of small spheres.

Optimization of Ordered Subset Expectation Maximization Parameters for Image Reconstruction in Tc-99m Methoxyisobutylisonitrile Myocardial Perfusion SPECT and Comparison with Corresponding Filtered Back Projection-Reconstructed Images.

Purpose of the Study:To establish the most appropriate ordered subset expectation maximization (OSEM) parameters for image reconstruction in Tc-99m methoxyisobutylisonitrile (MIBI) myocardial perfusion SPECT (MPS) and comparison with corresponding filtered back projection (FBP)-reconstructed images.Methods:A total of 99 stress–rest MPS studies (47 normal and 52 abnormal) were retrospectively analyzed using 16 different combinations of iterations and subsets. Images were reconstructed both with and without postreconstruction Butterworth filter (cutoff frequency and order for stress: 0.4 and 10 and for rest: 0.52 and 5, respectively) for each combination. A total of 3168 images were evaluated qualitatively by two nuclear medicine physicians on a scoring scale of 1–4. Best visual quality image iteration–subset combination was determined for each patient both with and without Butterworth filter and was further compared with FBP-reconstructed image. The interobserver agreement was obtained using kappa statistics.Results:The best quality images were obtained using a combination of four iterations and six subsets for both with and without Butterworth filter. The value of kappa for interobserver agreement for OSEM images with Butterworth filter was 0.570 and for OSEM images without Butterworth filter was 0.857. On comparison, FBP images were better than OSEM-reconstructed images without Butterworth filter (P < 0.0001 calculated using Fisher's exact test) with substantial agreement (kappa = 0.628). However, OSEM-reconstructed images with Butterworth filter were better than FBP images and showed moderate agreement (kappa = 0.486).Conclusion:The most appropriate OSEM reconstruction parameter in Tc-99m MIBI MPS is 4-iteration and 6-subset combination. FBP-reconstructed images were better than the images reconstructed with OSEM without postreconstruction Butterworth filter. However, OSEM-reconstructed image with Butterworth filter was better than FBP images.

A GPU-accelerated fully 3D OSEM image reconstruction for a high-resolution small animal PET scanner using dual-ended readout detectors

In this work, a GPU-accelerated fully 3D ordered-subset expectation maximization (OSEM) image reconstruction with point spread function (PSF) modeling was developed for a small animal PET scanner with a long axial field of view (FOV). Dual-ended readout detectors that provided high depth of interaction (DOI) resolution were used for the small animal PET scanner to simultaneously achieve uniform high spatial resolution and high sensitivity. First, we developed a novel sinogram generation method, in which the dimension of the sinogram was determined first and then an event was assigned to a few neighboring sinogram elements by using weights that are inversely proportional to the distance from the measured line of response (LOR) to the LOR of the sinogram elements. System geometric symmetry, precomputation of LOR-driven ray-tracing and texture memory were applied to accelerate the GPU-based reconstruction. We developed a spatially variant PSF model where the PSF parameters were obtained by using point source images measured at 18 positions in the FOV and a spatial invariant PSF model where the PSF parameters were obtained by using only one image measured at the center FOV. The performance of the image reconstruction method was evaluated by using simulated phantom data as well as phantom and in-vivo mouse data acquired on the scanner. The results showed that the proposed reconstruction method provided better spatial resolution, a higher contrast recovery coefficient and lower noise than the OSEM reconstruction and was more than 1000 times faster than the CPU-based reconstruction. The spatially variant PSF model did not result in any spatial resolution improvement compared to the spatial invariant PSF model, and thus, the latter that is much easier to implement in image reconstruction and can be used in a small animal PET scanner using detectors with very high DOI resolution. A whole body 18F-FDG mouse image with high resolution and a high contrast to noise ratio was obtained by using the proposed reconstruction method.

Time-modified OSEM algorithm for more robust assessment of left ventricular dyssynchrony with phase analysis in ECG-gated myocardial perfusion SPECT

BackgroundIn ordered subsets expectation maximization (OSEM) reconstruction of electrocardiography (ECG)-gated myocardial perfusion single-photon emission computed tomography (SPECT), it is often assumed that the image acquisition time is constant for each projection angle and ECG bin. Due to heart rate variability (HRV), this assumption may lead to errors in quantification of left ventricular mechanical dyssynchrony with phase analysis. We hypothesize that a time-modified OSEM (TOSEM) algorithm provides more robust results.MethodsList-mode data of 44 patients were acquired with a dual-detector SPECT/CT system and binned to eight ECG bins. First, activity ratio (AR)—the ratio of total activity in the last OSEM-reconstructed ECG bin and first five ECG bins—was computed, as well as standard deviation SDR-R of the accepted R–R intervals; their association was evaluated with Pearson correlation analysis. Subsequently, patients whose AR was higher than 90% were selected, and their list-mode data were rebinned by omitting a part of the acquired counts to yield AR values of 90%, 80%, 70%, 60% and 50%. These data sets were reconstructed with OSEM and TOSEM algorithms, and phase analysis was performed. Reliability of both algorithms was assessed by computing concordance correlation coefficients (CCCs) between the 90% data and data corresponding to lower AR values. Finally, phase analysis results assessed from OSEM- and TOSEM-reconstructed images were compared.ResultsA strong negative correlation (r = -0.749) was found between SDR-R and AR. As AR decreased, phase analysis parameters obtained from OSEM images decreased significantly. On the contrary, reduction of AR had no significant effect on phase analysis parameters obtained from TOSEM images (CCC > 0.88). The magnitude of difference between OSEM and TOSEM results increased as AR decreased.ConclusionsTOSEM algorithm minimizes the HRV-related error and can be used to provide more robust phase analysis results.

Ordered subset expectation maximization algorithm for positron emission tomographic image reconstruction using belief kernels.

The aim of this study is to investigate the benefits of incorporating prior information in list mode, time-of-flight (TOF) positron emission tomography (PET) image reconstruction using the ordered subset expectation maximization (OSEM) algorithm. This investigation consists of an IEC phantom study and a patient study. For the image under reconstruction, the activity profile along a line of response is treated as a priori and is combined with the TOF measurement to define a belief kernel used for forward and backward projections during the OSEM image reconstruction. Activity profiles are smoothed and combined with the TOF kernels to control the adverse impact of noise, and different levels of smoothness are attempted. The standard TOF OSEM reconstruction is used as a baseline for comparison. Image quality is assessed using a combination of visual assessment and quantitative measurement including contrast recovery coefficients (CRC) and background variability. On the IEC phantom study, the reconstruction using belief kernels converges faster and the reconstructed images are more appealing. The CRCs for all sizes of regions of interest on images reconstructed with belief kernels are higher than those of the baseline. The background variability, measured as a coefficient of variation, is generally lower for the images reconstructed using belief kernels. Similar observations occur on the patient study. Particularly, the images reconstructed using belief kernels have better defined lesions, improved contrast, and reduced background noise. OSEM PET image reconstruction using belief kernels that combine the information from prior images and TOF measurements seems promising and worth further investigation.

The Influence of Minimal Misalignment on the Repeatability of PET Images Examined by the Repositioning of Point Sources.

We aimed to evaluate the influence of minimal misalignment of a hot spot on the repeatability of PET images using repositioning of point sources. Methods: Point sources with an inner diameter of 1 mm were made with 1 μL of 18F solution. Seven point sources were placed on the x-axis in the field of view. For fixed-position imaging, PET data were acquired for 10 min 5 times serially. For variable-position imaging, PET data were acquired for 10 min each with the point sources placed at 0, ±0.5, and ±1.0 mm in the x-axis direction. The data were reconstructed using ordered-subsets expectation maximization (OSEM) and OSEM plus point-spread function (PSF). The image matrix was 128 × 128, 200 × 200, 256 × 256, 400 × 400, and 512 × 512 pixels. The normalized maximum count (rMax), the coefficient of variance (CVmax), and the full width at half maximum were analyzed. Results: The hot spots on OSEM images far from the center became faint and broad, whereas those on OSEM+PSF images became small and dense. Although rMax was overestimated at the 5-cm position on OSEM images, rMax at other positions was overestimated on OSEM+PSF images with a matrix of at least 256 × 256. rMax showed a similar pattern in fixed- and variable-position images. CVmax in fixed-position OSEM images was less than 2%, irrespective of matrix size. In contrast, CVmax in variable-position images was higher than in fixed-position images. CVmax was higher for OSEM+PSF images than for OSEM images. The full width at half maximum increased at positions far from the center on OSEM images but was stable at all positions on OSEM+PSF images. Conclusion: The repeatability of the small hot spot was affected by the minimal misalignment, especially on OSEM+PSF images. Precise positioning is necessary if PET is to be used as a biomarker. Professionals should recognize that PSF correction worsens the repeatability of the small hot spot although improving the spatial resolution of PET images.

Time-of-Flight Information Improved the Detectability of Subcentimeter Spheres Using a Clinical PET/CT Scanner.

Recent advancements in clinical PET/CT scanners have improved the detectability of small lesions. However, the ideal reconstruction parameters for detecting small lesions have not yet been sufficiently clarified. The purpose of this study was to investigate the detectability of subcentimeter spheres using a clinical PET/CT scanner. Methods: We used a clinical PET/CT scanner to obtain the data of a National Electrical Manufacturers Association body phantom consisting of 6 small spheres (inner diameters, 4.0, 5.0, 6.2, 7.9, 10, and 37 mm) containing 18F solution. The background activity was 2.65 kBq/mL, and the sphere-to-background ratio was 8. The PET data obtained for 2 and 120 min were reconstructed using ordered-subsets expectation maximization (OSEM), OSEM + point-spread function (PSF), and OSEM + time-of-flight (TOF) with voxel sizes of 2.04 × 2.04 × 2.00 mm (2-mm voxels) and 4.07 × 4.07 × 3.99 mm (4-mm voxels). A gaussian filter was not used. The image quality was evaluated by visual assessment, as well as by physical assessment of the detectability index and recovery coefficients. Results: According to the visual assessment, the detectability of the spheres improved using TOF and a longer acquisition. Using the OSEM+TOF model, the smallest visually detected spheres were 5 mm in diameter with a 120-min acquisition and 6 mm in diameter with a 2-min acquisition. According to physical assessment, the detectability of spheres 10 mm or smaller using the OSEM+TOF image was superior to that using the OSEM image. In addition, the detectability of each hot sphere and recovery coefficient with 2-mm voxels was superior to that with 4-mm voxels. Although OSEM+PSF images showed less background noise, detectability and the recovery coefficient were not improved for spheres 8 mm or smaller. Conclusion: The TOF model with 2-mm voxels improved the detectability of subcentimeter hot spheres on a clinical PET/CT scanner.

Edge Artifacts in Point Spread Function-based PET Reconstruction in Relation to Object Size and Reconstruction Parameters.

Objective(s):We evaluated edge artifacts in relation to phantom diameter and reconstruction parameters in point spread function (PSF)-based positron emission tomography (PET) image reconstruction.Methods:PET data were acquired from an original cone-shaped phantom filled with 18F solution (21.9 kBq/mL) for 10 min using a Biograph mCT scanner. The images were reconstructed using the baseline ordered subsets expectation maximization (OSEM) algorithm and the OSEM with PSF correction model. The reconstruction parameters included a pixel size of 1.0, 2.0, or 3.0 mm, 1-12 iterations, 24 subsets, and a full width at half maximum (FWHM) of the post-filter Gaussian filter of 1.0, 2.0, or 3.0 mm. We compared both the maximum recovery coefficient (RCmax) and the mean recovery coefficient (RCmean) in the phantom at different diameters.Results:The OSEM images had no edge artifacts, but the OSEM with PSF images had a dense edge delineating the hot phantom at diameters 10 mm or more and a dense spot at the center at diameters of 8 mm or less. The dense edge was clearly observed on images with a small pixel size, a Gaussian filter with a small FWHM, and a high number of iterations. At a phantom diameter of 6-7 mm, the RCmax for the OSEM and OSEM with PSF images was 60% and 140%, respectively (pixel size: 1.0 mm; FWHM of the Gaussian filter: 2.0 mm; iterations: 2). The RCmean of the OSEM with PSF images did not exceed 100%.Conclusion:PSF-based image reconstruction resulted in edge artifacts, the degree of which depends on the pixel size, number of iterations, FWHM of the Gaussian filter, and object size.

The edge artifact in the point-spread function-based PET reconstruction at different sphere-to-background ratios of radioactivity.

The aim of this study was to quantitatively evaluate the edge artifacts in PET images reconstructed using the point-spread function (PSF) algorithm at different sphere-to-background ratios of radioactivity (SBRs). We used a NEMA IEC body phantom consisting of six spheres with 37, 28, 22, 17, 13 and 10 mm in inner diameter. The background was filled with (18)F solution with a radioactivity concentration of 2.65 kBq/mL. We prepared three sets of phantoms with SBRs of 16, 8, 4 and 2. The PET data were acquired for 20 min using a Biograph mCT scanner. The images were reconstructed with the baseline ordered subsets expectation maximization (OSEM) algorithm, and with the OSEM + PSF correction model (PSF). For the image reconstruction, the number of iterations ranged from one to 10. The phantom PET image analyses were performed by a visual assessment of the PET images and profiles, a contrast recovery coefficient (CRC), which is the ratio of SBR in the images to the true SBR, and the percent change in the maximum count between the OSEM and PSF images (Δ % counts). In the PSF images, the spheres with a diameter of 17 mm or larger were surrounded by a dense edge in comparison with the OSEM images. In the spheres with a diameter of 22 mm or smaller, an overshoot appeared in the center of the spheres as a sharp peak in the PSF images in low SBR. These edge artifacts were clearly observed in relation to the increase of the SBR. The overestimation of the CRC was observed in 13 mm spheres in the PSF images. In the spheres with a diameter of 17 mm or smaller, the Δ % counts increased with an increasing SBR. The Δ % counts increased to 91 % in the 10-mm sphere at the SBR of 16. The edge artifacts in the PET images reconstructed using the PSF algorithm increased with an increasing SBR. In the small spheres, the edge artifact was observed as a sharp peak at the center of spheres and could result in overestimation.

Image‐derived and arterial blood sampled input functions for quantitative PET imaging of the angiotensin II subtype 1 receptor in the kidney

The radioligand 11C-KR31173 has been introduced for positron emission tomography (PET) imaging of the angiotensin II subtype 1 receptor in the kidney in vivo. To study the biokinetics of 11C-KR31173 with a compartmental model, the input function is needed. Collection and analysis of arterial blood samples are the established approach to obtain the input function but they are not feasible in patients with renal diseases. The goal of this study was to develop a quantitative technique that can provide an accurate image-derived input function (ID-IF) to replace the conventional invasive arterial sampling and test the method in pigs with the goal of translation into human studies. The experimental animals were injected with [11C]KR31173 and scanned up to 90 min with dynamic PET. Arterial blood samples were collected for the artery derived input function (AD-IF) and used as a gold standard for ID-IF. Before PET, magnetic resonance angiography of the kidneys was obtained to provide the anatomical information required for derivation of the recovery coefficients in the abdominal aorta, a requirement for partial volume correction of the ID-IF. Different image reconstruction methods, filtered back projection (FBP) and ordered subset expectation maximization (OS-EM), were investigated for the best trade-off between bias and variance of the ID-IF. The effects of kidney uptakes on the quantitative accuracy of ID-IF were also studied. Biological variables such as red blood cell binding and radioligand metabolism were also taken into consideration. A single blood sample was used for calibration in the later phase of the input function. In the first 2 min after injection, the OS-EM based ID-IF was found to be biased, and the bias was found to be induced by the kidney uptake. No such bias was found with the FBP based image reconstruction method. However, the OS-EM based image reconstruction was found to reduce variance in the subsequent phase of the ID-IF. The combined use of FBP and OS-EM resulted in reduced bias and noise. After performing all the necessary corrections, the areas under the curves (AUCs) of the AD-IF were close to that of the AD-IF (average AUC ratio=1±0.08) during the early phase. When applied in a two-tissue-compartmental kinetic model, the average difference between the estimated model parameters from ID-IF and AD-IF was 10% which was within the error of the estimation method. The bias of radioligand concentration in the aorta from the OS-EM image reconstruction is significantly affected by radioligand uptake in the adjacent kidney and cannot be neglected for quantitative evaluation. With careful calibrations and corrections, the ID-IF derived from quantitative dynamic PET images can be used as the input function of the compartmental model to quantify the renal kinetics of 11C-KR31173 in experimental animals and the authors intend to evaluate this method in future human studies.

Local validation of the use of Evolution for Bone for bone SPECT imaging.

In order to locally validate the technique, a retrospective review of a cohort of randomly selected single-photon emission computed tomography (SPECT) bone scans reconstructed with ordered subsets expectation maximization (OSEM) and Evolution for Bone was undertaken. Thirty consecutive bone SPECT patient data sets (17 spine, nine pelvis, and four spine and pelvis) were chosen. Poisson resampling was used to simulate reduced count data at 50, 75, and 100% of the original number of counts. Evolution for Bone applied resolution recovery to the reduced count images. All images were compared with the original OSEM images, currently used as the standard for clinical use. A qualitative blinded assessment was made by two independent observers, who assessed for noise, contrast, and resolution. Both radiologists saw an improvement in resolution (P = 0.776), noise (P = 0.007), and image quality with all data sets, compared with images processed purely with OSEM and viewed in Volumetrix. However, they completely disagreed on contrast, as the two radiologists scored contrast differently; however, the results are understandable. Images with 50, 75, and 100% of the original counts viewed using Evolution for Bone have improved image quality compared with images processed purely with OSEM and viewed in Volumetrix. Evolution for Bone therefore has great potential in departments for reducing either patient doses, waiting lists, or both.