Impact of the Bayesian penalized likelihood algorithm (Q.Clear®) in comparison with the OSEM reconstruction on low contrast PET hypoxic images.
ABSTRACT: PURPOSE:To determine the impact of the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison to OSEM on hypoxia PET/CT images of NSCLC using 18F-MIZO and 18F-FAZA. MATERIALS AND METHODS:Images of low-contrasted (SBR = 3) micro-spheres of Jaszczak phantom were acquired. Twenty patients with lung neoplasia were included. Each patient benefitted from 18F-MISO and/or 18F-FAZA PET/CT exams, reconstructed with OSEM and BPL. Lesion was considered as hypoxic if the lesion SUVmax > 1.4. A blind evaluation of lesion detectability and image quality was performed on a set of 78 randomized BPL and OSEM images by 10 nuclear physicians. SUVmax, SUVmean, and hypoxic volumes using 3 thresholding approaches were measured and compared for each reconstruction. RESULTS:The phantom and patient datasets showed a significant increase of quantitative parameters using BPL compared to OSEM but had no impact on detectability. The optimal beta parameter determined by the phantom analysis was ?350. Regarding patient data, there was no clear trend of image quality improvement using BPL. There was no correlation between SUVmax increase with BPL and either SUV or hypoxic volume from the initial OSEM reconstruction. Hypoxic volume obtained by a SUV > 1.4 thresholding was not impacted by the BPL reconstruction parameter. CONCLUSION:BPL allows a significant increase in quantitative parameters and contrast without significantly improving the lesion detectability or image quality. The variation in hypoxic volume by BPL depends on the method used but SUV > 1.4 thresholding seems to be the more robust method, not impacted by the reconstruction method (BPL or OSEM). TRIAL REGISTRATION:ClinicalTrials.gov, NCT02490696. Registered 1 June 2015.
Project description:In contrast to ordered subset expectation maximization (OSEM), block sequential regularized expectation maximization (BSREM) positron emission tomography (PET) reconstruction algorithms can run until full convergence while controlling image quality and noise. Recent studies with BSREM and 18F-FDG PET reported higher signal-to-noise ratios and higher standardized uptake values (SUV). In this study, we investigate the optimal regularization parameter (?) for clinical 68Ga-PSMA PET/MR reconstructions in the pelvic region applying time-of-flight (TOF) BSREM in comparison to TOF OSEM. Two-minute emission data from the pelvic region of 25 patients who underwent 68Ga-PSMA PET/MR were retrospectively reconstructed. Reference OSEM reconstructions had 28 subsets and 2 iterations. BSREM reconstructions were performed with 15 ? values between 150 and 1200. Regions of interest (ROIs) were drawn around lesions and in uniform background. Background SUVmean (average) and SUVstd (standard deviation), and lesion SUVmax (average of 5 hottest voxels) were calculated. Differences were analyzed using the Wilcoxon matched pairs signed-rank test.A total of 40 lesions were identified in the pelvic region. Background noise (SUVstd) and lesions SUVmax decreased with increasing ?. Image reconstructions with ? values lower than 400 have higher (p?<?0.01) background noise, compared to the reference OSEM reconstructions, and are therefore less useful. Lesions with low activity on images reconstructed with ? values higher than 600 have a lower (p?<?0.05) SUVmax compared to the reference. These reconstructions are likely visually appealing due to the lower background noise, but the lower SUVmax could possibly render small low-uptake lesions invisible.In our study, we showed that PET images reconstructed with TOF BSREM in combination with the 68Ga-PSMA tracer result in lower background noise and higher SUVmax values in lesions compared to TOF OSEM. Our study indicates that a ? value between 400 and 550 might be the optimal compromise between high SUVmax and low background noise.
Project description:OBJECTIVES:Whole body [18F]-fluorodihydrotestosterone positron emission tomography ([18F]FDHT PET) imaging directly targets the androgen receptor and is a promising prognostic and predictive biomarker in metastatic castration-resistant cancer (mCRPC). To optimize [18F]FDHT PET-CT for diagnostic and response assessment purposes, we assessed how count statistics and reconstruction protocol affect its accuracy, repeatability, and lesion detectability. METHODS:Whole body [18F]FDHT PET-CT scans were acquired on an analogue PET-CT on two consecutive days in 14 mCRPC patients harbouring a total of 336 FDHT-avid lesions. Images were acquired at 45 min post-injection of 200 MBq [18F]FDHT at 3 min per bed position. List-mode PET data were split on a count-wise basis, yielding two statistically independent scans with each 50% of counts. Images were reconstructed according to current EANM Research Ltd. (EARL1, 4 mm voxel) and novel EARL2 guidelines (4 mm voxel + PSF). Per lesion, we measured SUVpeak, SUVmax, SUVmean, and contrast-to-noise ratio (CNR). SUV was normalized to dose per bodyweight as well as to the parent plasma input curve integral. Variability was assessed with repeatability coefficients (RCs). RESULTS:Count reduction increased liver coefficient of variation from 9.0 to 12.5% and from 10.8 to 13.2% for EARL1 and EARL2, respectively. SUVs of EARL2 images were 12.0-21.7% higher than EARL1. SUVs of 100% and 50% count data were highly correlated (R2 > 0.98; slope = 0.97-1.01; ICC = 0.99-1.00). Intrascan variability was volume-dependent, and count reduction resulted in higher intrascan variability for EARL2 than EARL1 images. Intrascan RCs were lowest for SUVmean (8.5-10.6%), intermediate for SUVpeak (12.0-16.0%), and highest for SUVmax (17.8-22.2%). Count reduction increased test-retest variance non-significantly (p > 0.05) for all SUV types and normalizations. For SUVpeak at 50% of counts, RCs remained < 30% when small lesions were excluded. Splitting data reduced CNR by median 4.6% (interquartile range 1.2-8.7%) and 4.6% (interquartile range 1.2-8.7%) for EARL1 and EARL2 images, respectively. CONCLUSIONS:Reducing [18F]FDHT PET acquisition time from 3 min to 1.5 per bed position resulted in a repeatability of SUVpeak (bodyweight) remaining ≤ 30%, which is generally acceptable for response monitoring purposes. However, EARL2 reconstruction was more affected, especially for SUVmax whose repeatability tended to exceed 30%. Lesion detectability was only slightly impaired by reducing acquisition time, which might not be clinically relevant in mCRPC.
Project description:Positron emission tomography (PET) imaging has a wide applicability in oncology, cardiology and neurology. However, a major drawback when imaging very active regions such as the bladder is the spill-in effect, leading to inaccurate quantification and obscured visualisation of nearby lesions. Therefore, this study aims at investigating and correcting for the spill-in effect from high-activity regions to the surroundings as a function of activity in the hot region, lesion size and location, system resolution and application of post-filtering using a recently proposed background correction technique. This study involves analytical simulations for the digital XCAT2 phantom and validation acquiring NEMA phantom and patient data with the GE Signa PET/MR scanner. Reconstructions were done using the ordered subset expectation maximisation (OSEM) algorithm. Dedicated point-spread function (OSEM+PSF) and a recently proposed background correction (OSEM+PSF+BC) were incorporated into the reconstruction for spill-in correction. The standardised uptake values (SUV) were compared for all reconstruction algorithms. The simulation study revealed that lesions within 15-20?mm from the hot region were predominantly affected by the spill-in effect, leading to an increased bias and impaired lesion visualisation within the region. For OSEM, lesion SUVmax converged to the true value at low bladder activity, but as activity increased, there was an overestimation as much as 19% for proximal lesions (distance around 15-20?mm from the bladder edge) and 2-4% for distant lesions (distance larger than 20?mm from the bladder edge). As bladder SUV increases, the % SUV change for proximal lesions is about 31% and 6% for SUVmax and SUVmean, respectively, showing that the spill-in effect is more evident for the SUVmax than the SUVmean. Also, the application of post-filtering resulted in up to 65% increment in the spill-in effect around the bladder edges. For proximal lesions, PSF has no major improvement over OSEM because of the spill-in effect, coupled with the blurring effect by post-filtering. Within two voxels around the bladder, the spill-in effect in OSEM is 42% (32%), while for OSEM+PSF, it is 31% (19%), with (and without) post-filtering, respectively. But with OSEM+PSF+BC, the spill-in contribution from the bladder was relatively low (below 5%, either with or without post-filtering). These results were further validated using the NEMA phantom and patient data for which OSEM+PSF+BC showed about 70-80% spill-in reduction around the bladder edges and increased contrast-to-noise ratio up to 36% compared to OSEM and OSEM+PSF reconstructions without post-filtering. The spill-in effect is dependent on the activity in the hot region, lesion size and location, as well as post-filtering; and this is more evident in SUVmax than SUVmean. However, the recently proposed background correction method facilitates stability in quantification and enhances the contrast in lesions with low uptake.
Project description:BACKGROUND:Recently, the block-sequential regularized expectation maximization (BSREM) reconstruction algorithm was commercially introduced (Q.Clear, GE Healthcare, Milwaukee, WI, USA). However, the combination of noise-penalizing factor (β), acquisition time, and administered activity for optimal image quality has not been established for 18F-fluorocholine (FCH). The aim was to compare image quality and diagnostic performance of different reconstruction protocols for patients with prostate cancer being examined with 18F-FCH on a silicon photomultiplier-based PET-CT. Thirteen patients were included, injected with 4 MBq/kg, and images were acquired after 1 h. Images were reconstructed with frame durations of 1.0, 1.5, and 2.0 min using β of 150, 200, 300, 400, 500, and 550. An ordered subset expectation maximization (OSEM) reconstruction with a frame duration of 2.0 min was used for comparison. Images were quantitatively analyzed regarding standardized uptake values (SUV) in metastatic lymph nodes, local background, and muscle to obtain contrast-to-noise ratios (CNR) as well as the noise level in muscle. Images were analyzed regarding image quality and number of metastatic lymph nodes by two nuclear medicine physicians. RESULTS:The highest median CNR was found for BSREM with a β of 300 and a frame duration of 2.0 min. The OSEM reconstruction had the lowest median CNR. Both the noise level and lesion SUVmax decreased with increasing β. For a frame duration of 1.5 min, the median quality score was highest for β 400-500, and for a frame duration of 2.0 min the score was highest for β 300-500. There was no statistically significant difference in the number of suspected lymph node metastases between the different image series for one of the physicians, and for the other physician the number of lymph nodes differed only for one combination of image series. CONCLUSIONS:To achieve acceptable image quality at 4 MBq/kg 18F-FCH, we propose using a β of 400-550 with a frame duration of 1.5 min. The lower β should be used if a high CNR is desired and the higher if a low noise level is important.
Project description:INTRODUCTION:11C-acetate (ACE)-PET/CT is used for staging of high-risk prostate cancer. PET data is reconstructed with iterative algorithms, such as VUEPointHD ViP (VPHD) and VUEPoint HD Sharp IR (SharpIR), the latter with additional resolution recovery. It is expected that the resolution recovery algorithm should render more accurate maximum and mean standardized uptake values (SUVmax and SUVmean) and functional tumor volumes (FTV) than the ordinary OSEM. Performing quantitative analysis, choice of volume-of-interest delineation algorithm (SUV threshold) may influence FTV. Optimizing PET acquisition time is justified if image quality and quantitation do not deteriorate. The aim of this study is to identify the optimal reconstruction algorithm, SUV threshold and acquisition time for ACE-PET/CT. METHODS:ACE-PET/CT data acquired with a General Electric Discovery 690 PET/CT from 16 consecutive high-risk prostate cancer patients was reconstructed with VPHD and SharpIR. Forty pelvic lymph nodes (LNs) and 14 prostate glands were delineated with 42% and estimated threshold. SUVmax, SUVmean, FTV and total lesion uptake were measured. Default acquisition time was four minutes per bed position. In a subset of lesions, acquisition times of one, two and four minutes were evaluated. Structural tumor volumes (STV) of the LNs were measured with CT for correlation with functional volumetric parameters. To validate SUV quantification under different conditions with SharpIR 42%, recovery coefficients (RCs) of SUVmean and FTV were calculated from a phantom with 18F-fluoro-deoxy-glucose (FDG)-filled volumes 0.1-9.2cm3 and signal-to-background (S/B) ratios 4.3-15.9. RESULTS:With SharpIR, SUVmax and SUVmean were higher and FTV lower compared with VPHD, regardless of threshold method, in both prostates and LNs. Total lesion uptake determined with both threshold methods was lower with SharpIR compared with VPHD with both threshold methods, except in subgroup analysis of prostate targets where estimated threshold returned higher values. Longer acquisition times returned higher FTV for both threshold methods, regardless of reconstruction algorithm. The FTV difference was most pronounced with one minute's acquisition per bed position, which also produced visually the highest noise. SUV parameters were unaffected by varying acquisition times. FTV with SharpIR 42% showed the best correspondence with STV. SharpIR 42% gave higher RCs of SUVmean and FTV with increasing phantom size and S/B-ratio, as expected. CONCLUSIONS:Delineation with SharpIR 42% seems to provide the most accurate combined information from SUVmax, SUVmean, FTV and total lesion uptake. Acquisition time may be shortened to two minutes per bed position with preserved image quality.
Project description:BACKGROUND:Q.Clear is a new Bayesian penalized-likelihood PET reconstruction algorithm. It has been documented that Q.Clear increases the SUVmax values of different malignant lesions. PURPOSE:SUVmax values are crucial for the interpretation of PET/CT images in patients with lymphoma, particularly when the early and final responses to treatment are evaluated. The aim of the study was to systematically analyse the impact of the use of Q.Clear on the interpretation of PET/CT in patients with lymphoma. METHODS:A total of 280 18F-FDG PET/CT scans in patients with lymphoma were performed for staging (sPET), for early treatment response (iPET), after the end of treatment (ePET) and when a relapse of lymphoma was suspected (rPET). Scans were separately reconstructed with two algorithms, Q.Clear and OSEM, and further compared. RESULTS:The stage of lymphoma was concordantly diagnosed in 69/70 patients with both algorithms on sPET. Discordant assessment of the Deauville score (p < 0.001) was found in 11 cases (15.7%) of 70 iPET scans and in 11 cases of 70 ePET scans. An upgrade from a negative to a positive scan by Q.Clear occurred in 3 cases (4.3%) of iPET scans and 7 cases (10.0%) of ePET scans. The results of all 70 rPET scans were concordant. The SUVmax values of the target lymphoma lesions measured with Q.Clear were higher than those measured with OSEM in 88.8% of scans. CONCLUSION:Although the Q.Clear algorithm may alter the interpretations of PET/CT in only a small proportion of patients, we recommend using standard OSEM reconstruction for the assessment of treatment response.
Project description:Q.Clear, a Bayesian penalized-likelihood reconstruction algorithm for PET, was recently introduced by GE Healthcare on their PET scanners to improve clinical image quality and quantification. In this work, we determined the optimum penalization factor (beta) for clinical use of Q.Clear and compared Q.Clear with standard PET reconstructions.A National Electrical Manufacturers Association image-quality phantom was scanned on a time-of-flight PET/CT scanner and reconstructed using ordered-subset expectation maximization (OSEM), OSEM with point-spread function (PSF) modeling, and the Q.Clear algorithm (which also includes PSF modeling). Q.Clear was investigated for ? (B) values of 100-1,000. Contrast recovery (CR) and background variability (BV) were measured from 3 repeated scans, reconstructed with the different algorithms. Fifteen oncology body (18)F-FDG PET/CT scans were reconstructed using OSEM, OSEM PSF, and Q.Clear using B values of 200, 300, 400, and 500. These were visually analyzed by 2 scorers and scored by rank against a panel of parameters (overall image quality; background liver, mediastinum, and marrow image quality; noise level; and lesion detectability).As ? is increased, the CR and BV decreases; Q.Clear generally gives a higher CR and lower BV than OSEM. For the smallest sphere reconstructed with Q.Clear B400, CR is 28.4% and BV 4.2%, with corresponding values for OSEM of 24.7% and 5.0%. For the largest hot sphere, Q.Clear B400 yields a CR of 75.2% and a BV of 3.8%, with corresponding values for OSEM of 64.4% and 4.0%. Scorer 1 and 2 ranked B400 as the preferred reconstruction in 13 of 15 (87%) and 10 of 15 (73%) cases. The least preferred reconstruction was OSEM PSF in all cases. In most cases, lesion detectability was highest ranked for B200, in 9 of 15 (67%) and 10 of 15 (73%), with OSEM PSF ranked lowest. Poor lesion detectability on OSEM PSF was seen in cases of mildly (18)F-FDG-avid mediastinal nodes in lung cancer and small liver metastases due to background noise. Conversely, OSEM PSF was ranked second highest for lesion detectability in most pulmonary nodule evaluation cases. The combined scores confirmed B400 to be the preferred reconstruction.Our phantom measurement results demonstrate improved CR and reduced BV when using Q.Clear instead of OSEM. A ? value of 400 is recommended for oncology body PET/CT using Q.Clear.
Project description:Hypoxia is associated with resistance to radiotherapy and chemotherapy in malignant gliomas, and it can be imaged by positron emission tomography with 18F-fluoromisonidazole (18F-FMISO). Previous results for patients with brain cancer imaged with 18F-FMISO at a single center before conventional chemoradiotherapy showed that tumor uptake via T/Bmax (tissue SUVmax/blood SUV) and hypoxic volume (HV) was associated with poor survival. However, in a multicenter clinical trial (ACRIN 6684), traditional uptake parameters were not found to be prognostically significant, but tumor SUVpeak did predict survival at 1 year. The present analysis considered both study cohorts to reconcile key differences and examine the potential utility of adding radiomic features as prognostic variables for outcome prediction on the combined cohort of 72 patients with brain cancer (30 University of Washington and 42 ACRIN 6684). We used both 18F-FMISO intensity metrics (T/Bmax, HV, SUV, SUVmax, SUVpeak) and assessed radiomic measures that determined first-order (histogram), second-order, and higher-order radiomic features of 18F-FMISO uptake distributions. A multivariate model was developed that included age, HV, and the intensity of 18F-FMISO uptake. HV and SUVpeak were both independent predictors of outcome for the combined data set (P < .001) and were also found significant in multivariate prognostic models (P < .002 and P < .001, respectively). Further model selection that included radiomic features showed the additional prognostic value for overall survival of specific higher order texture features, leading to an increase in relative risk prediction performance by a further 5%, when added to the multivariate clinical model..
Project description:Esophageal cancer is an aggressive disease with poor survival rates. A more patient-tailored approach based on predictive biomarkers could improve outcome. We aimed to predict radiotherapy (RT) response by imaging tumor hypoxia with 18F-FAZA PET/CT in an esophageal adenocarcinoma (EAC) mouse model. Additionally, we investigated the radiosensitizing effect of the hypoxia modifier nimorazole in vitro and in vivo.In vitro MTS cell proliferation assays (OACM5 1.C SC1, human EAC cell line) were performed under normoxic and hypoxic (<?1%) conditions: control (100 ?L PBS), nimorazole, irradiation (5, 10 or 20 Gy) with or without nimorazole. In vivo, subcutaneous xenografts were induced in nude mice (OACM5 1.C SC1). Treatment was given daily for 5 consecutive days: (A) control (600 ?l NaCl 0.9% intraperitoneally (IP)) (N?=?5, n?=?7), (B) RT (5 Gy/d) (N?=?11, n?=?20), (C) combination (nimorazole (200 mg/kg/d IP) 30 min before RT) (N?=?13, n?=?21). N?=?number of mice, n?=?number of tumors. 18F-FAZA PET/CT was performed before treatment and tumor to background (T/B) ratios were calculated. Relative tumor growth was calculated and tumor sections were examined histologically (hypoxia, proliferation).A T/B???3.59 on pre-treatment 18F-FAZA PET/CT was predictive for worse RT response (sensitivity 92.3%, specificity 71.4%). Radiation was less effective in hypoxic tumors (T/B???3.59) compared to normoxic tumors (T/B?<?3.59) (P?=?0.0025). In vitro, pre-treatment with nimorazole significantly decreased hypoxic radioresistance (P?<?0.01) while in vivo, nimorazole enhanced the efficacy of RT to suppress cancer cell proliferation in hypoxic tumor areas (Ki67, P?=?0.064), but did not affect macroscopic tumor growth.Tumor tissue hypoxia as measured with 18F-FAZA PET/CT is predictive for RT response in an EAC xenograft model. The radiosensitizing effect of nimorazole was questionable and requires further investigation.
Project description:This study investigated the spatial relationship of 2-deoxy-2-[18F]-fluoro-D-glucose positron emission tomography ([18F]FDG-PET) standardized uptake values (SUVs) and apparent diffusion coefficients (ADCs) derived from magnetic resonance (MR) diffusion imaging on a voxel level using simultaneously acquired PET/MR data. We performed an institutional retrospective analysis of patients with newly diagnosed cervical cancer who received a pre-treatment simultaneously acquired [18F]FDG-PET/MR. Voxel SUV and ADC values, and global tumor metrics including maximum SUV (SUVmax), mean ADC (ADCmean), and mean tumor-to-muscle ADC ratio (ADCT/M) were compared. The impacts of histology, grade, and tumor volume on the voxel SUV to ADC relationship were also evaluated. The potential prognostic value of the voxel SUV/ADC relationship was evaluated in an exploratory analysis using Kaplan-Meier/log-rank and univariate Cox analysis.Seventeen patients with PET/MR scans were identified. There was a significant inverse correlation between SUVmax and ADCmean, and SUVmax and ADCT/M. In the voxelwise analysis, squamous cell carcinomas (SCCAs) and poorly differentiated tumors showed a consistent significant inverse correlation between voxel SUV and ADC values; adenocarcinomas (AdenoCAs) and well/moderately differentiated tumors did not. The strength of the voxel SUV/ADC correlation varied with metabolic tumor volume (MTV). On log-rank analysis, the correlation between voxel SUV/ADC values was prognostic of disease-free survival (DFS).In this hypothesis-generating study, a consistent inverse correlation between voxel SUV and ADC values was seen in SCCAs and poorly differentiated tumors. On univariate statistical analysis, correlation between voxel SUV and ADC values was prognostic for DFS.