Phantom and clinical evaluation of the Bayesian penalised likelihood reconstruction algorithm Q.Clear without PSF correction in amyloid PET images.

PURPOSE: Bayesian penalised likelihood (BPL) reconstruction, which incorporates point-spread-function (PSF) correction, provides higher signal-to-noise ratios and more accurate quantitation than conventional ordered subset expectation maximization (OSEM) reconstruction. However, applying PSF correction to brain PET imaging is controversial due to Gibbs artefacts that manifest as unpredicted cortical uptake enhancement. The present study aimed to validate whether BPL without PSF would be useful for amyloid PET imaging. METHODS: Images were acquired from Hoffman 3D brain and cylindrical phantoms for phantom study and 71 patients administered with [18F]flutemetamol in clinical study using a Discovery MI. All images were reconstructed using OSEM, BPL with PSF correction, and BPL without PSF correction. Count profile, %contrast, recovery coefficients (RCs), and image noise were calculated from the images acquired from the phantoms. Amyloid ß deposition in patients was visually assessed by two physicians and quantified based on the standardised uptake value ratio (SUVR). RESULTS: The overestimated radioactivity in profile curves was eliminated using BPL without PSF correction. The %contrast and image noise decreased with increasing ß values in phantom images. Image quality and RCs were better using BPL with, than without PSF correction or OSEM. An optimal ß value of 600 was determined for BPL without PSF correction. Visual evaluation almost agreed perfectly (κ = 0.91-0.97), without depending on reconstruction methods. Composite SUVRs did not significantly differ between reconstruction methods. CONCLUSION: Gibbs artefacts disappeared from phantom images using the BPL without PSF correction. Visual and quantitative evaluation of [18F]flutemetamol imaging was independent of the reconstruction method. The BPL without PSF correction could be the standard reconstruction method for amyloid PET imaging, despite being qualitatively inferior to BPL with PSF correction for [18F]flutemetamol amyloid PET imaging.

Deep Learning-Driven Estimation of Centiloid Scales from Amyloid PET Images with 11C-PiB and 18F-Labeled Tracers in Alzheimer's Disease.

BACKGROUND: Standard methods for deriving Centiloid scales from amyloid PET images are time-consuming and require considerable expert knowledge. We aimed to develop a deep learning method of automating Centiloid scale calculations from amyloid PET images with 11C-Pittsburgh Compound-B (PiB) tracer and assess its applicability to 18F-labeled tracers without retraining. METHODS: We trained models on 231 11C-PiB amyloid PET images using a 50-layer 3D ResNet architecture. The models predicted the Centiloid scale, and accuracy was assessed using mean absolute error (MAE), linear regression analysis, and Bland-Altman plots. RESULTS: The MAEs for Alzheimer's disease (AD) and young controls (YC) were 8.54 and 2.61, respectively, using 11C-PiB, and 8.66 and 3.56, respectively, using 18F-NAV4694. The MAEs for AD and YC were higher with 18F-florbetaben (39.8 and 7.13, respectively) and 18F-florbetapir (40.5 and 12.4, respectively), and the error rate was moderate for 18F-flutemetamol (21.3 and 4.03, respectively). Linear regression yielded a slope of 1.00, intercept of 1.26, and R2 of 0.956, with a mean bias of -1.31 in the Centiloid scale prediction. CONCLUSIONS: We propose a deep learning means of directly predicting the Centiloid scale from amyloid PET images in a native space. Transferring the model trained on 11C-PiB directly to 18F-NAV4694 without retraining was feasible.

Impact of [11C]methionine PET with Bayesian penalized likelihood reconstruction on glioma grades based on new WHO 2021 classification.

OBJECTIVE: The uptake of [11C]methionine in positron emission tomography (PET) imaging overlapped in earlier images of tumors. Bayesian penalized likelihood (BPL) reconstruction increases the quantitative values of tumors compared with conventional ordered subset-expectation maximization (OSEM). The present study aimed to grade glioma malignancy based on the new WHO 2021 classification using [11C]methionine PET images reconstructed using BPL. METHODS: We categorized 32 gliomas in 28 patients as grades 2/3 (n = 15) and 4 (n = 17) based on the WHO 2021 classification. All [11C]methionine images were reconstructed using OSEM + time-of-flight (TOF) and BPL + TOF (ß = 200). Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (T/Nmax) were measured at each lesion. RESULTS: The mean SUVmax was 4.65 and 4.93 in grade 2/3 and 6.38 and 7.11 in grade 4, and the mean T/Nmax was 7.08 and 7.22 in grade 2/3 and 9.30 and 10.19 in grade 4 for OSEM and BPL, respectively. The BPL significantly increased these values in grade 4 gliomas. The area under the receiver operator characteristic (ROC) curve (AUC) for SUVmax was the highest (0.792) using BPL. CONCLUSIONS: The BPL increased mean SUVmax and mean T/Nmax in lesions with higher contrast such as grade 4 glioma. The discrimination power between grades 2/3 and 4 in SUVmax was also increased using [11C]methionine PET images reconstructed with BPL.

Impact of irregular waveforms on data-driven respiratory gated PET/CT images processed using MotionFree algorithm.

OBJECTIVES: MotionFree® (AMF) is a data-driven respiratory gating (DDG) algorithm for image processing that has recently been introduced into clinical practice. The present study aimed to verify the accuracy of respiratory waveform and the effects of normal and irregular respiratory motions using AMF with the DDG algorithm. METHODS: We used a NEMA IEC body phantom comprising six spheres (37-, 28-, 22-, 17-, 13-, and 10 mm diameter) containing 18F. The sphere-to-background ratio was 4:1 (21.2 and 5.3 kBq/mL). We acquired PET/CT images from a stationary or moving phantom placed on a custom-designed motion platform. Respiratory motions were reproduced based on normal (sinusoidal or expiratory-paused waveforms) and irregular (changed amplitude or shifted baseline waveforms) movements. The "width" parameters in AMF were set at 10-60% and extracted data during the expiratory phases of each waveform. We verified the accuracy of the derived waveforms by comparing those input from the motion platform and output determined using AMF. Quantitative accuracy was evaluated as recovery coefficients (RCs), improvement rate, and %change that were calculated based on sphere diameter or width. We evaluated statistical differences in activity concentrations of each sphere between normal and irregular waveforms. RESULTS: Respiratory waveforms derived from AMF were almost identical to the input waveforms on the motion platform. Although the RCs in each sphere for expiratory-paused and ideal stationary waveforms were almost identical, RCs except the expiratory-paused waveform were lower than those for the stationary waveform. The improvement rate decreased more for the irregular, than the normal waveforms with AMF in smaller spheres. The %change was improved by decreasing the width of waveforms with a shifted baseline. Activity concentrations significantly differed between normal waveforms and those with a shifted baseline in spheres < 28 mm. CONCLUSIONS: The PET images using AMF with the DDG algorithm provided the precise waveform of respiratory motions and the improvement of quantitative accuracy in the four types of respiratory waveforms. The improvement rate was the most obvious in expiratory-paused waveforms, and the most subtle in those with a shifted baseline. Optimizing the width parameter in irregular waveform will benefit patients who breathe like the waveform with the shifted baseline.

Toward standardization of tau PET imaging corresponding to various tau PET tracers: a multicenter phantom study.

OBJECTIVE: Tau positron emission tomography (PET) imaging is a recently developed non-invasive tool that can detect the density and extension of tau neurofibrillary tangles. Tau PET tracers have been validated to harmonize and accelerate their development and implementation in clinical practice. Whereas standard protocols including injected dose, uptake time, and duration have been determined for tau PET tracers, reconstruction parameters have not been standardized. The present study conducted phantom experiments based on tau pathology to standardize quantitative tau PET imaging parameters and optimize reconstruction conditions of PET scanners at four Japanese sites according to the results of phantom experiments. METHODS: The activity of 4.0 and 2.0 kBq/mL for Hoffman 3D brain and cylindrical phantoms, respectively, was estimated from published studies of brain activity using [18F]flortaucipir, [18F]THK5351, and [18F]MK6240. We developed an original tau-specific volume of interest template for the brain based on pathophysiological tau distribution in the brain defined as Braak stages. We acquired brain and cylindrical phantom images using four PET scanners. Iteration numbers were determined as contrast and recover coefficients (RCs) in gray (GM) and white (WM) matter, and the magnitude of the Gaussian filter was determined from image noise. RESULTS: Contrast and RC converged at ≥ 4 iterations, the error rates of RC for GM and WM were < 15% and 1%, respectively, and noise was < 10% in Gaussian filters of 2-4 mm in images acquired using the four scanners. Optimizing the reconstruction conditions for phantom tau PET images acquired by each scanner improved contrast and image noise. CONCLUSIONS: The phantom activity was comprehensive for first- and second-generation tau PET tracers. The mid-range activity that we determined could be applied to later tau PET tracers. We propose an analytical tau-specific VOI template based on tau pathophysiological changes in patients with AD to standardize tau PET imaging. Phantom images reconstructed under the optimized conditions for tau PET imaging achieved excellent image quality and quantitative accuracy.

Impact of γ factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images.

BACKGROUND: The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function. The ß value that controls the behavior of RDP determines the global strength of noise suppression, whereas the γ factor in RDP controls the degree of edge preservation. The present study aimed to assess the effects of various γ factors in RDP on the ability to detect sub-centimeter lesions. METHODS: All PET data were acquired for 10 min using a Discovery MI PET/CT system (GE Healthcare). We used a NEMA IEC body phantom containing spheres with inner diameters of 10, 13, 17, 22, 28 and 37 mm and 4.0, 5.0, 6.2, 7.9, 10 and 13 mm. The target-to-background ratio of the phantom was 4:1, and the background activity concentration was 5.3 kBq/mL. We also evaluated cold spheres containing only non-radioactive water with the same background activity concentration. All images were reconstructed using BPL + time of flight (TOF). The ranges of ß values and γ factors in BPL were 50-600 and 2-20, respectively. We reconstructed PET images using the Duetto toolbox for MATLAB software. We calculated the % hot contrast recovery coefficient (CRChot) of each hot sphere, the cold CRC (CRCcold) of each cold sphere, the background variability (BV) and residual lung error (LE). We measured the full width at half maximum (FWHM) of the micro hollow hot spheres ≤ 13 mm to assess spatial resolution on the reconstructed PET images. RESULTS: The CRChot and CRCcold for different ß values and γ factors depended on the size of the small spheres. The CRChot, CRCcold and BV increased along with the γ factor. A 6.2-mm hot sphere was obvious in BPL as lower ß values and higher γ factors, whereas γ factors ≥ 10 resulted in images with increased background noise. The FWHM became smaller when the γ factor increased. CONCLUSION: High and low γ factors, respectively, preserved the edges of reconstructed PET images and promoted image smoothing. The BPL with a γ factor above the default value in Q.Clear (γ factor = 2) generated high-resolution PET images, although image noise slightly diverged. Optimizing the ß value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm.

Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer's disease.

OBJECTIVE: Although beta-amyloid (Aß) positron emission tomography (PET) images are interpreted visually as positive or negative, approximately 10% are judged as equivocal in Alzheimer's disease. Therefore, we aimed to develop an automated semi-quantitative analysis technique using 18F-flutemetamol PET images without anatomical images. METHODS: Overall, 136 cases of patients administered 18F-flutemetamol were enrolled. Of 136 cases, five PET images each with the highest and lowest values of standardized uptake value ratio (SUVr) of cerebral cortex-to-pons were used to create positive and negative templates. Using these templates, PET images of the remaining 126 cases were standardized, and SUVr images were produced with the pons as a reference region. The mean of SUVr values in the volume of interest delineated on the cerebral cortex was compared to those in the CortexID Suite (GE Healthcare). Furthermore, centiloid (CL) values were calculated for the 126 cases using data from the Centiloid Project ( http://www.gaain.org/centiloid-project ) and both templates. 18F-flutemetamol-PET was interpreted visually as positive/negative based on Aß deposition in the cortex. However, the criterion "equivocal" was added for cases with focal or mild Aß accumulation that were difficult to categorize. Optimal cutoff values of SUVr and CL maximizing sensitivity and specificity for Aß detection were determined by receiver operating characteristic (ROC) analysis using the visual evaluation as a standard of truth. RESULTS: SUVr calculated by our method and CortexID were highly correlated (R2 = 0.9657). The 126 PET images comprised 84 negative and 42 positive cases of Aß deposition by visual evaluation, of which 11 and 10 were classified as equivocal, respectively. ROC analyses determined the optimal cutoff values, sensitivity, and specificity for SUVr as 0.544, 89.3%, and 92.9%, respectively, and for CL as 12.400, 94.0%, and 92.9%, respectively. Both semi-quantitative analyses showed that 12 and 9 of the 21 equivocal cases were negative and positive, respectively, under the optimal cutoff values. CONCLUSIONS: This semi-quantitative analysis technique using 18F-flutemetamol-PET calculated SUVr and CL automatically without anatomical images. Moreover, it objectively and homogeneously interpreted positive or negative Aß burden in the brain as a supplemental tool for the visual reading of equivocal cases in routine clinical practice.

Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [18 F]FDG and [11 C]PiB.

PURPOSE: The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (ß) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal ß value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using 18 F-fluoro-2-deoxy-D-glucose ([18 F]FDG) and 11 C-labeled Pittsburg compound B ([11 C]PiB). METHODS: Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/computed tomography (CT) 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (ß = 20-1000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [18 F]FDG and [11 C]PiB PET to validate the findings from the phantom study. The ß values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal ß values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent). RESULTS: The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥55% and exceeded that of OSEM at ranges of ß = 20-450 and 20-600 for [18 F]FDG and [11 C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤15% and was below that in OSEM when ß = 150-1000 and 400-1000 for [18 F]FDG and [11 C]PiB, respectively. The phantom study restricted the ranges of ß values to 100-300 and 300-500 for [18 F]FDG and [11 C]PiB, respectively. The BPL scores for gray-white matter contrast and image noise, exceeded those of OSEM in [18 F]FDG and [11 C]PiB images regardless of ß values. Visual evaluation confirmed that the optimal ß values were 200 and 450 for [18 F]FDG and [11 C]PiB, respectively. CONCLUSIONS: The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative standardized uptake value ratios (SUVR) due to full convergence, more rigorous noise control, and edge preservation. The optimal ß values for [18 F]FDG and [11 C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal ß values in BPL for brain PET imaging.

Decreased imaging time of amyloid PET using [18F]florbetapir can maintain quantitative accuracy.

Shortening the amount of time required to acquire amyloid positron emission tomography (PET) brain images while maintaining the accuracy of quantitative evaluation would help to overcome motion artifacts associated with Alzheimer's disease patients. The present study aimed to validate the quantitative accuracy of [18F]florbetapir ([18F]FBP) imaging over a shorter acquisition duration. Forty participants were injected with [18F]FBP, and PET images were acquired for 50-55, 50-60, and 50-70 min after injection. Three physicians visually assessed the reprocessed [18F]FBP images using a binary scale to classify them as amyloid ß (Aß) negative or positive. A mean composite standard uptake value ratio (cSUVR) > 1.075 was defined as Aß-positive based on receiver operating characteristic curves. Inter-reader and inter-acquisition duration agreements with visual assessment were evaluated using Cohen's kappa (κ). Binary visual discrimination of 102 for the 120 [18F]FBP images, was consistent among the three readers. Sixteen, sixteen, and fourteen of the 40 [18F]FBP images acquired for 50-55, 50-60, and 50-70 min after injection, respectively, were deemed Aß-positive by visual assessment. The inter-rater agreement was high, and the inter-acquisition duration agreement was almost perfect. The cSUVR did not change significantly among the acquisition durations, and the acquisition duration did not affect the outcome of discrimination based on the cSUVR cutoff. A shorter acquisition duration changed the visual assessment outcomes. Stable quantitative values were derived from [18F]FBP images acquired within 5 min. cSUVR helped to improve the performance and confidence in the outcomes of visual assessment.

Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by 18F-FDG PET/CT texture analysis.

The differentiation of non-small cell lung cancer (NSCLC) and radiation pneumonitis (RP) is critically essential for selecting optimal clinical therapeutic strategies to manage post carbon-ion radiotherapy (CIRT) in patients with NSCLC. The aim of this study was to assess the ability of 18F-FDG PET/CT metabolic parameters and its textural image features to differentiate NSCLC from RP after CIRT to develop a differential diagnosis of malignancy and benign lesion. We retrospectively analyzed 18F-FDG PET/CT image data from 32 patients with histopathologically proven NSCLC who were scheduled to undergo CIRT and 31 patients diagnosed with RP after CIRT. The SUV parameters, metabolic tumor volume (MTV), total lesion glycolysis (TLG) as well as fifty-six texture parameters derived from seven matrices were determined using PETSTAT image-analysis software. Data were statistically compared between NSCLC and RP using Wilcoxon rank-sum tests. Diagnostic accuracy was assessed using receiver operating characteristics (ROC) curves. Several texture parameters significantly differed between NSCLC and RP (p < 0.05). The parameters that were high in areas under the ROC curves (AUC) were as follows: SUVmax, 0.64; GLRLM run percentage, 0.83 and NGTDM coarseness, 0.82. Diagnostic accuracy was improved using GLRLM run percentage or NGTDM coarseness compared with SUVmax (p < 0.01). The texture parameters of 18F-FDG uptake yielded excellent outcomes for differentiating NSCLC from radiation pneumonitis after CIRT, which outperformed SUV-based evaluation. In particular, GLRLM run percentage and NGTDM coarseness of 18F-FDG PET/CT images would be appropriate parameters that can offer high diagnostic accuracy.

Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) for 18F-NaF bone PET/CT images acquired over shorter durations using a custom-designed phantom.

BACKGROUND: The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for 18F-NaF PET/CT images and to determine the feasibility of 18F-NaF PET/CT image acquisition over shorter durations in clinical practice. METHODS: A custom-designed thoracic spine phantom consisting of several inserts, soft tissue, normal spine, and metastatic bone tumor, was scanned using a Discovery MI PET/CT scanner (GE Healthcare). The phantom allows optional adjustment of activity distribution, tumor size, and attenuation. We reconstructed PET images using OSEM + PSF + TOF (2 iterations, 17 subsets, and a 4-mm Gaussian filter), BPL + TOF (ß = 200 to 700), and scan durations of 30-120 s. Signal-to-noise ratios (SNR), contrast, and coefficients of variance (CV) as image quality indicators were calculated, whereas the quantitative measures were recovery coefficients (RC) and RC linearity over a range of activity. We retrospectively analyzed images from five persons without bone metastases (male, n = 1; female, n = 4), then standardized uptake values (SUV), CV, and SNR at the 4th, 5th, and 6th thoracic vertebra were calculated in BPL + TOF (ß = 400) images. RESULTS: The optimal reconstruction parameter of the BPL was ß = 400 when images were acquired at 120 s/bed. At 90 s/bed, the BPL with a ß value of 400 yielded 24% and 18% higher SNR and contrast, respectively, than OSEM (2 iterations; 120 s acquisitions). The BPL was superior to OSEM in terms of RC and the RC linearity over a range of activity, regardless of scan duration. The SUVmax were lower in BPL, than in OSEM. The CV and vertebral SNR in BPL were superior to those in OSEM. CONCLUSIONS: The optimal reconstruction parameters of 18F-NaF PET/CT images acquired over different durations were determined. The BPL can reduce PET acquisition to 90 s/bed in 18F-NaF PET/CT imaging. Our results suggest that BPL (ß = 400) on SiPM-based TOF PET/CT scanner maintained high image quality and quantitative accuracy even for shorter acquisition durations.

Detection of sub-centimeter lesions using digital TOF-PET/CT system combined with Bayesian penalized likelihood reconstruction algorithm.

OBJECTIVE: Many advances in PET/CT technology can potentially improve image quality and the ability to detect small lesions. A new digital TOF-PET/CT scanner based on silicon photomultipliers (SiPM) integrated with a Bayesian penalized likelihood (BPL) PET reconstruction algorithm (Q.Clear; GE Healthcare) has been introduced into clinical practice. The present study aimed to quantify the ability of a digital TOF-PET/CT scanner combined with BPL reconstruction to detect small lesions, and to determine the optimal penalization factor (ß) in BPL to accurately detect such lesions. METHODS: All PET data were acquired from a NEMA body phantom using a Discovery MI (DMI) PET/CT system (GE Healthcare). The phantom included six spheres with diameters of 4, 5, 6, 8, 10, and 13 mm, and contained a background activity level of 5.3 kBq/mL, with target-to-background ratios (TBR) of 4:1 and 8:1. Images were reconstructed using a baseline OSEM algorithm, with OSEM + PSF, OSEM + TOF, OSEM + PSF + TOF, and BPL + PSF + TOF (ß: 50-400). The matrix size was 192 × 192 and 384 × 384. Data acquired in 100-min list mode were re-binned into acquisition times ranging from 2 to 100 min. The quantitative accuracy and detectability of small hot spheres were evaluated by physical assessment of a recovery coefficient (RC) and a detectability index (DI), as well as visual assessment of PET images at each acquisition time. RESULTS: The RC and DI of sub-centimeter spheres were improved, because the digital TOF-PET/CT scanner has a larger TOF performance gain due to better timing resolution. The RC and DI were higher with BPL in sub-centimeter spheres, than with other OSEM-based types of reconstruction. The BPL for an 8-mm sphere overestimated uptake due to edge artifact overshoot induced by PSF modeling. The variability of RC and DI for acquisition times and TBR differed considerably according to ß values. The RC for ~ 8-mm spheres were > 1 at ß values between 50 and 100, but were close to 1 at ß value of 200. The visual scores for ß = 200 in BPL were maximal, whereas those for spheres that were ≥ 6 mm exceeded the criterion of 3. CONCLUSION: The BPL in the digital TOF-PET/CT scanner improved the quantitation and detectability of sub-centimeter spheres compared with OSEM-based reconstruction. Optimization of the ß value in BPL might allow the detection of lesions ≤ 6 mm, although detectability depended on the TBR of lesions. A ß value of 200 seemed optimal for detecting sub-centimeter lesions.