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Positron emission tomography/magnetic resonance imaging (PET/MRI) is an innovative imaging technology that allows the simultaneous acquisition of metabolic, structural, and functional information for an accurate characterization of tissues. The qualitative interpretation of PET/MR images has shown great potential in tumor diagnosis, treatment planning, and follow-up. Furthermore, PET/MRI also potentially provides several quantitative imaging biomarkers, also called “radiomic features,” for tumor characterization extracted both from PET and MRI. In the new era of precision medicine, radiomics is an emerging translational field of research aiming to extract a large number of quantitative features followed by their interpretation through various analyses and integration into predictive models. In this chapter, different clinical applications of radiomics based on PET, MRI, and PET/MR images will be presented, with a specific focus on the characterization of brain tumors, breast cancer, pancreatic neuroendocrine tumors, and prostate cancer. Finally, some other applications of artificial intelligence applied to PET/MR images that can shortly enter into clinical practice will be shown.

We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: “(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)” have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.

Purpose: To explore the role of fully hybrid 68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine tumours (PanNETs) undergoing surgery. Methods: One hundred eighty-seven consecutive 68Ga-DOTATOC PET/MRI scans (March 2018–June 2020) performed for gastroenteropancreatic neuroendocrine tumour were retrospectively evaluated; 16/187 patients met the eligibility criteria (68Ga-DOTATOC PET/MRI for preoperative staging of PanNET and availability of histological data). PET/MR scans were qualitatively and quantitatively interpreted, and the following imaging parameters were derived: PET-derived SUVmax, SUVmean, somatostatin receptor density (SRD), total lesion somatostatin receptor density (TLSRD), and MRI-derived apparent diffusion coefficient (ADC), arterial and late enhancement, necrosis, cystic degeneration, and maximum diameter. Additionally, first-, second-, and higher-order radiomic parameters were extracted from both PET and MRI scans. Correlations with several PanNETs’ histopathological prognostic factors were evaluated using Spearman’s coefficient, while the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate parameters’ predictive performance. Results: Primary tumour was detected in all 16 patients (15/16 by 68Ga-DOTATOC PET and 16/16 by MRI). SUVmax and SUVmean resulted good predictors of lymphnodal (LN) involvement (AUC of 0.850 and 0.783, respectively). Second-order radiomic parameters GrayLevelVariance and HighGrayLevelZoneEmphasis extracted from T2 MRI demonstrated significant correlations with LN involvement (adjusted p = 0.009), also showing good predictive performance (AUC = 0.992). Conclusion: This study demonstrates the role of the fully hybrid PET/MRI tool for the synergic function of imaging parameters extracted by the two modalities and highlights the potentiality of imaging and radiomic parameters in assessing h

The aim of the present study is to investigate the synergic role of68Ga-PSMA PET/MRI and68Ga-DOTA-RM2 PET/MRI in prostate cancer (PCa) staging. We present pilot data on twenty-two patients with biopsy-proven PCa that underwent68Ga-PSMA PET/MRI for staging purposes, with 19/22 also undergoing68Gaa-DOTA-RM2 PET/MRI. TNM classification based on image findings was performed and quantitative imaging parameters were collected for each scan. Furthermore, twelve patients underwent radical prostatectomy with the availability of histological data that were used as the gold standard to validate intraprostatic findings. A DICE score between regions of interest manually segmented on the primary tumour on68Ga-PSMA PET,68Ga-DOTA-RM2 PET and on T2 MRI was computed. All imaging modalities detected the primary PCa in 18/19 patients, with68Ga-DOTA-RM2 PET not detecting any lesion in 1/19 patients. In the remaining patients,68Ga-PSMA and MRI were concordant. Seven patients presented seminal vesicles involvement on MRI, with two of these being also detected by68Ga-PSMA, and68Ga-DOTA-RM2 PET being negative. Regarding extraprostatic disease,68Ga-PSMA PET,68Ga-DOTA-RM2 PET and MRI resulted positive in seven, four and five patients at lymph-nodal level, respectively, and at a bone level in three, zero and one patients, respectively. These preliminary results suggest the potential complementary role of68Ga-PSMA PET,68Ga-DOTA-RM2 PET and MRI in PCa characterization during the staging phase.

Background: Time-of-Flight (TOF) is a leading technological development of Positron Emission Tomography (PET) scanners. It reduces noise at the Maximum-Likelihood solution, depending on the coincidence–timing–resolution (CTR). However, in clinical applications, it is still not clear how to best exploit TOF information, as early stopped reconstructions are generally used. Methods: A contrast-recovery (CR) matching rule for systems with different CTRs and non-TOF systems is theoretically derived and validated using (1) digital simulations of objects with different contrasts and background diameters, (2) realistic phantoms of different sizes acquired on two scanners with different CTRs. Results: With TOF, the CR matching rule prescribes modifying the iterations number by the CTRs ratio. Without TOF, the number of iterations depends on the background dimension. CR matching was confirmed by simulated and experimental data. With TOF, image noise followed the square root of the CTR when the rule was applied on simulated data, while a significant reduction was obtained on phantom data. Without TOF, preserving the CR on larger objects significantly increased the noise. Conclusions: TOF makes PET reconstructions less dependent on background dimensions, thus, improving the quantification robustness. Better CTRs allows performing fewer updates, thus, maintaining accuracy while minimizing noise.

Purpose: To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. Methods: Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. Results: Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7–0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. Conclusions: Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.

Aim To explore the potentiality of radiomics analysis, performed on 68Ga-DOTATOC and fluorine-18-fluorodeoxyglucose (18F-FDG) PET/computed tomography (CT) images, in predicting tumour aggressiveness and outcome in patients candidate to surgery for pancreatic neuroendocrine neoplasms (PanNENs). Patients and methods Retrospective study including 61 patients who underwent 68Ga-DOTATOC and 18F-FDG PET/CT before surgery for PanNEN. Semiquantitative variables [SUVmax and somatostatin receptor density (SRD) for 68Ga-DOTATOC PET; SUVmax and MTV for 18F-FDG PET] and texture features [intensity variability, size zone variability (SZV), zone percentage, entropy; homogeneity, dissimilarity and coefficient of variation (Co-V)] have been analysed to evaluate their possible role in predicting tumour characteristics. Principal component analysis (PCA) was firstly performed and then multiple regression analyses were performed by using the extracted principal components. Results Regarding 68Ga-DOTATOC PET, SZV, entropy, intensity variability and SRD were predictive for tumour dimension. Regarding 18F-FDG PET, intensity variability, SZV, homogeneity, SUVmax and MTV were predictive for tumour dimension. Four principal components were extracted from PCA: PC1 correlated with all 18F-FDG variables, while PC2, PC3 and PC4 with 68Ga-DOTATOC variables. PC1 was the only significantly predicting angioinvasion (P = 0.0222); PC4 was the only one significantly predicting lymph nodal involvement (P = 0.0151). All principal components except PC4 significantly predicted tumour dimension (P <0.0001 for PC1, P = 0.0016 for PC2 and P < 0.0001 for PC3). Co-V from 68Ga-DOTATOC PET/CT was predictive of the outcome. Conclusion Specific texture features derived from preoperative 68Ga-DOTATOC and 18F-FDG PET/CT could noninvasively predict specific tumour characteristics and patients' outcome, delineating the potential role of dual tracer technique and texture analysis in the risk assessment of patients

Quantification of amyloid PET studies is most accurate if regions of interest (ROIs) are not affected by the presence of cerebrospinal fluid. Patients with high amyloid load often have great atrophy, therefore, the use of atlas-based ROIs, instead of patient specific anatomy, can underestimate amyloid load, leading to a bias. Traditionally, this can be overcome only using MR anatomical sequences, which are burdensome and might not be ideal to be performed for each patient in the clinical routine. In this work, we propose to overcome this issue by using a method based on deep learning. As CT scans provide anatomical information, even at the very low doses used for PET attenuation correction, we propose the use of such a scan, together with the PET one, for a U-NET based segmentation. The approach achieves a median DICE score of 77% on a validation cohort of N=20 patients, even when using only N=14 patients in the training dataset. A dedicated data augmentation strategy is used, and the individual contribution of each modality is analyzed. We find that the joint effect of PET and CT is beneficial (median DICE: PET only 73.0%, CT only 74%). A near perfect correlation with MR-based quantification was also found.

Automatic analysis toolboxes are popular in brain image analysis, both in clinical and in preclinical practices. In this regard, we proposed a new toolbox for mouse PET–CT brain image analysis including a new Statistical Parametric Mapping-based template and a pipeline for image registration of PET–CT images based on CT images. The new templates is compatible with the common coordinate framework (CCFv3) of the Allen Reference Atlas (ARA) while the CT based registration step allows to facilitate the analysis of mouse PET–CT brain images. From the ARA template, we identified 27 volumes of interest that are relevant for in vivo imaging studies and provided binary atlas to describe them. We acquired 20 C57BL/6 mice with [18F]FDG PET–CT, and 12 of them underwent 3D T2-weighted high-resolution MR scans. All images were elastically registered to the ARA atlas and then averaged. High-resolution MR images were used to validate a CT-based registration pipeline. The resulting method was applied to a mouse model of Parkinson’s disease subjected to a test–retest study (n = 6) with the TSPO-specific radioligand [18F]VC701. The identification of regions of microglia/macrophage activation was performed in comparison to the Ma and Mirrione template. The new toolbox identified 11 (6 after false discovery rate adjustment, FDR) brain sub-areas of significant [18F]VC701 uptake increase versus the 4 (3 after FDR) macro-regions identified by the Ma and Mirrione template. Moreover, these 11 areas are functionally connected as found by applying the Mouse Connectivity tool of ARA. In conclusion, we developed a mouse brain atlas tool optimized for PET–CT imaging analysis that does not require MR. This tool conforms to the CCFv3 of ARA and could be applied to the analysis of mouse brain disease models.

Background: Tumor heterogeneity poses major clinical challenges in high-grade gliomas (HGGs). Quantitative radiomic analysis with spatial tumor habitat clustering represents an innovative, non-invasive approach to represent and quantify tumor microenvironment heterogeneity. To date, habitat imaging has been applied mainly on conventional magnetic resonance imaging (MRI), although virtually extendible to any imaging modality, including advanced MRI techniques such as perfusion and diffusion MRI as well as positron emission tomography (PET) imaging. Objectives: This study aims to evaluate an innovative PET and MRI approach for assessing hypoxia, perfusion, and tissue diffusion in HGGs and derive a combined map for clustering of intra-tumor heterogeneity. Materials and Methods: Seventeen patients harboring HGGs underwent a pre-operative acquisition of MR perfusion (PWI), Diffusion (dMRI) and 18F-labeled fluoroazomycinarabinoside (18F-FAZA) PET imaging to evaluate tumor vascularization, cellularity, and hypoxia, respectively. Tumor volumes were segmented on fluid-attenuated inversion recovery (FLAIR) and T1 post-contrast images, and voxel-wise clustering of each quantitative imaging map identified eight combined PET and physiologic MRI habitats. Habitats’ spatial distribution, quantitative features and histopathological characteristics were analyzed. Results: A highly reproducible distribution pattern of the clusters was observed among different cases, particularly with respect to morphological landmarks as the necrotic core, contrast-enhancing vital tumor, and peritumoral infiltration and edema, providing valuable supplementary information to conventional imaging. A preliminary analysis, performed on stereotactic bioptic samples where exact intracranial coordinates were available, identified a reliable correlation between the expected microenvironment of the different spatial habitats and the actual histopathological features. A trend toward a higher representation of

Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 k

In emission tomography the Fisher information matrix is highly non-uniform along the diagonal due to the Poisson nature of photon counting. For this reason, quadratic penalties result in non-uniform spatial resolution, with the poorest resolution in regions with most counts. A strategy had been proposed to spatially modulate the penalty strength to achieve uniform resolution. In recent years, however, regularizations based on the L1 norm have generated much interest for their sparsity inducing proprieties. Nonetheless, we observed that, in emission tomography, if the penalty is not spatially modulated it suffers from the same problem observed in the L2 case. We propose here a spatial modulation factor for L1 penalties. We test this approach in two independent tasks. The first one involves the reconstruction of 2D PET digital phantoms in a sparse wavelet basis. The second task is the reconstruction of the attenuation map from emission data in 3D human studies with two L1 terms. In both tasks the proposed strategy greatly improves image quality and produces images that appear to be of uniform spatial resolution.

PURPOSE: In order to achieve comparability of image quality, harmonisation of PET system performance is imperative. In this study, prototype harmonisation criteria for PET brain studies were developed. METHODS: Twelve clinical PET/CT systems (4 GE, 4 Philips, 4 Siemens, including SiPM-based "digital" systems) were used to acquire 30-min PET scans of a Hoffman 3D Brain phantom filled with ~ 33 kBq·mL-1 [18F]FDG. Scan data were reconstructed using various reconstruction settings. The images were rigidly coregistered to a template (voxel size 1.17 × 1.17 × 2.00 mm3) onto which several volumes of interest (VOIs) were defined. Recovery coefficients (RC) and grey matter to white matter ratios (GMWMr) were derived for eroded (denoted in the text by subscript e) and non-eroded grey (GM) and white (WM) matter VOIs as well as a mid-phantom cold spot (VOIcold) and VOIs from the Hammers atlas. In addition, left-right hemisphere differences and voxel-by-voxel differences compared to a reference image were assessed. RESULTS: Systematic differences were observed for reconstructions with and without point-spread-function modelling (PSFON and PSFOFF, respectively). Normalising to image-derived activity, upper and lower limits ensuring image comparability were as follows: for PSFON, RCGMe = [0.97-1.01] and GMWMre = [3.51-3.91] for eroded VOI and RCGM = [0.78-0.83] and GMWMr = [1.77-2.06] for non-eroded VOI, and for PSFOFF, RCGMe = [0.92-0.99] and GMWMre = [3.14-3.68] for eroded VOI and RCGM = [0.75-0.81] and GMWMr = [1.72-1.95] for non-eroded VOI. CONCLUSIONS: To achieve inter-scanner comparability, we propose selecting reconstruction settings based on RCGMe and GMWMre as specified in "Results". These proposed standards should be tested prospectively to validate and/or refine the harmonisation criteria.

The reference standard for spatial normalization of brain positron emission tomography (PET) images involves structural Magnetic Resonance Imaging (MRI) data. However, the lack of such structural information is fairly common in clinical settings. This might lead to lack of proper image quantification and to evaluation based only on visual ratings, which does not allow research studies or clinical trials based on quantification. PET/CT systems are widely available and CT normalization procedures need to be explored. Here we describe and validate a procedure for the spatial normalization of PET images based on the low-dose Computed Tomography (CT) images contextually acquired for attenuation correction in PET/CT systems. We included N = 34 subjects, spanning from cognitively normal to mild cognitive impairment and dementia, who underwent amyloid-PET/CT (18F-Florbetaben) and structural MRI scans. The proposed pipeline is based on the SPM12 unified segmentation algorithm applied to low-dose CT images. The validation of the normalization pipeline focused on 1) statistical comparisons between regional and global 18F-Florbetaben-PET/CT standardized uptake value ratios (SUVrs) estimated from both CT-based and MRI-based normalized PET images (SUVrCT SUVrMRI) and 2) estimation of the degrees of overlap between warped gray matter (GM) segmented maps derived from CT- and MRI-based spatial transformations. We found negligible deviations between regional and global SUVrs in the two CT and MRI-based methods. SUVrCT and SUVrMRI global uptake scores showed negligible differences (mean ± sd 0.01 ± 0.03). Notably, the CT- and MRI-based warped GM maps showed excellent overlap (90% within 1 mm). The proposed analysis pipeline, based on low-dose CT images, allows accurate spatial normalization and subsequent PET image quantification. A CT-based analytical pipeline could benefit both research and clinical practice, allowing the recruitment of larger samples and favoring clinical routine a

Background: We explored the relation between blood concentrations of monocyte/lymphocyte subsets and carotid artery plaque macrophage content, measured by positron emission tomography (PET) with 11C-PK11195. Methods and results: In 9 patients with carotid plaques we performed 11C-PK11195-PET/computed tomography angiography imaging and measurement of absolute concentrations and frequencies of circulating monocytes and T-cell subsets. Plaque standardized uptake value (SUV) for 11C-PK11195 was negatively correlated with concentrations of total monocytes (r = −0.58, p = 0.05) and CD14++CD16−HLA-DR+ classical subset (r = −0.82, p = 0.005). These correlations hold true also in relation to plaque target to background ratio. No correlation was observed between plaque SUV and CD3+T lymphocytes, CD4+T lymphocytes nor with activated CD3+CD4+T cells expressing HLA-DR. Conclusions: We first demonstrated a reduction in the absolute concentration of monocytes and particularly in classical monocytes expressing HLA-DR in the presence of an increased uptake of 11C-PK11195 in carotid plaques. The present work, despite being a pilot study comprising only a small number of subjects provides new insights in the search for specific cellular biomarkers with potential diagnostic and prognostic value in patients with a known carotid plaque.

Objective: Postmortem studies reported significant microglia activation in association with neuronal apoptosis in Fatal Familial Insomnia (FFI), indicating a specific glial response, but negative evidence also exists. An in vivo study of local immune responses over FFI natural course may contribute to the understanding of the underlying pathogenesis. Methods: We included eight presymptomatic subjects (mean ± SD age:44.13 ± 3.83 years) carrying the pathogenic D178N-129met FFI mutation, one symptomatic patient (male, 45 yrs. old), and nine healthy controls (HC) (mean ± SD age: 44.00 ± 11.10 years.) for comparisons. 11C-(R)-PK11195 PET allowed the measurement of Translocator Protein (TSPO) overexpression, indexing microglia activation. A clustering algorithm was adopted to define subject-specific reference regions. Voxel-wise statistical analyses were performed on 11C-(R)-PK11195 binding potential (BP) images both at the group and individual level. Results: The D178N-129met/val FFI patient showed significant 11C-(R)-PK11195 BP increases in the midbrain, cerebellum, anterior thalamus, anterior cingulate cortex, orbitofrontal cortex, and anterior insula, bilaterally. Similar TSPO increases, but limited to limbic structures, were observed in four out of eight presymptomatic carriers. The only carrier with the codon 129met/val polymorphism was the only one showing an additional TSPO increase in the anterior thalamus. Interpretation: In comparison to nonprion neurodegenerative diseases, the observed lack of a diffuse brain TSPO overexpression in preclinical and the clinical FFI cases suggests the presence of a different microglia response. The involvement of limbic structures might indicate a role for microglia activation in these key pathologic regions, known to show the most significant neuronal loss and functional deafferentation in FFI.

Joint estimation of activity and attenuation in PET is promising but has not achieved widespread use due to its lack of robustness. In this work, we propose the use of a different minimizer, the Split-Bregman (SB) algorithm, instead of the common expectation maximization algorithm. The SB is widely used in compressive sensing for its ability to easily integrate a large number of L1 regularization terms. The L1 norm has been shown to be superior to the L2 norm in recovering signals that are sparse in a proper basis. In this work we review this minimizer and then perform two simulations of representative settings to analyse its performances to reconstruct the transmission part of the joint emission/transmission problem. We first reconstruct the transmission image of a digital phantom supposing that the emission image has been correctly reconstructed and we compare the results with the standard MLTR algorithm, implementing the total variation regularization in both algorithms. Both show comparable levels of noise but the SB minimization has better edge preserving proprieties due to the use of the L1 norm. In a second simulation we analyse a setup similar to a condition that could be encountered in a PET/MR study: some prior information is available about the attenuation map, but it is not correct everywhere; the prior information does not feature the MR coils and they lie outside of the region where the attenuation can be univocally recovered from PET coincidences. We show promising results which demonstrate that imposing the total variation together with the prior attenuation information is successful in recovering the attenuation map everywhere. This prototype algorithm which we present, can be easily modified to insert as many L1 constraints as desired, optimized for specific tasks.

Time of flight (TOF) in positron emission tomography (PET) is expected to greatly reduce image noise, and this can be clearly demonstrated from the likelihood of the problem. Despite this, clinical reconstructions using the maximum likelihood expectation maximization algorithm (MLEM) do not show reduced amounts of noise. In this work we review the theory that predicts the variance of a pixel as a function of iterations in MLEM. We are able to show that when reconstructions are stopped at the earliest iterations, as in clinical setups, TOF increases the noise variance. Nonetheless the convergence of small objects is greatly improved. Therefore, noise reduction can be achieved by performing fewer iterations without loss of image quality. In a second part of this work we perform 2D simulations. The simulations confirm the previous calculations showing a noise increase at the earliest iterations, while the predicted reduction is observed only at full convergence. We also find that the convergence rate of a small sphere improves with timing resolution and becomes more independent of the dimensions of the background object. Without TOF, convergence slows linearly with the background diameter. This can justify the use of less iterations in TOF reconstructions, to achieve equal or better contrast recovery at lower noise compared to traditional MLEM. In the final phase of this work we performed a phantom acquisition on a TOF capable PET scanner. Phantoms with spheres simulating a small or a large background were used. The analysis of the phantoms again showed higher noise with TOF up to about 120 MLEM iterations. The convergence rate of a 15 mm diameter sphere was compared in the small and large configurations and was found to improve using TOF.

Purpose: The aim of the present systematic review was to provide an overview on the different positron emission tomography (PET) hypoxia radiotracers in the clinical setting of lung cancer. Methods: We performed a comprehensive literature review on the role of PET hypoxia imaging in lung cancer using the electronic databases PubMed and Scopus to select English written language articles on humans from January 2007 to February 2017. The following keywords have been used: “hypoxia” or “hypoxic” and “PET” and “lung cancer”. Reviews, clinical reports, and editorial articles were excluded. Results: Originally, we considered 76 manuscripts, coming to a selection of 37 original articles. In particular, the selected original articles included the following PET radiotracer categories: nitroimidazole compounds, glucose analogue and bis(thiosemicarbazone) complexes. PET radiotracers, particularly nitroimidazole compounds, are the most suitable method to directly identify the presence of hypoxia in lung cancer. Conclusions: Based on the literature review, the definition of the role of clinical application of PET hypoxia radiotracers has been provided reporting that in vivo hypoxia imaging is needed for effective treatment selection, individual treatment planning, and treatment monitoring in oncology.

18F–fluoro-deoxy-glucose Positron Emission Tomography (FDG-PET) allows early identification of neurodegeneration in dementia. The use of an optimized method based on the SPM software package highly improves diagnostic accuracy. However, the impact of different scanners for data acquisition on the SPM results and the effects of different pools of healthy subjects on the statistical comparison have not been investigated yet. Images from 144 AD patients acquired using six different PET scanners were analysed with an optimized single-subject SPM procedure to identify the typical AD hypometabolism pattern at single subject level. We compared between-scanners differences on the SPM outcomes in a factorial design. Single-subject SPM comparison analyses were also performed against a different group of healthy controls from the ADNI initiative. The concordance between the two analyses (112 vs. 157 control subjects) was tested using Dice scores. In addition, we applied the optimized single-subject SPM procedure to the FDG-PET data acquired with 3 different scanners in 57 MCI subjects, in order to assess for tomograph influence in early disease phase. All the patients showed comparable AD-like hypometabolic patterns, also in the prodromal phase, in spite of being acquired with different PET scanners. SPM statistical comparisons performed with the two different healthy control databases showed a high degree of concordance (76% average pattern volume overlap and 90% voxel-wise agreement in AD-related brain structures). The validated optimized SPM-based single-subject procedure is influenced neither by the scanners used for image acquisition, nor by differences in healthy control groups, thus implying a great reliability of this method for longitudinal and multicentre studies.