Automated semi-quantitative amyloid PET analysis technique without MR images for Alzheimer\u27s disease.

Objective: Although beta-amyloid (Aβ) positron emission tomography (PET) images are interpreted visually as positive or negative, approximately 10% are judged as equivocal in Alzheimer\u27s 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 Aβ deposition in the cortex. However, the criterion "equivocal" was added for cases with focal or mild Aβ accumulation that were difficult to categorize. Optimal cutoff values of SUVr and CL maximizing sensitivity and specificity for Aβ 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 Aβ 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 Aβ burden in the brain as a supplemental tool for the visual reading of equivocal cases in routine clinical practice

Assesment of F-FDG PET/CT texture analysis to discriminate NSCLC from radiation pneumonitis after CIRT

Aim: The differentiation of local recurrence from a primary tumor and radiation pneumonitis (RP) is critically important for selecting optimal clinical therapeutic strategies to manage post carbon-ion radiotherapy (CIRT) in patients with non-small cell lung cancer (NSCLC). Although 18F-FDG PET/CT (FDG-PET/CT) plays a key role in the metabolic imaging of patients with NSCLC who require CIRT management, PET/CT diagnosis based on SUVmax cannot always distinguish between NSCLC and RP. The present study aimed to determine whether FDG-PET/CT texture parameters can differentiate NSCLC from RP after CIRT.Material and Methods: We retrospectively analyzed FDG-PET/CT image data from 32 patients with histopathologically proven NSCLC who were scheduled to undergo CIRT, and 31 patients who were diagnosed with RP after CIRT (50.0 Gy in 4 fractions/day). Radiation pneumonitis was diagnosed by biopsy or at clinical follow-up > 1 year after CIRT. Volumes of interest (VOI) on tumors were delineated using a threshold of 40% of the maximum standard uptake value (SUVmax) in each lesion. The SUV parameters of SUVmax, SUVpeak, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis (TLG) and seven typical texture parameters of FDG-PET/CT were determined using PETSTAT image-analysis software. Data were statistically compared between NSCLC and RP using nonparametric Wilcoxon rank sum tests. Diagnostic accuracy was assessed using ROC curves.Results: Among SUV parameters, MTV (p < 0.0001) and TLG (p = 0.001) significantly differed between NSCLC and RP. The feature quantities of texture parameters, namely, GLRLM, GLSZM, NGLCM3D, NGLCM and NGTDM significantly differed between NSCLC and RP. The areas under the receiver operating characteristics (ROC) curves (AUC) were as follows: SUVmax 0.64, MTV 0.86, TLG 0.75, GLRLM 0.83, GLSZM 0.76, NGLCM3D 0.71, NGLCM 0.72 and GTDM 0.82. Diagnostic accuracy was better using GLRLRM or NGTDM than SUVmax (p < 0.01). Conclusion: The texture parameters of FDG-PET/CT were useful to differentiate NSCLC from radiation pneumonitis after CIRT, and GLRLM and NGTDM in particular would be promising parameters with excellent diagnostic accuracy.EANM2019(ヨーロッパ核医学会

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

Abstract 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 β 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 β 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 β 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 β 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 β value and the γ factor in BPL enabled the detection of lesions ≤ 6.2 mm

Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by FDG-PET/CT metabolic parameters

Objectives: In management after carbon-ion radiotherapy (CIRT) in patients with non-small cell lung cancer (NSCLC, LC), differentiation of local recurrence or presence of the tumor and radiation pneumonitis (RP) is one of paramount importance for the following clinical therapeutic strategy. 18F-FDG PET/CT (FDG-PET/CT) is playing a key metabolic imaging in management of CIRT for LC patients, however PET/CT diagnosis using SUVmax parameter often encounters cases where it is difficult to distinguish between LC and RP. The aim of this study is to assess FDG-PET/CT metabolic parameters such as SUVpeak, MTV and TLG for differentiating LC and RP after CIRT.Methods: We retrospectively analyzed FDG-PET/CT image data of histopathologically proven 19 LC patients who were scheduled to undergo CIRT and 28 patients who were diagnosed having RP after CIRT (50.0 Gy / 1 fraction). RP was diagnosed by biopsy or by clinical follow-up more than 1 year after CIRT. Volumes of interest (VOI) on tumors were delineated using a threshold of 40% of the maximum standard uptake value (SUVmax) in each lesion. SUVmax, SUVpeak, MTV and TLG which were metabolic parameters of FDG-PET/CT were determined using an image-analysis software. Statistical analysis was performed between LC and RP using nonparametric Wilcoxon rank sum test. The diagnostic accuracy by ROC analysis was also assessed.Results: As a result of the test, SUVmax: LC 4.446 ± 0.552, RP 2.329 ± 0.255 (p <0.001), MTV: LC 10.59 ± 1.333, RP 46.94 ± 6.073 (p <0.0001), TLG: LC 30.66 ± 7.096, RP 63.35 ± 9.177 p <0.005), and significant differences were observed in all indices.The area under the ROC curve (area under curve, AUC) was SUVmax: 0.80, MTV: 0.96, TLG: 0.77, and the diagnostic accuracy of MTV was the highest among these FDG-PET/CT metabolic parameters.Conclusions: In differentiation between NSCLC and radiation pneumonitis after CIRT, FDG-PET/CT metabolic parameters were useful, and MTV in particular would be an appropriate parameter with high diagnostic accuracy.SNMMI 2019 Annual Meetin

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 F-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 F-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: SUV, 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 SUV (p < 0.01). The texture parameters of F-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 F-FDG PET/CT images would be appropriate parameters that can offer high diagnostic accuracy

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

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 (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using F-fluoro-2-deoxy-D-glucose ([ F]FDG) and C-labeled Pittsburg compound B ([ C]PiB)