Raw PET Data Research Articles

The effect of reconstruction algorithms on semi-quantitative measurements in 18F-FDG PET/CT imaging.

This study was carried out to understand whether Q.Clear and ordered subset expectation maximization (OSEM), reconstruction algorithms used in fluorine-18-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) applications, and parameters such as time of flight (TOF) and point spread function (PSF) cause different results in semi-quantitative measurements. Raw PET data of 264 patients who were referred to 18F-FDG PET/CT imaging with the purpose of evaluation of known or suspicious malignant disease were reconstructed separately with Q.Clear (GE Healthcare), a BPL, an OSEM algorithm, PSF (SharpIR®) and TOF (VUE Point FX®) methods. Each patient's liver, mediastinal blood pool, metabolic tumor volume (MTV), total lesion glycolysis (TLG), and standardized uptake values (SUV) (SUVmax, SUVmean, and SUVpeak) of a total of 264 lesions selected from the patients were performed. β350+ToF yielded higher measurement results than all other variables for all of the lesion SUVmax, lesion SUVmean, L/AP SUVmax, and L/AP SUVmean parameters. OSEM+ToF and OSEM+TOF+PSF algorithms yielded higher mean and median SUVmax values for the reference structures (liver and mediastinum) and for lesions SUVmax and SUVmean values were statistically significantly lower than the β350+ToF method. The method with the lowest mean value for the L/Liver SUVmax variable was OSEM+ToF 4iter16ss (mean=1.76), while the method with the highest mean value was β350+ToF (mean=2.26). β350+ToF was the reconstruction method with the highest ratios for L/AP SUVmax and SUVmean for both lesions below and above 1 cm. β350+ToF algorithm had also statistically significantly higher results for these variables compared to all other parameters in malignant lesions. When comparing 18F-FDG PET/CT images, the use of different reconstruction algorithms may lead to misleading results, especially in the evaluation of response to treatment of malignancies.

Comparison of quantitative whole body PET parameters on [68Ga]Ga-PSMA-11 PET/CT using ordered Subset Expectation Maximization (OSEM) vs. bayesian penalized likelihood (BPL) reconstruction algorithms in men with metastatic castration-resistant prostate cancer

BackgroundPSMA PET/CT is a predictive and prognostic biomarker for determining response to [177Lu]Lu-PSMA-617 in patients with metastatic castration resistant prostate cancer (mCRPC). Thresholds defined to date may not be generalizable to newer image reconstruction algorithms. Bayesian penalized likelihood (BPL) reconstruction algorithm is a novel reconstruction algorithm that may improve contrast whilst preventing introduction of image noise. The aim of this study is to compare the quantitative parameters obtained using BPL and the Ordered Subset Expectation Maximization (OSEM) reconstruction algorithms.MethodsFifty consecutive patients with mCRPC who underwent [68Ga]Ga-PSMA-11 PET/CT using OSEM reconstruction to assess suitability for [177Lu]Lu-PSMA-617 therapy were selected. BPL algorithm was then used retrospectively to reconstruct the same PET raw data. Quantitative and volumetric measurements such as tumour standardised uptake value (SUV)max, SUVmean and Molecular Tumour Volume (MTV-PSMA) were calculated on both reconstruction methods. Results were compared (Bland-Altman, Pearson correlation coefficient) including subgroups with low and high-volume disease burdens (MTV-PSMA cut-off 40 mL).ResultsThe SUVmax and SUVmean were higher, and MTV-PSMA was lower in the BPL reconstructed images compared to the OSEM group, with a mean difference of 8.4 (17.5%), 0.7 (8.2%) and − 21.5 mL (-3.4%), respectively. There was a strong correlation between the calculated SUVmax, SUVmean, and MTV-PSMA values in the OSEM and BPL reconstructed images (Pearson r values of 0.98, 0.99, and 1.0, respectively). No patients were reclassified from low to high volume disease or vice versa when switching from OSEM to BPL reconstruction.Conclusions[68Ga]Ga-PSMA-11 PET/CT quantitative and volumetric parameters produced by BPL and OSEM reconstruction methods are strongly correlated. Differences are proportional and small for SUVmean, which is used as a predictive biomarker. Our study suggests that both reconstruction methods are acceptable without clinical impact on quantitative or volumetric findings. For longitudinal comparison, committing to the same reconstruction method would be preferred to ensure consistency.

Impact of 18F-FDG PET Intensity Normalization on Radiomic Features of Oropharyngeal Squamous Cell Carcinomas and Machine Learning-Generated Biomarkers.

We aimed to investigate the effects of 18F-FDG PET voxel intensity normalization on radiomic features of oropharyngeal squamous cell carcinoma (OPSCC) and machine learning-generated radiomic biomarkers. Methods: We extracted 1,037 18F-FDG PET radiomic features quantifying the shape, intensity, and texture of 430 OPSCC primary tumors. The reproducibility of individual features across 3 intensity-normalized images (body-weight SUV, reference tissue activity ratio to lentiform nucleus of brain and cerebellum) and the raw PET data was assessed using an intraclass correlation coefficient (ICC). We investigated the effects of intensity normalization on the features' utility in predicting the human papillomavirus (HPV) status of OPSCCs in univariate logistic regression, receiver-operating-characteristic analysis, and extreme-gradient-boosting (XGBoost) machine-learning classifiers. Results: Of 1,037 features, a high (ICC ≥ 0.90), medium (0.90 > ICC ≥ 0.75), and low (ICC < 0.75) degree of reproducibility across normalization methods was attained in 356 (34.3%), 608 (58.6%), and 73 (7%) features, respectively. In univariate analysis, features from the PET normalized to the lentiform nucleus had the strongest association with HPV status, with 865 of 1,037 (83.4%) significant features after multiple testing corrections and a median area under the receiver-operating-characteristic curve (AUC) of 0.65 (interquartile range, 0.62-0.68). Similar tendencies were observed in XGBoost models, with the lentiform nucleus-normalized model achieving the numerically highest average AUC of 0.72 (SD, 0.07) in the cross validation within the training cohort. The model generalized well to the validation cohorts, attaining an AUC of 0.73 (95% CI, 0.60-0.85) in independent validation and 0.76 (95% CI, 0.58-0.95) in external validation. The AUCs of the XGBoost models were not significantly different. Conclusion: Only one third of the features demonstrated a high degree of reproducibility across intensity-normalization techniques, making uniform normalization a prerequisite for interindividual comparability of radiomic markers. The choice of normalization technique may affect the radiomic features' predictive value with respect to HPV. Our results show trends that normalization to the lentiform nucleus may improve model performance, although more evidence is needed to draw a firm conclusion.

Image Quality and Quantitative PET Parameters of Low-Dose [18F]FDG PET in a Long Axial Field-of-View PET/CT Scanner.

PET/CT scanners with a long axial field-of-view (LAFOV) provide increased sensitivity, enabling the adjustment of imaging parameters by reducing the injected activity or shortening the acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectability, and the quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min scan time) were included in this analysis. The PET raw data was rebinned for shorter frame durations to simulate 5 min scans with lower activities in the high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n = 82) were assessed using a 5-point Likert scale. The coefficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Subjective image ratings were generally superior in UHS compared to the HS mode. At 0.5 MBq/kg, lesion detectability decreased to 95% (HS) and to 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7 ± 0.6) and 0.5 MBq/kg in UHS (5.5 ± 0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing the [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a LAFOV PET/CT provides diagnostic image quality without statistically significant changes in the uptake parameters. The UHS mode improves image quality, noise, and lesion detectability compared to the HS mode.

Head-to-head intra-individual comparison of total-body 2-[18F]FDG PET/CT and digital PET/CT in patients with malignant tumor: how sensitive could it be?

To comparatively evaluate the lesion-detecting ability of 2-[18F]FDG total-body PET/CT (TB PET/CT) and conventional digital PET/CT. This study enrolled 67 patients (median age, 65years; 24 female and 43 male patients) who underwent a TB PET/CT scan and a conventional digital PET/CT scan after a single 2-[18F]FDG injection (3.7MBq/kg). Raw PET data for TB PET/CT were acquired over the course of 5min, and images were reconstructed using data from the first 1, 2, 3, and 4min and the entire 5min (G1, G2, G3, G4, and G5, respectively). The conventional digital PET/CT scan acquired in 2-3min per bed (G0). Two nuclear medicine physicians independently assessed subjective image quality using a 5-point Likert scale and recorded the number of 2-[18F]FDG-avid lesions. A total of 241 lesions (69 primary lesions; 32 liver, lung, and peritoneum metastases; and 140 regional lymph nodes) among 67 patients with various types of cancer were analyzed. The subjective image quality score and SNR (signal-to-noise ratio) increased gradually from G1 to G5, and these values were significantly higher than the values at G0 (all p < 0.05). Compared to conventional PET/CT, G4 and G5 of TB PET/CT detected an additional 15 lesions (2 primary lesions; 5 liver, lung, and peritoneum lesions; and 8 lymph node metastases). TB PET/CT was more sensitive than conventional whole-body PET/CT in detecting small (4.3mm, maximum standardized uptake value (SUVmax) of 1.0) or low-uptake (tumor-to-liver ratio of 1.6, SUVmax of 4.1) lesions. This study explored the gain of the image quality and lesion detectability of TB PET/CT, compared to conventional PET/CT, and recommended the appropriate acquisition time for TB PET/CT in clinical practice with an ordinary 2-[18F] FDG dose. • TB PET/CT increases the effective sensitivity to approximately 40 times that of conventional PET scanners. • The subjective image quality score and signal-to-noise ratio of TB PET/CT from G1 to G5 were better than those of conventional PET/CT. • 2-[18F]FDG TB PET/CT with a 4-min acquisition time at a regular tracer dose detected an additional 15 lesions compared to conventional PET/CT.

PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition?

Background and objective: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the “PEg TRAnsfer Workflow recognition” (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value.Methods: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score.Results: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks).Conclusion: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.

An objective evaluation method for head motion estimation in PET—Motion corrected centroid-of-distribution

Head motion presents a continuing problem in brain PET studies. A wealth of motion correction (MC) algorithms had been proposed in the past, including both hardware-based methods and data-driven methods. However, in most real brain PET studies, in the absence of ground truth or gold standard of motion information, it is challenging to objectively evaluate MC quality. For MC evaluation, image-domain metrics, e.g., standardized uptake value (SUV) change before and after MC are commonly used, but this measure lacks objectivity because 1) other factors, e.g., attenuation correction, scatter correction and parameters used in the reconstruction, will confound MC effectiveness; 2) SUV only reflects final image quality, and it cannot precisely inform when an MC method performed well or poorly during the scan time period; 3) SUV is tracer-dependent and head motion may cause increases or decreases in SUV for different tracers, so evaluating MC effectiveness is complicated. Here, we present a new algorithm, i.e., motion corrected centroid-of-distribution (MCCOD) to perform objective quality control for measured or estimated rigid motion information. MCCOD is a three-dimensional surrogate trace of the center of tracer distribution after performing rigid MC using the existing motion information. MCCOD is used to inform whether the motion information is accurate, using the PET raw data only, i.e., without PET image reconstruction, where inaccurate motion information typically leads to abrupt changes in the MCCOD trace. MCCOD was validated using simulation studies and was tested on real studies acquired from both time-of-flight (TOF) and non-TOF scanners. A deep learning-based brain mask segmentation was implemented, which is shown to be necessary for non-TOF MCCOD generation. MCCOD is shown to be effective in detecting abrupt translation motion errors in slowly varying tracer distribution caused by the motion tracking hardware and can be used to compare different motion estimation methods as well as to improve existing motion information.

Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition

BackgroundPET/CT image quality is directly influenced by the F-18-FDG injected activity. The higher the injected activity, the less noise in the reconstructed images but the more radioactive staff exposition. A new FDA cleared software has been introduced to obtain clinical PET images, acquired at 25% of the count statistics considering US practices. Our aim is to determine the limits of a deep learning based denoising algorithm (SubtlePET) applied to statistically reduced PET raw data from 3 different last generation PET scanners in comparison to the regular acquisition in phantom and patients, considering the European guidelines for radiotracer injection activities. Images of low and high contrasted (SBR = 2 and 5) spheres of the IEC phantom and high contrast (SBR = 5) of micro-spheres of Jaszczak phantom were acquired on 3 different PET devices. 110 patients with different pathologies were included. The data was acquired in list-mode and retrospectively reconstructed with the regular acquisition count statistic (PET100), 50% reduction in counts (PET50) and 66% reduction in counts (PET33). These count reduced images were post-processed with SubtlePET to obtain PET50 + SP and PET33 + SP images. Patient image quality was scored by 2 senior nuclear physicians. Peak-signal-to-Noise and Structural similarity metrics were computed to compare the low count images to regular acquisition (PET100).ResultsSubtlePET reliably denoised the images and maintained the SUVmax values in PET50 + SP. SubtlePET enhanced images (PET33 + SP) had slightly increased noise compared to PET100 and could lead to a potential loss of information in terms of lesion detectability. Regarding the patient datasets, the PET100 and PET50 + SP were qualitatively comparable. The SubtlePET algorithm was able to correctly recover the SUVmax values of the lesions and maintain a noise level equivalent to full-time images.ConclusionBased on our results, SubtlePET is adapted in clinical practice for half-time or half-dose acquisitions based on European recommended injected dose of 3 MBq/kg without diagnostic confidence loss.

Diagnostic performance of total-body 18F-FDG PET/CT with fast 2-min acquisition for liver tumours: comparison with conventional PET/CT.

To comparatively evaluate the diagnostic performances of total-body 18F-fluorodeoxyglucose positron-emission tomography/computed tomography (18F-FDG PET/CT) with fast 2-min acquisition and conventional PET/CT in liver cancer patients. This study included 156 patients with liver tumours. Seventy-eight patients underwent total-body PET/CT. PET raw data were reconstructed using acquisition durations of 2min (G2) and 15min (G15). Another 78 patients with liver lesions (control patients) underwent conventional uMI780 PET/CT (G780). All patients were evaluated based on TNM staging. The maximum tumour standardized uptake value (tumour SUVmax), mean normal liver SUV (SUVmean), and tumour SUVmax-to-liver SUVmean ratio (TLR) were determined for all patients. G15 data were used as the reference in the lesion detectability analysis. The diagnostic performances of PET/CT in terms of visual parameters and of PET in terms of semi-quantitative parameters such as SUVmax and TLR were evaluated. Receiver operating characteristics (ROC) curve analysis of SUVmax and TLR at G2 was performed. Pathologic findings of surgical specimens served as the gold standard for all patients. The lesions found in G15 were also noted in G2; three lymph nodes were missed in G2. However, no significant difference was found in the TNM stage among G2, G15, and G780. For benign and malignant lesions, the liver SUVmean in G2 and G15 was higher than that in G780 (all P < 0.05). The tumour SUVmax and TLR in G2 were equivalent to those in G15 and G780 regardless of whether the lesions were benign or malignant. ROC curve analysis (SUVmax cutoff: 4.34, TLR cutoff: 1.34) demonstrated that G2 also had good sensitivity in detecting liver cancer. The diagnostic performance of total-body PET/CT in G2 was comparable to that in G15 among liver cancer patients. Further, the diagnostic efficiency of total-body PET/CT imaging with fast 2-min acquisition and conventional PET/CT was similar.

Feasibility of Acquisitions Using Total-Body PET/CT with an Ultra-Low 18F-FDG Activity

The present study aimed to evaluate the feasibility of ultra-low 18F-FDG activity in total-body PET/CT oncologic studies. Methods: Thirty patients with cancer were enrolled prospectively and underwent a total-body PET/CT scan 60 min after injection of an ultra-low 18F-FDG activity (0.37 MBq/kg). Of the 30 enrolled patients, 11 were diagnosed with colorectal cancer (CRC). PET raw data were acquired within 15 min and reconstructed using data from the first 1, 2, 4, 8, and 10 min and the entire 15 min (G1, G2, G4, G8, G10, and G15, respectively). Image quality was qualitatively assessed twice by 2 readers using a 5-point Likert scale. The Cohen κ-test was used to investigate the intra- and interreader agreement. The SUVmax of lesions; the SUVmax, SUVmean, and SD of the livers; the tumor-to-background ratio; and the signal-to-noise ratio were measured and compared. The acquisition time for a clinically acceptable image quality using an ultra-low-activity injection was determined. In a matched-pair study, 11 patients with CRC who received a full 18F-FDG activity (3.7 MBq/kg) with an acquisition time of 2 min were selected retrospectively by matching sex, height, weight, body mass index, glucose level, uptake time, and pathologic types with the 11 CRC subjects in the prospective study. Qualitative and quantitative analyses were performed and compared between the 11 patients with CRC in the ultra-low-activity group and their matched full-activity controls. Results: Qualitative analysis of image quality showed good intra- and interreader agreements (all κ > 0.7). All the images acquired for 8 min or longer scored over 3 (indicating clinical acceptability). There was no significant difference in tumor-to-background ratio and liver signal-to-noise ratio among all the images acquired for 8 min or longer. In the matched study, no significant difference was found in the image quality score and quantitative parameters between the ultra-low-activity group with an 8-min acquisition and the full-activity group with a 2-min acquisition. Conclusion: An ultra-low 18F-FDG activity with an 8-min acquisition in a total-body PET/CT study can achieve acceptable image quality equivalent to that in the full-activity group after a 2-min acquisition.

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

PET imaging is an important diagnostic tool for management of patients with cancer and other diseases. Medical decisions based on quantitative PET information could potentially benefit from the availability of tools for evaluation of associated uncertainties. Raw PET data can be viewed as a sample from an inhomogeneous Poisson process so there is the possibility to directly apply bootstrapping to raw projection-domain list-mode data. Unfortunately this is computationally impractical, particularly if data reconstruction is iterative or the acquisition protocol is dynamic. We develop a flexible statistical linear model analysis to be used with multi-frame PET image data to create valid bootstrap samples. The technique is illustrated using data from dynamic PET studies with fluoro-deoxyglucose (FDG) and fluoro-thymidine (FLT) in brain and breast cancer patients. As is often the case with dynamic PET studies, data have been archived without raw list-mode information. Using the bootstrapping technique maps of kinetic parameters and associated uncertainties are obtained. The quantitative performance of the approach is assessed by simulation. The proposed image-domain bootstrap is found to substantially match the projection-domain alternative. Analysis of results points to a close relation between relative uncertainty in voxel-level kinetic parameters and local reconstruction error. This is consistent with statistical theory.

Total-body 18F-FDG PET/CT scan in oncology patients: how fast could it be?

The aim of the study was to determine a faster PET acquisition protocol for a total-body PET/CT scanner by assessing the image quality that is equivalent to a conventional digital PET/CT scanner from both a phantom and a clinical perspective. A phantom study using a NEMA/IEC NU-2 body phantom was first performed in both a total-body PET/CT (uEXPLORER) and a routine digital PET/CT (uMI 780), with a hot sphere to background activity concentration ratio of 4:1. The contrast recovery coefficient (CRC), background variability (BV), and recovery coefficient (RC: RCmax and RCmean) were assessed in the uEXPLORER with different scanning durations and reconstruction protocols, which were compared to those acquired from the uMI 780 with clinical acquisition settings. The coefficient of variation (COV) of the uMI 780 with clinical settings was calculated and used as a threshold reference to determine the optimized scanning duration and reconstruction protocol for the uEXPLORER. The obtained protocol from the phantom study was subsequently tested and validated in 30 oncology patients. Images acquired from the uMI 780 with 2-3min per bed position were referred as G780 and served as the reference for comparison. All PET raw data from the uEXPLORER were reconstructed using the data-cutting technique to simulate a 30-s, 45-s, or 60-s acquisition duration, respectively. The iterations were 2 and 3 for the uEXPLORER, referred as G30s_3i, G45s_2i, G45s_3i, G60s_2i, and G60s_3i, respectively. A 5-point Likert scale was used in the qualitative analysis to assess the image quality. The image quality was also evaluated by the liver COV, the lesion target-to-background ratio (TBR), and the lesion signal-to-noise ratio (SNR). In the phantom study, CRC, BV, RCmax, and RCmean in the uEXPLORER with different scanning durations and reconstruction iterations were compared with those in the uMI 780 with clinical settings. A minor fluctuation was found among different scanning durations. COV of the uMI 780 with clinical settings was 11.6%, and a protocol with a 30-45-s scanning duration and 2 or 3 iterations for the uEXPLORER was found to provide an equivalent image quality as the uMI 780. An almost perfect agreement was shown with a kappa value of 0.875. The qualitative score of the G30s_3i in the uEXPLORER was inferior to the G780 reference (p = 0.001); however, the scores of other groups in the uEXPLORER with a 45-s and above acquisition time were higher than the G780 in the uMI 780. In quantitative analysis, the delay time between the two scans in the two orders was not significantly different. There was no significant difference of the liver COV between the G780 and G30s_3i (p = 0.162). A total of 33 lesions were analyzed in the clinical patient study. There was no significant difference in lesion TBR between the reference G780 and the G45s_2i obtained from the uEXPLORER (p = 0.072), while the latter showed a higher lesion SNR value compared to that in uMI 780 with clinical settings (p < 0.001). This study showed that a fast PET protocol with a 30-45-s acquisition time in the total-body uEXPLORER PET/CT can provide an equivalent image quality as the conventional digital uMI 780 PET/CT with longer clinical acquisition settings.

Ultra-low dose whole-body CT for attenuation correction in a dual tracer PET/CT protocol for multiple myeloma.

To investigate within phantoms the minimum CT dose allowed for accurate attenuation correction of PET data and to quantify the effective dose reduction when a CT for this purpose is incorporated in the clinical setting. The NEMA image quality phantom was scanned within a large parallelepiped container. Twenty-one different CT images were acquired to correct attenuation of PET raw data. Radiation dose and image quality were evaluated. Thirty-one patients with proven multiple myeloma who underwent a dual tracer PET/CT scan were retrospectively reviewed. 18F-fluorodeoxyglucose PET/CT included a diagnostic whole-body low dose CT (WBLDCT: 120kV-80mAs) and 11C-Methionine PET/CT included a whole-body ultra-low dose CT (WBULDCT) for attenuation correction (100kV-40mAs). Effective dose and image quality were analysed. Only the two lowest radiation dose conditions (80kV-20mAs and 80kV-10mAs) produced artifacts in CT images that degraded corrected PET images. For all the other conditions (CTDIvol≥0.43mGy), PET contrast recovery coefficients varied less than±1.2%. Patients received a median dose of 6.4mSv from diagnostic CT and 2.1mSv from the attenuation correction CT. Despite the worse image quality of this CT, 94.8% of bone lesions were identifiable. Phantom experiments showed that an ultra-low dose CT can be implemented in PET/CT procedures without any noticeable degradation in the attenuation corrected PET scan. The replacement of the standard CT for this ultra-low dose CT in clinical PET/CT scans involves a significant radiation dose reduction.

Respiratory Gating and the Performance of PET/CT in Pulmonary Lesions.

BackgroundMotion artifacts related to the patient’s breathing can be the cause of underestimation of the lesion uptake and can lead to missing of small lung lesions. The respiratory gating (RG) technology has demonstrated a significant increase in image quality.ObjectiveThe aim of this paper was to evaluate the advantages of RG technique on PET/CT performance in lung lesions. The impact of 4D-PET/CT on diagnosis (metabolic characterization), staging and re-staging lung cancer was also assessed, including its application for radiotherapy planning. Finally, new technologies for respiratory motion management were also discussed.MethodsA comprehensive electronic search of the literature was performed by using Medline database (PubMed) searching “PET/CT”, “gated” and “lung”. Original articles, review articles, and editorials published in the last 10 years were selected, included and critically reviewed in order to select relevant articles.ResultsMany papers compared Standardized Uptake Value (SUV) in gated and ungated PET studies showing an increase in SUV of gated images, particularly for the small lesions located in medium and lower lung. In addition, other features as Metabolic Tumor Volume (MTV), Total Lesion Glycolysis (TLG) and textural-features presented differences when obtained from gated and ungated PET acquisitions. Besides the increase in quantification, gating techniques can determine an increase in the diagnostic accuracy of PET/CT. Gated PET/CT was evaluated for lung cancer staging, therapy response assessment and for radiation therapy planning.ConclusionNew technologies able to track the motion of organs lesion directly from raw PET data, can reduce or definitively solve problems (i.e.: extended acquisition time, radiation exposure) currently limiting the use of gated PET/CT in clinical routine.

Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data.

AimAttenuation correction using zero-echo time (ZTE) – magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer’s disease (AD) by performing simulation study.MethodsWe recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient’s specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference.ResultsPositron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (−0.0796 ± 0.0938 vs. −0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R2: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8–84.5%), respectively).ConclusionFor FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.

A novel supervised learning method to generate CT images for attenuation correction in delayed pet scans

Background and objectivesAttenuation correction is important for PET image reconstruction. In clinical PET/CT scans, the attenuation information is usually obtained by CT. However, additional CT scans for delayed PET imaging may increase the risk of cancer. In this paper, we propose a novel CT generation method for attenuation correction in delayed PET imaging that requires no additional CT scans. MethodsAs only PET raw data is available for the delayed PET scan, routine image registration methods are difficult to use directly. To solve this problem, a reconstruction network is developed to produce pseudo PET images from raw data first. Then a second network is used to generate the CT image through mapping PET/CT images from the first scan to the delayed scan. The inputs of the second network are the two pseudo PET images from the first and delayed scans, and the CT image from the first scan. The labels are taken from the ground truth CT image in the delayed scan. The loss function contains an image similarity term and a regularization term, which reflect the anatomy matching accuracy and the smoothness of the non-rigid deformation field, respectively. ResultsWe evaluated the proposed method with simulated and clinical PET/CT datasets. Standard Uptake Value was computed and compared with the gold standard (with coregistered CT for attenuation correction). The results show that the proposed supervised learning method can generate PET images with high quality and quantitative accuracy. For the test cases in our study, the average MAE and RMSE of the proposed supervised learning method were 4.61 and 22.75 respectively, and the average PSNR between the reconstructed PET image and the ground truth PET image was 62.13 dB. ConclusionsThe proposed method is able to generate accurate CT images for attenuation correction in delayed PET scans. Experiments indicate that the proposed method outperforms traditional methods with respect to quantitative PET image accuracy.

Approximating anatomically-guided PET reconstruction in image space using a convolutional neural network

In the last two decades, it has been shown that anatomically-guided PET reconstruction can lead to improved bias-noise characteristics in brain PET imaging. However, despite promising results in simulations and first studies, anatomically-guided PET reconstructions are not yet available for use in routine clinical because of several reasons. In light of this, we investigate whether the improvements of anatomically-guided PET reconstruction methods can be achieved entirely in the image domain with a convolutional neural network (CNN). An entirely image-based CNN post-reconstruction approach has the advantage that no access to PET raw data is needed and, moreover, that the prediction times of trained CNNs are extremely fast on state of the art GPUs which will substantially facilitate the evaluation, fine-tuning and application of anatomically-guided PET reconstruction in real-world clinical settings.In this work, we demonstrate that anatomically-guided PET reconstruction using the asymmetric Bowsher prior can be well-approximated by a purely shift-invariant convolutional neural network in image space allowing the generation of anatomically-guided PET images in almost real-time. We show that by applying dedicated data augmentation techniques in the training phase, in which 16 [18F]FDG and 10 [18F]PE2I data sets were used, lead to a CNN that is robust against the used PET tracer, the noise level of the input PET images and the input MRI contrast. A detailed analysis of our CNN in 36 [18F]FDG, 18 [18F]PE2I, and 7 [18F]FET test data sets demonstrates that the image quality of our trained CNN is very close to the one of the target reconstructions in terms of regional mean recovery and regional structural similarity.

Assessment of Myocardial Viability Using Nuclear Medicine Imaging in Dextrocardia.

Imaging of dextrocardia in humans requires an understanding of the orientation of the heart chambers and walls. There are many types of cardiac malpositioning, such as dextrocardia (with or without situs inversus), mesocardia, and levocardia. Myocardial perfusion scintigraphy of dextrocardia has been explained in case reports and imaging atlases; however, myocardial viability assessment using nuclear medicine imaging techniques is less documented in the literature. Methods: In 2 cases of dextrocardia with situs inversus and 1 case of mesocardia, myocardial viability was assessed using 99mTc-sestamibi rest perfusion scintigraphy and 18F-FDG PET. Cardiac SPECT images of dextrocardia with situs inversus were acquired using the feet-first supine position with a 180° arc from left anterior oblique to right posterior oblique, whereas a right-lateral-to-left-lateral arc was used for mesocardia. The processing and reconstruction were done by entering the dataset for the feet-first supine position and repeating after entering the dataset for the feet-first prone position. The 2 sets of reconstructed images were compared for orientation of walls and cardiac chambers. Results: The first processing, using the feet-first supine position, revealed an interchanged septum and lateral wall in reconstructed images of dextrocardia with situs inversus. This interchange was corrected by changing the position to prone during processing of the rest perfusion and PET raw data. The display of cardiac slices in various axes matched the conventional nomenclature for the septum and lateral wall, leading to easy interpretation. However, this change was not required in the mesocardia, for which the location of the heart chambers was not interchanged. Conclusion: Because the acquisition protocol for SPECT is a semicircular orbit, the various types of dextrocardia require careful selection of the arc, with the patient positioning kept feet-first supine. Processing and reconstruction of data by changing the patient position to prone was found to be most useful method of matching the septum and lateral wall orientation for interpretation of images.

Supervised learning with cyclegan for low-dose FDG PET image denoising.

PET imaging involves radiotracer injections, raising concerns about the risk of radiation exposure. To minimize the potential risk, one way is to reduce the injected tracer. However, this will lead to poor image quality with conventional image reconstruction and processing. In this paper, we proposed a supervised deep learning model, CycleWGANs, to boost low-dose PET image quality. Validations were performed on a low dose dataset simulated from a real dataset with biopsy-proven primary lung cancer or suspicious radiological abnormalities. Low dose PET images were reconstructed on reduced PET raw data by randomly discarding events in the PET list mode data towards the count level of 1 million. Traditional image denoising methods (Non-Local Mean (NLM) and block-matching 3D(BM3D)) and two recently-published deep learning methods (RED-CNN and 3D-cGAN) were included for comparisons. At the count level of 1 million (true counts), the proposed model can accurately estimate full-dose PET image from low-dose input image, which is superior to the other four methods in terms of the mean and maximum standardized uptake value (SUVmean and SUVmax) bias for lesions and normal tissues. The bias of SUV (SUVmean, SUVmax) for lesions and normal tissues are (-2.06±3.50%,-0.84±6.94%) and (-0.45±5.59%, N/A) in the estimated PET images, respectively. However, the RED-CNN achieved the best score in traditional metrics, such as structure similarity (SSIM), peak signal to noise ratio (PSNR) and normalized root mean square error (NRMSE). Correlation and profile analyses have successfully explained this phenomenon and further suggested that our method could effectively preserve edge and also SUV values than RED-CNN, 3D-cGAN and NLM with a slightly higher noise.

The Effect of Various β Values on Image Quality and Semiquantitative Measurements in 68Ga-RM2 and 68Ga-PSMA-11 PET/MRI Images Reconstructed With a Block Sequential Regularized Expectation Maximization Algorithm.

To compare the block sequential regularized expectation maximization (BSREM) algorithm with the ordered subsets expectation maximization (OSEM) algorithm and to evaluate how different penalty factors (b values) influence image quality and SUV measurements. We analyzed data from 78 prostate cancer patients who underwent Ga-RM2 (n = 42) or Ga-prostate-specific membrane antigen (PSMA)-11 (n = 36) PET/MRI. The raw PET data were retrospectively reconstructed using both time-of-flight (TOF)-BSREM with b values of 250, 350, 500, 750, and 1000 and TOF-OSEM. Each reconstruction was reviewed independently by 3 nuclear medicine physicians and scored qualitatively using a Likert scale (1 = poor, 5 = excellent quality). SUV measurements were analyzed as well. Fifty-seven lesions were detected (21 on Ga-RM2 and 36 on Ga-PSMA-11 PET/MRI); SUVmax decreased with the increase of β values for both tracers. Background noise (SUVsd) decreased with increasing of β values for both tracers. The mean ± SD scores for Ga-RM2 PET images were 2.4 ± 0.5 for b = 250 reconstructions, 3.2 ± 0.6 for b = 350, 4 ± 0.6 for b = 500, 4.5 ± 0.5 for b = 750, 4.4 ± 0.7 for b = 1000, and 3.4 ± 0.6 for TOF-OSEM. The mean ± SD scores for Ga-PSMA-11 PET images were 3.2 ± 0.8 for b = 250 reconstructions, 4.1 ± 0.8 for b = 350, 4.7 ± 0.6 for b = 500, 4.8 ± 0.4 for b = 750, 4.7 ± 0.6 for b = 1000, and 3.8 ± 0.5 for TOF-OSEM. Time-of-flight-BSREM algorithm improves image quality. Different b values should be used for different Ga-labeled radiopharmaceuticals such as those targeting GRPR and PSMA receptors. Once selected, the same b value should be consistently used because SUVmax measurements differ with different b values.