Standard Positron Emission Tomography Research Articles

Brain dopamine responses to ultra-processed milkshakes are highly variable and not significantly related to adiposity in humans.

Ultra-processed foods high in fat and sugar may be addictive, in part, due to their purported ability to induce an exaggerated postingestive brain dopamine response akin to drugs of abuse. Using standard [ 11 C]raclopride positron emission tomography (PET) displacement methods used to measure brain dopamine responses to addictive drugs, we measured postingestive striatal dopamine responses to an ultra-processed milkshake high in fat and sugar in 50 young, healthy adults over a wide body mass index range (BMI 20-45 kg/m 2 ). Surprisingly, milkshake consumption did not result in significant postingestive dopamine response in the striatum ( p =0.62) nor any striatal subregion (p>0.33) and the highly variable interindividual responses were not significantly related to adiposity (BMI: r =0.076, p =0.51; %body fat: r =0.16, p =0.28). Thus, postingestive striatal dopamine responses to an ultra-processed milkshake were likely substantially smaller than many addictive drugs and below the limits of detection using standard PET methods. ClinicalTrials.gov Identifier: NCT03648892.

Combining PET and Compton imaging with edge-on CZT detectors for enhanced diagnostic capabilities

The key metrics for positron emission tomography (PET) imaging devices include the capability to capture the maximum available amount of annihilation photon information while generating high-quality images of the radiation distribution. This capability carries clinical implications by reducing scanning time for imaging, thus reducing radiation exposure for patients. However, imaging quality is degraded by positron range effects and the non-collinearity of positron annihilation photons. Utilizing an edge-on configuration of cadmium zinc telluride (CZT) detector crystals offers a potential solution to increase PET sensitivity. The high cross-section of CZT and its capacity to detect both 511 keV annihilation gammas and high-energy prompt gammas, along with multiple photon interaction events, contribute to this increased sensitivity. In this study, we propose a dual-panel edge-on CZT detector system comprised of 4 × 4 × 0.5 cm3 CZT detectors, with panel dimensions of 20 × 15 cm2 and a thickness of 4 cm. In this study, we demonstrate the increased sensitivity of our imaging system due to the detection of the Compton kinematics of high-energy gammas originating from prompt-gamma-emitting isotopes. This was achieved using Monte Carlo simulations of a prompt-gamma-emitting isotope,72As, with mean positron ranges >3 mm. Our system’s dynamic energy range, capable of detecting gammas up to 1.2 MeV, allows it to operate in a dual-mode fashion as both a Compton camera (CC) and standard PET. By presenting reconstructions of 72As, we highlight the absence of positron range effects in CC reconstructions compared to PET reconstructions. In addition, we evaluate the system’s increased sensitivity resulting from its ability to detect high-energy prompt gammas.

Deep learning-based partial volume correction in standard and low-dose positron emission tomography-computed tomography imaging.

Positron emission tomography (PET) imaging encounters the obstacle of partial volume effects, arising from its limited intrinsic resolution, giving rise to (I) considerable bias, particularly for structures comparable in size to the point spread function (PSF) of the system; and (II) blurred image edges and blending of textures along the borders. We set out to build a deep learning-based framework for predicting partial volume corrected full-dose (FD + PVC) images from either standard or low-dose (LD) PET images without requiring any anatomical data in order to provide a joint solution for partial volume correction and de-noise LD PET images. We trained a modified encoder-decoder U-Net network with standard of care or LD PET images as the input and FD + PVC images by six different PVC methods as the target. These six PVC approaches include geometric transfer matrix (GTM), multi-target correction (MTC), region-based voxel-wise correction (RBV), iterative Yang (IY), reblurred Van-Cittert (RVC), and Richardson-Lucy (RL). The proposed models were evaluated using standard criteria, such as peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity index (SSIM), relative bias, and absolute relative bias. Different levels of error were observed for these partial volume correction methods, which were relatively smaller for GTM with a SSIM of 0.63 for LD and 0.29 for FD, IY with an SSIM of 0.63 for LD and 0.67 for FD, RBV with an SSIM of 0.57 for LD and 0.65 for FD, and RVC with an SSIM of 0.89 for LD and 0.94 for FD PVC approaches. However, large quantitative errors were observed for multi-target MTC with an RMSE of 2.71 for LD and 2.45 for FD and RL with an RMSE of 5 for LD and 3.27 for FD PVC approaches. We found that the proposed framework could effectively perform joint de-noising and partial volume correction for PET images with LD and FD input PET data (LD vs. FD). When no magnetic resonance imaging (MRI) images are available, the developed deep learning models could be used for partial volume correction on LD or standard PET-computed tomography (PET-CT) scans as an image quality enhancement technique.

Tracking a moving point source using triple gamma imaging

Abstract With positron emission tomography (PET), the positron of a β + emitter radioisotope annihilates with a nearby electron producing a pair of back-to-back 511 keV gamma rays that can be detected in a scanner surrounding the point source. The position of the point source is somewhere along the Line of Response (LOR) that passes through the positions where the 511 keV gammas are detected. In standard PET, an image reconstruction algorithm is used to combine these LORs into a final image. This paper presents a new tomographic imaging technique to locate the position of a β + emitting point source without using a standard PET image reconstruction algorithm. The data were collected with a Proof-of-Concept (PoC) PET scanner which has high spatial and energy resolutions. The imaging technique presented in this paper uses events where a gamma undergoes Compton scattering. The positions and energies deposited by the Compton scattered gamma define the surface of a Compton cone (CC) which is the locus of all possible positions of the point source, allowed by the Compton kinematics. The position of the same point source is also located somewhere on the LOR. Therefore, the position of the point source is defined by the 3 gammas and is given by the intersection point of the LOR and the Compton cone inside the Field of View (FOV) of the scanner. We refer to this method as CC×LOR. This new technique can locate the point source with an uncertainty of about 1 mm, after collecting a minimum of 200 CC×LOR events.

Combination of tumor asphericity and an extracellular matrix-related prognostic gene signature in non-small cell lung cancer patients

One important aim of precision oncology is a personalized treatment of patients. This can be achieved by various biomarkers, especially imaging parameters and gene expression signatures are commonly used. So far, combination approaches are sparse. The aim of the study was to independently validate the prognostic value of the novel positron emission tomography (PET) parameter tumor asphericity (ASP) in non small cell lung cancer (NSCLC) patients and to investigate associations between published gene expression profiles and ASP. This was a retrospective evaluation of PET imaging and gene expression data from three public databases and two institutional datasets. The whole cohort comprised 253 NSCLC patients, all treated with curative intent surgery. Clinical parameters, standard PET parameters and ASP were evaluated in all patients. Additional gene expression data were available for 120 patients. Univariate Cox regression and Kaplan–Meier analysis was performed for the primary endpoint progression-free survival (PFS) and additional endpoints. Furthermore, multivariate cox regression testing was performed including clinically significant parameters, ASP, and the extracellular matrix-related prognostic gene signature (EPPI). In the whole cohort, a significant association with PFS was observed for ASP (p < 0.001) and EPPI (p = 0.012). Upon multivariate testing, EPPI remained significantly associated with PFS (p = 0.018) in the subgroup of patients with additional gene expression data, while ASP was significantly associated with PFS in the whole cohort (p = 0.012). In stage II patients, ASP was significantly associated with PFS (p = 0.009), and a previously published cutoff value for ASP (19.5%) was successfully validated (p = 0.008). In patients with additional gene expression data, EPPI showed a significant association with PFS, too (p = 0.033). The exploratory combination of ASP and EPPI showed that the combinatory approach has potential to further improve patient stratification compared to the use of only one parameter. We report the first successful validation of EPPI and ASP in stage II NSCLC patients. The combination of both parameters seems to be a very promising approach for improvement of risk stratification in a group of patients with urgent need for a more personalized treatment approach.

CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images.

Positron emission tomography (PET) is the most sensitive molecular imaging modality routinely applied in our modern healthcare. High radioactivity caused by the injected tracer dose is a major concern in PET imaging and limits its clinical applications. However, reducing the dose leads to inadequate image quality for diagnostic practice. Motivated by the need to produce high quality images with minimum 'low-dose', convolutional neural networks (CNNs) based methods have been developed for high quality PET synthesis from its low-dose counterparts. Previous CNNs-based studies usually directly map low-dose PET into features space without consideration of different dose reduction level. In this study, a novel approach named CG-3DSRGAN (Classification-Guided Generative Adversarial Network with Super Resolution Refinement) is presented. Specifically, a multi-tasking coarse generator, guided by a classification head, allows for a more comprehensive understanding of the noise-level features present in the low-dose data, resulting in improved image synthesis. Moreover, to recover spatial details of standard PET, an auxiliary super resolution network - Contextual-Net - is proposed as a second-stage training to narrow the gap between coarse prediction and standard PET. We compared our method to the state-of-the-art methods on whole-body PET with different dose reduction factors (DRF). Experiments demonstrate our method can outperform others on all DRF.Clinical Relevance- Low-Dose PET, PET recovery, GAN, task driven image synthesis, super resolution.

Dynomics: A Novel and Promising Approach for Improved Breast Cancer Prognosis Prediction.

Traditional imaging techniques for breast cancer (BC) diagnosis and prediction, such as X-rays and magnetic resonance imaging (MRI), demonstrate varying sensitivity and specificity due to clinical and technological factors. Consequently, positron emission tomography (PET), capable of detecting abnormal metabolic activity, has emerged as a more effective tool, providing critical quantitative and qualitative tumor-related metabolic information. This study leverages a public clinical dataset of dynamic 18F-Fluorothymidine (FLT) PET scans from BC patients, extending conventional static radiomics methods to the time domain-termed as 'Dynomics'. Radiomic features were extracted from both static and dynamic PET images on lesion and reference tissue masks. The extracted features were used to train an XGBoost model for classifying tumor versus reference tissue and complete versus partial responders to neoadjuvant chemotherapy. The results underscored the superiority of dynamic and static radiomics over standard PET imaging, achieving accuracy of 94% in tumor tissue classification. Notably, in predicting BC prognosis, dynomics delivered the highest performance, achieving accuracy of 86%, thereby outperforming both static radiomics and standard PET data. This study illustrates the enhanced clinical utility of dynomics in yielding more precise and reliable information for BC diagnosis and prognosis, paving the way for improved treatment strategies.

Detection of tau‐related structural connectivity changes in Alzheimer’s disease

AbstractBackgroundStructural connectivity assessed by diffusion tensor imaging (DTI) is reportedly associated with tau pathology in Alzheimer’s disease. In this study, we aimed to determine whether structural connectivity derived from DTI can reveal tau positivity at the individual level by utilizing a machine learning classifier.MethodAll data were collected from Alzheimer’s Disease Neuroimaging Initiative Phase 3. Sixty subjects (27 females; age range, 56‐94 years old) who underwent tau positron emission tomography (PET) and DTI scans at baseline were included. Tau pathology was assessed by 18F‐Flortaucipir standardized uptake value ratio PET in Braak stage composite regions. The cutoff point denoting positivity was set at 1.23 (Jack et al. 2017), yielding 30 scans each for normal and abnormal scans. DTI scans were acquired with 6 b0 and 48 diffusion‐weighted volumes with b = 1000 s/mm2 at 2.7 × 2.7 × 2.7 mm3 resolution and corrected for head motion and eddy‐current‐induced artifacts. Whole‐brain white‐matter fiber was tracked by deterministic tractography using DSI Studio under Q‐space diffeomorphic reconstruction scheme. The brain was parcellated into 120 regions‐of‐interest (ROIs) based on Automated Anatomical Labeling atlas 2, and each ROI served as a node of the network. Pairwise connectivity strength was quantified by the number of tracts connecting two ROIs and populated as a 120 × 120 connectivity matrix, serving as features for the following classification. A support vector machine classifier with the radial basis function kernel was trained for classification with stratified 10‐fold cross‐validation. Performance was evaluated by accuracy, sensitivity, and specificity.ResultThe average classification accuracy, sensitivity, and specificity were achieved at 65.0%, 86.7%, and 23.2% respectively.ConclusionStructural connectivity achieved high sensitivity in the detection of tau positivity, but it may not enable accurate classification at the individual level.

Synaptic Loss in Spinocerebellar Ataxia Type 3 Revealed by SV2A Positron Emission Tomography.

Severe reduced synaptic density was observed in spinocerebellar ataxia (SCA) in postmortem neuropathology, but in vivo assessment of synaptic loss remains challenging. OBJECTIVE SPINOCEREBELLAR ATAXIA TYPE 3: The objective of this study was to assess in vivo synaptic loss and its clinical correlates in spinocerebellar ataxia type 3 (SCA3) patients by synaptic vesicle glycoprotein 2A (SV2A)-positron emission tomography (PET) imaging. We recruited 74 SCA3 individuals including preataxic and ataxic stages and divided into two cohorts. All participants received SV2A-PET imaging using 18 F-SynVesT-1 for synaptic density assessment. Specifically, cohort 1 received standard PET procedure and quantified neurofilament light chain (NfL), and cohort 2 received simplified PET procedure for exploratory purpose. Bivariate correlation was performed between synaptic loss and clinical as well as genetic assessments. In cohort 1, significant reductions of synaptic density were observed in cerebellum and brainstem in SCA3 ataxia stage compared to preataxic stage and controls. Vermis was found significantly involved in preataxic stage compared to controls. Receiver operating characteristic (ROC) curves highlighted SV2A of vermis, pons, and medulla differentiating preataxic stage from ataxic stage, and SV2A combined with NfL improved the performance. Synaptic density was significantly negatively correlated with disease severity in cerebellum and brainstem (International Co-operative Ataxia Rating Scale: ρ ranging from -0.467 to -0.667, P ≤ 0.002; Scale of Assessment and Rating of Ataxia: ρ ranging from -0.465 to -0.586, P ≤ 0.002). SV2A reduction tendency of cerebellum and brainstem identified in cohort 1 was observed in cohort 2 with simplified PET procedure. We first identified in vivo synaptic loss was related to disease severity of SCA3, suggesting SV2A PET could be a promising clinical biomarker for disease progression of SCA3. © 2023 International Parkinson and Movement Disorder Society.

An RPC-PET brain scanner demonstrator: First results

We present first results from a Positron Emission Tomography (PET) scanner demonstrator based on Resistive Plate Chambers and specialized for brain imaging. The device features a 30 cm wide cubic field-of-view and each detector comprises 40 gas gaps with 3D location of the interaction point of the photon. Besides other imagery, we show that the reconstructed image resolution, as evaluated by a hot-rod phantom, is sub-millimetric, which is beyond the state-of-the-art of the standard PET technology for this application.

Realizing 32-time Scan Duration Reduction of 18F-FDG PET Using Deep Learning Model with Image Augmentation

Purpose: 32-time scan duration reduction of 18F-Fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET) images through the generation of standard scan duration images using a multi-slice cycle-consistent Generative Adversarial Network (cycle-GAN) was studied. Also, the effect of the image augmentation methods on the performance of the cycle-GAN model was evaluated.&#x0D; Materials and Methods: Four subsets of standard and 32-time short scan duration PET image pairs, each contacting image data of 10 patients were used to train and test (80 percent for training and 20 percent for testing) a multi-slice cycle-GAN separately. Another patient’s image data was used as the validation dataset for different training subsets. When training the cycle-GAN model for each subset, two approaches were followed: with and without image augmentation. Common image quality metrics of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Normalized Root Mean Squared Error (NRMSE) were used to assess the generation performance of the cycle-GAN model. Paired sample t-test statistical testing with a confidence interval of 0.95 was used to determine whether the differences between approaches were statistically significant or not.&#x0D; Results: For subsets 1-3, both training approaches improved the image quality of the short scan duration inputs (p &lt; 0.001) while for subset 4 only the training approach with image augmentation was capable of improving the image quality. However, the training approach with image augmentation offered better results than the approach without image augmentation (p &lt; 0.001).&#x0D; Conclusion: Employing the training approach with image augmentation, the cycle-GAN model was capable of improving the image quality of 1/32nd short scan duration images through the generation of synthetic standard scan duration images. In the case of the training approach without image augmentation, except for subset 4, the model trained on all subsets 1-3 was capable of improving the image quality. Image augmentation does indeed improve the performance of the cycle-GAN model, especially in the case of insufficient available training datasets.

Developing a PET normal brain template using diffusion tensor imaging images: A proof of concept

Background: Registered Positron emission tomography (PET) brain images to the standard normal PET brain templates can be performed to diagnosis dementia by using a vendor software, in which the brain template is based on T1-Weighted (T1W) images. However, the imperfection of an overlap between PET images and the PET-T1W based brain template could be observed. Objectives: This pilot study aimed to develop a new PET brain template and compare the accuracy of image registration between a conventional PET-T1W based brain template and our proposed PET-DTI based brain template. Materials and methods: The new PET-DTI based brain template was developed from twenty-four normal volunteers (age ranged 42-79 years old) who underwent 11C-Pittsburgh compound B PET scans and both T1W and diffusion tensor image (DTI) magnetic resonance imaging brain scans. The correction of Eddy-Current distortions and related artifact removing in DTI images were performed using the open-source FMRIB Software Library (FSL) to generate whole-brain probabilistic tractography maps (MRI-Probtract). MRI-Probtract map was then deformably registered and normalized to PET images, which were used for brain boundary guidance. The accuracy of image registration was assessed by applying the newly developed PET-DTI brain template to PET images of four mild cognitive impairment patients who underwent the same brain-scanning protocols. The accuracy of image registrations using the conventional PET-T1 and PET-DTI templates was evaluated qualitatively by three nuclear medicine physicians. Wilcoxon Signed Ranks test was used to compare registration scores of the two methods. Additionally, the dice similarly coefficient was obtained to quantitatively evaluate the accuracy of image registration. Results: The registration scores of the PET images registered with the PET-DTI template were significantly higher than the PET-T1 template at p-value &lt; 0.05. This result is consistent with the dice similarly coefficient where the value of PET-DTI template was higher. Conclusion: Result of this pilot study showed that new PET-DTI brain template provides higher registration quality, suggesting the feasibility of using PET-DTI template in a clinical PET study of the brain.

Statistical Parametric Mapping in Amyloid Positron Emission Tomography.

Alzheimer’s disease (AD), the most common cause of dementia, has limited treatment options. Emerging disease modifying therapies are targeted at clearing amyloid-β (Aβ) aggregates and slowing the rate of amyloid deposition. However, amyloid burden is not routinely evaluated quantitatively for purposes of disease progression and treatment response assessment. Statistical Parametric Mapping (SPM) is a technique comparing single-subject Positron Emission Tomography (PET) to a healthy cohort that may improve quantification of amyloid burden and diagnostic performance. While primarily used in 2-[18F]-fluoro-2-deoxy-D-glucose (FDG)-PET, SPM’s utility in amyloid PET for AD diagnosis is less established and uncertainty remains regarding optimal normal database construction. Using commercially available SPM software, we created a database of 34 non-APOE ε4 carriers with normal cognitive testing (MMSE > 25) and negative cerebrospinal fluid (CSF) AD biomarkers. We compared this database to 115 cognitively normal subjects with variable AD risk factors. We hypothesized that SPM based on our database would identify more positive scans in the test cohort than the qualitatively rated [11C]-PiB PET (QR-PiB), that SPM-based interpretation would correlate better with CSF Aβ42 levels than QR-PiB, and that regional z-scores of specific brain regions known to be involved early in AD would be predictive of CSF Aβ42 levels. Fisher’s exact test and the kappa coefficient assessed the agreement between SPM, QR-PiB PET, and CSF biomarkers. Logistic regression determined if the regional z-scores predicted CSF Aβ42 levels. An optimal z-score cutoff was calculated using Youden’s index. We found SPM identified more positive scans than QR-PiB PET (19.1 vs. 9.6%) and that SPM correlated more closely with CSF Aβ42 levels than QR-PiB PET (kappa 0.13 vs. 0.06) indicating that SPM may have higher sensitivity than standard QR-PiB PET images. Regional analysis demonstrated the z-scores of the precuneus, anterior cingulate and posterior cingulate were predictive of CSF Aβ42 levels [OR (95% CI) 2.4 (1.1, 5.1) p = 0.024; 1.8 (1.1, 2.8) p = 0.020; 1.6 (1.1, 2.5) p = 0.026]. This study demonstrates the utility of using SPM with a “true normal” database and suggests that SPM enhances diagnostic performance in AD in the clinical setting through its quantitative approach, which will be increasingly important with future disease-modifying therapies.

Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram

Over the last decades, there has been an increasing interest in dedicated preclinical imaging modalities for research in biomedicine. Especially in the case of positron emission tomography (PET), reconstructed images provide useful information of the morphology and function of an internal organ. PET data, stored as sinograms, involve the Radon transform of the image under investigation. The analytical approach to PET image reconstruction incorporates the derivative of the Hilbert transform of the sinogram. In this direction, in the present work we present a novel numerical algorithm for the inversion of the Radon transform based on Chebyshev polynomials of the first kind. By employing these polynomials, the computation of the derivative of the Hilbert transform of the sinogram is significantly simplified. Extending the mathematical setting of previous research based on Chebyshev polynomials, we are able to efficiently apply our new Chebyshev inversion scheme for the case of analytic preclinical PET image reconstruction. We evaluated our reconstruction algorithm on projection data from a small-animal image quality (IQ) simulated phantom study, in accordance with the NEMA NU 4-2008 standards protocol. In particular, we quantified our reconstructions via the image quality metrics of percentage standard deviation, recovery coefficient, and spill-over ratio. The projection data employed were acquired for three different Poisson noise levels: 100% (NL1), 50% (NL2), and 20% (NL3) of the total counts, respectively. In the uniform region of the IQ phantom, Chebyshev reconstructions were consistently improved over filtered backprojection (FBP), in terms of percentage standard deviation (up to 29% lower, depending on the noise level). For all rods, we measured the contrast-to-noise-ratio, indicating an improvement of up to 68% depending on the noise level. In order to compare our reconstruction method with FBP, at equal noise levels, plots of recovery coefficient and spill-over ratio as functions of the percentage standard deviation were generated, after smoothing the NL3 reconstructions with three different Gaussian filters. When post-smoothing was applied, Chebyshev demonstrated recovery coefficient values up to 14% and 42% higher, for rods 1–3 mm and 4–5 mm, respectively, compared to FBP, depending on the smoothing sigma values. Our results indicate that our Chebyshev-based analytic reconstruction method may provide PET reconstructions that are comparable to FBP, thus yielding a good alternative to standard analytic preclinical PET reconstruction methods.

MGluR5 and GABAA receptor-specific parametric PET atlas construction-PET/MR data processing pipeline, validation, and application.

The glutamate and γ‐aminobutyric acid neuroreceptor subtypes mGluR5 and GABAA are hypothesized to be involved in the development of a variety of psychiatric diseases. However, detailed information relating to their in vivo distribution is generally unavailable. Maps of such distributions could potentially aid clinical studies by providing a reference for the normal distribution of neuroreceptors and may also be useful as covariates in advanced functional magnetic resonance imaging (MR) studies. In this study, we propose a comprehensive processing pipeline for the construction of standard space, in vivo distributions of non‐displaceable binding potential (BP ND), and total distribution volume (V T) based on simultaneously acquired bolus‐infusion positron emission tomography (PET) and MR data. The pipeline was applied to [11C]ABP688‐PET/MR (13 healthy male non‐smokers, 26.6 ± 7.0 years) and [11C]Flumazenil‐PET/MR (10 healthy males, 25.8 ± 3.0 years) data. Activity concentration templates, as well as V T and BP ND atlases of mGluR5 and GABAA, were generated from these data. The maps were validated by assessing the percent error δ from warped space to native space in a selection of brain regions. We verified that the average δABP = 3.0 ± 1.0% and δFMZ = 3.8 ± 1.4% were lower than the expected variabilities σ of the tracers (σABP = 4.0%–16.0%, σFMZ = 3.9%–9.5%). An evaluation of PET‐to‐PET registrations based on the new maps showed higher registration accuracy compared to registrations based on the commonly used [15O]H2O‐template distributed with SPM12. Thus, we conclude that the resulting maps can be used for further research and the proposed pipeline is a viable tool for the construction of standardized PET data distributions.

Generation and validation of a PET radiomics model that predicts survival in diffuse large B cell lymphoma treated with R-CHOP14: A SAKK 38/07 trial post-hoc analysis.

Functional parameters from positron emission tomography (PET) seem promising biomarkers in various lymphoma subtypes. This study investigated the prognostic value of PET radiomics in diffuse large B-cell lymphoma (DLBCL) patients treated with R-CHOP given either every 14 (testing set) or 21 days (validation set). Using the PyRadiomics Python package, 107 radiomics features were extracted from baseline PET scans of 133 patients enrolled in the Swiss Group for Clinical Cancer Research 38/07 prospective clinical trial (SAKK 38/07) [ClinicalTrial.gov identifier: NCT00544219]. The international prognostic indices, the main clinical parameters and standard PET metrics, together with 52 radiomics uncorrelated features (selected using the Spearman correlation test) were included in a least absolute shrinkage and selection operator (LASSO) Cox regression to assess their impact on progression-free (PFS), cause-specific (CSS), and overall survival (OS). A linear combination of the resulting parameters generated a prognostic radiomics score (RS) whose area under the curve (AUC) was calculated by receiver operating characteristic analysis. The RS efficacy was validated in an independent cohort of 107 DLBCL patients. LASSO Cox regression identified four radiomics features predicting PFS in SAKK 38/07. The derived RS showed a significant capability to foresee PFS in both testing (AUC, 0.709; p<0.001) and validation (AUC, 0.706; p<0.001) sets. RS was significantly associated also with CSS and OS in testing (CSS: AUC, 0.721; p<0.001; OS: AUC, 0.740; p<0.001) and validation (CSS: AUC, 0.763; p<0.0001; OS: AUC, 0.703; p=0.004) sets. The RS allowed risk classification of patients with significantly different PFS, CSS, and OS in both cohorts showing better predictive accuracy respect to clinical international indices. PET-derived radiomics may improve the prediction of outcome in DLBCL patients.

Projections for dual radioisotope applications in PET imaging

Positron emission tomography (PET) are widely accepted and used as an effective medical imaging method. We have studied on the dual radioisotopes C11 and Cu60 together by modifying the isotopes used in Standard cyclindirial PET through GATE imaging simulation. We scan the proper resolution intervals and the distances between the sources and present the differences in parameters such as full width at half maximum (FWHM), intensity and contrast. Applying statistical χ^2 method, we aim to show the significance limits of above parameteric differences in PET simulations. These results may help determine in which conditions the imaging devices can be used with dual isotope method in clinical applications.

The clinical effectiveness of reconstructing 18F-sodium fluoride PET/CT bone using Bayesian penalized likelihood algorithm for evaluation of metastatic bone disease in obese patients.

A new Bayesian penalized likelihood reconstruction algorithm for positron emission tomography (PET) (Q.Clear) is now in clinical use for fludeoxyglucose (FDG) PET/CT. However, experience with non-FDG tracers and in special patient populations is limited. This pilot study aims to compare Q.Clear to standard PET reconstructions for 18F sodium fluoride (18F-NaF) PET in obese patients. 30 whole body 18F-NaF PET/CT scans (10 patients with BMI 30-40 Kg/m2 and 20 patients with BMI >40 Kg/m2) and a NEMA image quality phantom scans were analyzed using ordered subset expectation maximization (OSEM) and Q.Clear reconstructions methods with B400, 600, 800 and 1000. The images were assessed for overall image quality (IQ), noise level, background soft tissue, and lesion detectability, contrast recovery (CR), background variability (BV) and contrast-to-noise ratio (CNR) for both algorithms. CNR for clinical cases was higher for Q.Clear than OSEM (p < 0.05). Mean CNR for OSEM was (21.62 ± 8.9), and for Q.Clear B400 (31.82 ± 14.6), B600 (35.54 ± 14.9), B800 (39.81 ± 16.1), and B1000 (40.9 ± 17.8). As the β value increased the CNR increased in all clinical cases. B600 was the preferred β value for reconstruction in obese patients. The phantom study showed Q.Clear reconstructions gave lower CR and lower BV than OSEM. The CNR for all spheres was significantly higher for Q.Clear (independent of β) than OSEM (p < 0.05), suggesting superiority of Q.Clear. This pilot clinical study shows that Q.Clear reconstruction algorithm improves overall IQ of 18F-NaF PET in obese patients. Our clinical and phantom measurement results demonstrate improved CNR and reduced BV when using Q.Clear. A β value of 600 is preferred for reconstructing 18F-NaF PET/CT with Q.Clear in obese patients. 18F-NaF PET/CT is less susceptible to artifacts induced by body habitus. Bayesian penalized likelihood reconstruction with18F-NaF PET improves overall IQ in obese patients.

Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer.

BackgroundRadiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non‐small cell lung cancer (NSCLC) dataset.Materials and methodsThe proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1‐yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.ResultsThe number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4–10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).ConclusionWhen performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

A pilot study on lung cancer detection based on regional metabolic activity distribution in digital low-dose 18F-FDG PET.

To investigate the potential of automatic lung cancer detection on submillisievert dose 18F-fludeoxyglucose (18F-FDG) scans using different positron emission tomography (PET) parameters, as a primary step towards a potential new indication for 18F-FDG PET in lung cancer screening. We performed a retrospective cohort analysis with 83 patients referred for 18F-FDG PET/CT, including of 34 patients with histology-proven lung cancer and 49 patients without lung disease. Aside clinical standard PET images (PET100%) two additional low-dose PET reconstructions were generated, using only 15 s and 5 s of the 150 s list mode raw data of the full-dose PET, corresponding to 10% and 3.3% of the original 18F-FDG activity. The lungs were subdivided into three segments on each side, and each segment was classified as normal or containing cancer. The following standardized uptake values (SUVs) were extracted from PET per lung segment: SUVmean, SUVhot5, SUVmedian, SUVstd and SUVtotal. A multivariate linear regression model was used and cross-validated. The accuracy for lung cancer detection was tested with receiver operating characteristics analysis and T-statistics was used to calculate the weight of each parameter. The T-statistics showed that SUVstd was the most important discriminative factor for lung cancer detection. The multivariate model achieved an area under the curve of 0.97 for full-dose PET, 0.85 for PET10% with PET3.3% reconstructions resulting in a still high sensitivity the PET10% reconstruction of 80%. This pilot study indicates that segment-based, quantitative PET parameters of low-dose PET reconstructions could be used to automatically detect lung cancer with high sensitivity. Automated assessment of PET parameters in low-dose PET may aid for an early detection of lung cancer.