PET NEMA IQ Phantom dataset: image reconstruction settings for quantitative PET imaging.
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ABSTRACT:
SUBMITTER: Vosoughi H
PROVIDER: S-EPMC8237526 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
PET NEMA IQ Phantom dataset: image reconstruction settings for quantitative PET imaging.
Vosoughi Habibeh H Hajizadeh Mohsen M Emami Farshad F Momennezhad Mehdi M Geramifar Parham P
Data in brief 20210618
The data presented here provide information about the role of reconstruction parameters on Positron Emission Tomography (PET) image quantification. Multiple phantom measurements in four different Spheres to Background Ratio (SBR) were performed on Biograph 6 TruePoint TrueV PET/CT scanner. PET raw data were reconstructed with/without resolution recovery algorithm using six various iteration x subsets with five different Full-Width Half-Maximum (FWHM) values of Gaussian post-smoothing filter. The ...[more]
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Project description:PurposeMany neurological diseases affect small structures in the brain and, as such, reliable visual evaluation and accurate quantification are required. Recent technological developments made the clinical use of hybrid positron emission tomography/magnetic resonance (PET/MR) systems possible, providing both functional and anatomical information in a single imaging session. Nevertheless, there is a trade-off between spatial resolution and image quality (contrast and noise), which is dictated mainly by the chosen acquisition and reconstruction protocols. Image reconstruction algorithms using spherical symmetric basis functions (blobs) for image representation have a number of additional parameters that impact both the qualitative and quantitative image characteristics. Hence, a detailed investigation of the blob-based reconstruction characteristics using different parameters is needed to achieve optimal reconstruction results.ProceduresThis work evaluated the impact of a range of blob parameters on image quality and quantitative accuracy of brain PET images acquired on the Ingenuity Time-of-Flight (TOF) PET/MR system. Two different phantoms were used to simulate brain imaging applications. Image contrast and noise characteristics were assessed using an image quality phantom. Quantitative performance in a clinical setting was investigated using the Hoffman 3D brain phantom at various count levels. Furthermore, the visual quality of four clinical studies was scored blindly by two experienced physicians to qualitatively evaluate the influence of different reconstruction protocols, hereby providing indications on parameters producing the best image quality.ResultsQuantitative evaluation using the image quality phantom showed that larger basis function radii result in lower contrast recovery (∼2%) and lower variance levels (∼15%). The brain phantom and clinical studies confirmed these observations since lower contrast was seen between anatomical structures. High and low count statistics gave comparable values. The qualitative evaluation of the clinical studies, based on the assessment performed by the physicians, showed a preference towards lower image variance levels with a slightly lower contrast, favoring higher radii and four iterations.ConclusionThis study systematically evaluated a number of basis function parameters and their quantitative and qualitative effect within PET image reconstruction in the context of brain imaging. A range of blob parameters can minimize error and provided optimal image quality, where the anatomical structures could be recognized but the exact delineation of these structures is simplified in scans with lower image variance levels and thus, higher radii should be preferred. With the optimization of blob parameters, the reconstructed images were found to be qualitatively improved (optimum parameters {d = 2.0375, alpha = 10.4101, radius = 3.9451}) as assessed by the physicians compared to the current clinical protocol. However, this qualitative improvement is task specific, depending on the desired image characteristics to be extracted. Finally, this work could be used as a guide for application-specific optimal parameter selection.
| S-EPMC4768229 | biostudies-literature
Project description:Background18 F-fluoro-2-deoxy-D-Glucose positron emission tomography (18 F-FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of 18 F-FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g., object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method.MethodsThe NEMA image quality phantom was scanned with various sphere-to-background ratios (SBR), simulating different activity uptakes, including spheres with low uptake, that is, SBR smaller than 1. Furthermore, images of a phantom containing 3D printed inserts reflecting realistic heterogeneity uptake patterns were acquired. Data were reconstructed using various matrix sizes, reconstruction algorithms, and scan durations (noise). For every specific reconstruction and noise level, ten statistically equal replicates were generated. The phantom inserts were delineated using CT and PET-based segmentation methods. A total of 246 radiomic features was extracted from each image dataset. Images were discretized with a fixed number of 64 bins (FBN) and a fixed bin width (FBW) of 0.25 for the high and a FBW of 0.05 for the low uptake data. In terms of feature reduction, we determined the impact of these factors on the composition of feature clusters, which were defined on the basis of Spearman's correlation matrices. To assess feature repeatability, the intraclass correlation coefficient was calculated over the ten replicates.ResultsIn general, larger spheres with high uptake resulted in better repeatability compared to smaller low uptake spheres. In terms of repeatability, features extracted from heterogeneous phantom inserts were comparable to features extracted from bigger high uptake spheres. For example, for an EARL-compliant reconstruction, larger and smaller high uptake spheres yielded good repeatability for 32% and 30% of the features, while the heterogeneous inserts resulted in 34% repeatable features. For the low uptake spheres, this was the case for 22% and 20% of the features for bigger and smaller spheres, respectively. Images reconstructed with point-spread-function (PSF) resulted in the highest repeatability when compared with OSEM or time-of-flight, for example, 53%, 30%, and 32% of repeatable features, respectively (for unsmoothed data, discretized with FBN, 300 s scan duration). Reducing image noise (increasing scan duration and smoothing) and using CT-based segmentation for the low uptake spheres yielded improved repeatability. FBW discretization resulted in higher repeatability than FBN discretization, for example, 89% and 35% of the features, respectively (for the EARL-compliant reconstruction and larger high uptake spheres).ConclusionFeature space reduction and repeatability of 18 F-FDG PET radiomic features depended on all studied factors. The high sensitivity of PET radiomic features to image quality suggests that a high level of image acquisition and preprocessing standardization is required to be used as clinical imaging biomarker.
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Project description:Recently, deep neural networks have been widely and successfully applied in computer vision tasks and have attracted growing interest in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need for large amounts of prior training pairs, which is not always feasible in clinical practice. This is especially true for medical image reconstruction problems, where raw data are needed. Inspired by the deep image prior framework, in this paper, we proposed a personalized network training method where no prior training pairs are needed, but only the patient's own prior information. The network is updated during the iterative reconstruction process using the patient-specific prior information and measured data. We formulated the maximum-likelihood estimation as a constrained optimization problem and solved it using the alternating direction method of multipliers algorithm. Magnetic resonance imaging guided positron emission tomography reconstruction was employed as an example to demonstrate the effectiveness of the proposed framework. Quantification results based on simulation and real data show that the proposed reconstruction framework can outperform Gaussian post-smoothing and anatomically guided reconstructions using the kernel method or the neural-network penalty.
| S-EPMC6584077 | biostudies-literature
Project description:Background and purposeTo investigate the image quality and accurate bone mineral density (BMD) on quantitative CT (QCT) for osteoporosis screening by deep-learning image reconstruction (DLIR) based on a multi-phantom and patient study.Materials and methodsHigh-contrast spatial resolution, low-contrast detectability, modulation function test (MTF), noise power spectrum (NPS), and image noise were evaluated for physical image quality on Caphan 500 phantom. Three calcium hydroxyapatite (HA) inserts were used for accurate BMD measurement on European Spine Phantom (ESP). CT images were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction-veo 50% (ASiR-V50%), and three levels of DLIR(L/M/H). Subjective evaluation of the image high-contrast spatial resolution and low-contrast detectability were compared visually by qualified radiologists, whilst the statistical difference in the objective evaluation of the image high-contrast spatial resolution and low-contrast detectability, image noise, and relative measurement error were compared using one-way analysis of variance (ANOVA). Cohen's kappa coefficient (k) was performed to determine the interobserver agreement in qualitative evaluation between two radiologists.ResultsOverall, for three levels of DLIR, 50% MTF was about 4.50 (lp/cm), better than FBP (4.12 lp/cm) and ASiR-V50% (4.00 lp/cm); the 2 mm low-contrast object was clearly resolved at a 0.5% contrast level, while 3mm at FBP and ASiR-V50%. As the strength level decreased and radiation dose increased, DLIR at three levels showed a higher NPS peak frequency and lower noise level, leading to leftward and rightward shifts, respectively. Measured L1, L2, and L3 were slightly lower than that of nominal HA inserts (44.8, 95.9, 194.9 versus 50.2, 100.6, 199.2mg/cm3) with a relative measurement error of 9.84%, 4.08%, and 2.60%. Coefficients of variance for the L1, L2, and L3 HA inserts were 1.51%, 1.41%, and 1.18%. DLIR-M and DLIR-H scored significantly better than ASiR-V50% in image noise (4.83 ± 0.34, 4.50 ± 0.50 versus 4.17 ± 0.37), image contrast (4.67 ± 0.73, 4.50 ± 0.70 versus 3.80 ± 0.99), small structure visibility (4.83 ± 0.70, 4.17 ± 0.73 versus 3.83 ± 1.05), image sharpness (3.83 ± 1.12, 3.53 ± 0.90 versus 3.27 ± 1.16), and artifacts (3.83 ± 0.90, 3.42 ± 0.37 versus 3.10 ± 0.83). The CT value, image noise, contrast noise ratio, and image artifacts in DLIR-M and DLIR-H outperformed ASiR-V50% and FBP (P<0.001), whilst it showed no statistically significant between DLIR-L and ASiR-V50% (P>0.05). The prevalence of osteoporosis was 74 (24.67%) in women and 49 (11.79%) in men, whilst the osteoporotic vertebral fracture rate was 26 (8.67%) in women and (5.29%) in men.ConclusionImage quality with DLIR was high-qualified without affecting the accuracy of BMD measurement. It has a potential clinical utility in osteoporosis screening.
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Project description:BackgroundNew-generation silicon-photomultiplier (SiPM)-based PET/CT systems exhibit an improved lesion detectability and image quality due to a higher detector sensitivity. Consequently, the acquisition time can be reduced while maintaining diagnostic quality. The aim of this study was to determine the lowest 18F-FDG PET acquisition time without loss of diagnostic information and to optimise image reconstruction parameters (image reconstruction algorithm, number of iterations, voxel size, Gaussian filter) by phantom imaging. Moreover, patient data are evaluated to confirm the phantom results.MethodsThree phantoms were used: a soft-tissue tumour phantom, a bone-lung tumour phantom, and a resolution phantom. Phantom conditions (lesion sizes from 6.5 mm to 28.8 mm in diameter, lesion activity concentration of 15 kBq/mL, and signal-to-background ratio of 5:1) were derived from patient data. PET data were acquired on an SiPM-based Biograph Vision PET/CT system for 10 min in list-mode format and resampled into time frames from 30 to 300 s in 30-s increments to simulate different acquisition times. Different image reconstructions with varying iterations, voxel sizes, and Gaussian filters were probed. Contrast-to-noise-ratio (CNR), maximum, and peak signal were evaluated using the 10-min acquisition time image as reference. A threshold CNR value ≥ 5 and a maximum (peak) deviation of ± 20% were considered acceptable. 20 patient data sets were evaluated regarding lesion quantification as well as agreement and correlation between reduced and full acquisition time standard uptake values (assessed by Pearson correlation coefficient, intraclass correlation coefficient, Bland-Altman analyses, and Krippendorff's alpha).ResultsAn acquisition time of 60 s per bed position yielded acceptable detectability and quantification results for clinically relevant phantom lesions ≥ 9.7 mm in diameter using OSEM-TOF or OSEM-TOF+PSF image reconstruction, a 4-mm Gaussian filter, and a 1.65 × 1.65 x 2.00-mm3 or 3.30 × 3.30 x 3.00-mm3 voxel size. Correlation and agreement of patient lesion quantification between full and reduced acquisition times were excellent.ConclusionA threefold reduction in acquisition time is possible. Patients might benefit from more comfortable examinations or reduced radiation exposure, if instead of the acquisition time the applied activity is reduced.
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Project description:Positron emission tomography (PET) with 15O-tracers is commonly used to measure brain hemodynamic parameters such as cerebral blood flow, cerebral blood volume, and cerebral metabolic rate of oxygen. Conventionally, the absolute quantification of these parameters requires an arterial input function that is obtained invasively by sampling blood from an artery. In this work, we developed and validated an image-derived arterial input function technique that avoids the unreliable and burdensome arterial sampling procedure for full quantitative 15O-PET imaging. We then compared hemodynamic PET imaging performed on a PET/MR hybrid scanner against a conventional PET only scanner. We demonstrated the proposed imaging-based technique was able to generate brain hemodynamic parameter measurements in strong agreement with the traditional arterial sampling based approach. We also demonstrated that quantitative 15O-PET imaging can be successfully implemented on a PET/MR hybrid scanner.
| S-EPMC5453463 | biostudies-literature
Project description:Multi-tracer positron emission tomography (PET) has the potential to enhance PET imaging by providing complementary information from different physiological processes. However, one or more of the images may present high levels of noise. Guided image reconstruction methods transfer information from a guide image into the PET image reconstruction to encourage edge-preserving noise reduction. In this work we aim to reduce noise in poorer quality PET datasets via guidance from higher quality ones by using a weighted quadratic penalty approach. In particular, we applied this methodology to [18F]fluorodeoxyglucose (FDG) and [11C]methionine imaging of gliomas. 3D simulation studies showed that guiding the reconstruction of methionine datasets using pre-existing FDG images reduced reconstruction errors across the whole-brain (-8%) and within a tumour (-36%) compared to maximum likelihood expectation-maximisation (MLEM). Furthermore, guided reconstruction outperformed a comparable non-local means filter, indicating that regularising during reconstruction is preferable to post-reconstruction approaches. Hyperparameters selected from the 3D simulation study were applied to real data, where it was observed that the proposed FDG-guided methionine reconstruction allows for better edge preservation and noise reduction than standard MLEM. Overall, the results in this work demonstrate that transferring information between datasets in multi-tracer PET studies improves image quality and quantification performance.
| S-EPMC6130802 | biostudies-other
Project description:ObjectiveSimultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems.MethodThe NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.7:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 min listmode data acquisition and on 6 realizations of 5 min from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3-7 mm in steps of 1 mm was applied. Attenuation correction was provided from a scaled computed tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuation corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. A hybrid metric combining the root-mean-squared discrepancy (RMSD) and the absolute CRC values was used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting toward higher resolution reconstructions. The image reconstruction parameter set was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction.ResultsThe range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves was obtained at the lowest RMSD values with: for CRCmean, 2 iterations-7 mm filter on the GE Signa and 4 iterations-6 mm filter on the Siemens mMR, for CRCmax, 4 iterations-6 mm filter on the GE Signa, 4 iterations-5 mm filter on the Siemens mMR and for CRCpeak, 4 iterations-7 mm filter with PSF on the GE Signa and 4 iterations-7 mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs was 1.8%, 3.6% and 2.9% for CRC mean, max and peak, respectively. The solution of 2 iterations-3 mm on the GE Signa and 4 iterations-3 mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 2.8%, 5.8% and 3.2%, respectively.ConclusionsFor two commercially available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing and PSF modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.
| S-EPMC8571452 | biostudies-literature
Project description:Iterative reconstruction algorithms are becoming more commonly employed in positron emission tomography (PET) imaging; however, the quantitative accuracy of the reconstructed images still requires validation for various levels of contrast and counting statistics.The authors present an evaluation of the quantitative accuracy of the 3D maximum a posteriori (3D-MAP) image reconstruction algorithm for dynamic PET imaging with comparisons to two of the most widely used reconstruction algorithms: the 2D filtered-backprojection (2D-FBP) and 2D-ordered subsets expectation maximization (2D-OSEM) on the Siemens microPET scanners. The study was performed for various levels of count density encountered in typical dynamic scanning as well as the imaging of cardiac activity concentration in small animal studies on the Focus 120. Specially designed phantoms were used for evaluation of the spatial resolution, image quality, and quantitative accuracy. A normal mouse was employed to evaluate the accuracy of the blood time activity concentration extracted from left ventricle regions of interest (ROIs) within the images as compared to the actual blood activity concentration measured from arterial blood sampling.For MAP reconstructions, the spatial resolution and contrast have been found to reach a stable value after 20 iterations independent of the β values (i.e., hyper parameter which controls the weight of the penalty term) and count density within the frame. The spatial resolution obtained with 3D-MAP reaches values of ∼1.0 mm with a β of 0.01 while the 2D-FBP has value of 1.8 mm and 2D-OSEM has a value of 1.6 mm. It has been observed that the lower the hyper parameter β used in MAP, more iterations are needed to reach the stable noise level (i.e., image roughness). The spatial resolution is improved by using a lower β value at the expense of higher image noise. However, with similar noise level the spatial resolution achieved by 3D-MAP was observed to be better than that by 2D-FBP or 2D-OSEM. Using an image quality phantom containing hot spheres, the estimated activity concentration in the largest sphere has the expected concentration relative to the background area for all the MAP images. The obtained recovery coefficients have been also shown to be almost independent of the count density. 2D-FBP and 2D-OSEM do not perform as well, yielding recovery coefficients lower than those observed with 3D-MAP (approximately 33% lower for the smallest sphere). However, a small positive bias was observed in MAP reconstructed images for frames of very low count density. This bias is present in the uniform area for count density of less than 0.05 × 10(6) counts/ml. For the dynamic mouse study, it was observed that 3D-MAP (even gated at diastole) cannot predict accurately the blood activity concentration due to residual spill-over activity from the myocardium into the left ventricle (approximately 15%). However, 3D-MAP predicts blood activity concentration closer to blood sampling than 2D-FBP.The authors observed that 3D-MAP produces more accurate activity concentration estimates than 2D-FBP or 2D-OSEM at all practical levels of statistics and contrasts due to improved spatial resolution leading to lesser partial volume effect.
| S-EPMC3281972 | biostudies-other
Project description:BackgroundAccurate pharmacokinetic modelling in PET necessitates measurements of an input function, which ideally is acquired non-invasively from image data. For hepatic pharmacokinetic modelling two input functions need to be considered, to account for the blood supply from the hepatic artery and portal vein. Image-derived measurements at the portal vein are challenging due to its small size and image artifacts caused by respiratory motion. In this work we seek to demonstrate, using phantom experiments, how a dedicated PET/MR protocol can tackle these challenges and potentially provide input function measurements of the portal vein in a clinical setup.MethodsA custom 3D printed PET/MR phantom was constructed to mimic the liver and portal vein. PET/MR acquisitions were made with emulated respiratory motion. The PET/MR imaging protocol consisted of high-resolution anatomical MR imaging of the portal vein, followed by a PET acquisition in parallel to a dedicated motion-tracking MR sequence. Motion tracking and deformation information were extracted from PET data and subsequently used in PET reconstruction to produce dynamic series of motion-free PET images. Anatomical MR images were used post PET reconstruction for partial volume correction of the input function measurements.ResultsReconstruction of dynamic PET data with motion-compensation provided nearly motion-free series of PET frame data, suitable for image derived input function measurements of the portal vein. After partial volume correction, the individual input function measurements were within a 16.1% error range from the true activity in the portal vein compartment at the time of PET acquisition.ConclusionThe proposed protocol demonstrates clinically feasible PET/MR imaging of the liver for pharmacokinetic studies with accurate quantification of the portal vein input function, including correction for respiratory motion and partial volume effects.
| S-EPMC11532313 | biostudies-literature