Diffusion-weighted Imaging Voxelwise-matched Analyses of Lung Cancer at 3.0-T PET/MRI: Reverse Phase Encoding Approach for Echo-planar Imaging Distortion Correction.

Background PET/MRI has drawn increasing interest in thoracic oncology due to the simultaneous acquisition of PET and MRI data. Geometric distortions related to diffusion-weighted imaging (DWI) limit the evaluation of voxelwise multimodal analyses. Purpose To assess the effectiveness of reverse phase encoding in correcting DWI geometric distortion for multimodal PET/MRI voxelwise lung tumor analyses. Materials and Methods In this prospective study, reverse phase encoding method was implemented with 3.0-T PET/MRI to correct geometric distortions related to DWI. The method was validated in dedicated phantom and then applied to 12 consecutive patients (mean age, 66 years ± 13 [standard deviation]; 10 men) suspected of having lung cancer who underwent fluorodeoxyglucose PET/MRI between October 2018 and April 2019. The effects on DWI-related image matching and apparent diffusion coefficient (ADC) regional map computation were assessed. Consequences on multimodal PET/MRI voxelwise lung tumor analyses were evaluated. Spearman correlation coefficients (rs) between the standardized uptake value (SUV) and ADC data corrected for distortion were computed from optimal realigned DWI PET data, along with bootstrap confidence intervals. Results Phantom results showed that in highly distorted areas, correcting the distortion significantly reduced the mean error against the ground truth (-25% ± 10.6 to -18.4% ± 12.6; P < .001) and the number of voxels with more than 20% error (from 85.3% to 31.4%). In the 12 patients, the coregistration of multimodal PET/MRI tumor data was improved by using the reverse phase encoding method (0.4%-44%). In all tumors, voxelwise correlations (rs) between ADC and SUV revealed null or weak monotonic relationships (mean rs of 0.016 ± 0.24 with none above 0.5). Conclusion Reverse phase encoding is a simple-to-implement method for improved diffusion-weighted multimodal PET/MRI voxelwise-matched analyses in lung cancer. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Colletti in this issue.

Relevance of 18F-DOPA visual and semi-quantitative PET metrics for the diagnostic of Parkinson disease in clinical practice: a machine learning-based inference study.

PURPOSE: To decipher the relevance of visual and semi-quantitative 6-fluoro-(18F)-L-DOPA (18F-DOPA) interpretation methods for the diagnostic of idiopathic Parkinson disease (IPD) in hybrid positron emission tomography (PET) and magnetic resonance imaging. MATERIAL AND METHODS: A total of 110 consecutive patients (48 IPD and 62 controls) with 11 months of median clinical follow-up (reference standard) were included. A composite visual assessment from five independent nuclear imaging readers, together with striatal standard uptake value (SUV) to occipital SUV ratio, striatal gradients and putamen asymmetry-based semi-quantitative PET metrics automatically extracted used to train machine learning models to classify IPD versus controls. Using a ratio of 70/30 for training and testing sets, respectively, five classification models-k-NN, LogRegression, support vector machine, random forest and gradient boosting-were trained by using 100 times repeated nested cross-validation procedures. From the best model on average, the contribution of PET parameters was deciphered using the Shapley additive explanations method (SHAP). Cross-validated receiver operating characteristic curves (cv-ROC) of the most contributive PET parameters were finally estimated and compared. RESULTS: The best machine learning model (k-NN) provided final cv-ROC of 0.81. According to SHAP analyses, visual PET metric was the most important contributor to the model overall performance, followed by the minimum between left and right striatal to occipital SUV ratio. The 10-time cv-ROC curves of visual, min SUVr or both showed quite similar performance (mean area under the ROC of 0.81, 0.81 and 0.79, respectively, for visual, min SUVr or both). CONCLUSION: Visual expert analysis remains the most relevant parameter to predict IPD diagnosis at 11 months of median clinical follow-up in 18F-FDOPA. The min SUV ratio appears interesting in the perspective of simple semi-automated diagnostic workflows.

Head-to-Head Comparison Between Rabbit Sign and EANM/SNMMI Criteria for the 18F-DOPA Visual Assessment of Parkinsonian Syndromes in PET/MRI: A Multiple Expert-Based and Blinded Controlled Study.

PURPOSE: The aim of this study was to compare the diagnostic performance of the rabbit visual pattern versus the one endorsed by the EANM/SNMMI for the diagnosis of parkinsonian syndromes in PET/MRI. PATIENTS AND METHODS: The 18F-DOPA PET images of 129 consecutive patients (65 Park+ and 64 controls) with 1 year of clinical follow-up were reviewed independently by 5 experienced readers on the same imaging workstation, blinded to the final clinical diagnosis. Two visual methods were assessed independently, with several days to months of interval: the criteria endorsed by EANM/SNMMI and the "rabbit" shape of the striate assessed on 3D MIP images. The sensitivities, specificities, likelihood ratios, and predictive values of the 2 diagnostic tests were estimated simultaneously by using the "comparison of 2 binary diagnostic tests to a paired design" method. RESULTS: The estimated 95% confidence interval (CI) of sensitivities and specificities ranged from 49.4% to 76.5% and from 83.2% to 97.7%, respectively. The 95% CI estimates of positive and negative likelihood ratios ranged from 3.8 to 26.7 and from 0.26 to 0.56, respectively. The 95% CI estimates of the positive and negative predictive values ranged from 78.1% to 96.7% and from 60.3% to 81.4%, respectively. For all the parameters, no statistical difference was observed between the 2 methods (P > 0.05). The rabbit sign reduced the readers' discrepancies by 25%, while maintaining the same performance. CONCLUSIONS: The rabbit visual pattern appears at least comparable to the current EANM/SNMMI reference procedure for the assessment of parkinsonian syndromes in daily clinical practice, without the need of any image postprocessing. Further multicenter prospective studies would be of relevance to validate these findings.

Striatal and extrastriatal dopamine transporter in cannabis and tobacco addiction: a high-resolution PET study.

The dopamine (DA) system is known to be involved in the reward and dependence mechanisms of addiction. However, modifications in dopaminergic neurotransmission associated with long-term tobacco and cannabis use have been poorly documented in vivo. In order to assess striatal and extrastriatal dopamine transporter (DAT) availability in tobacco and cannabis addiction, three groups of male age-matched subjects were compared: 11 healthy non-smoker subjects, 14 tobacco-dependent smokers (17.6 ± 5.3 cigarettes/day for 12.1 ± 8.5 years) and 13 cannabis and tobacco smokers (CTS) (4.8 ± 5.3 cannabis joints/day for 8.7 ± 3.9 years). DAT availability was examined in positron emission tomography (HRRT) with a high resolution research tomograph after injection of [11C]PE2I, a selective DAT radioligand. Region of interest and voxel-by-voxel approaches using a simplified reference tissue model were performed for the between-group comparison of DAT availability. Measurements in the dorsal striatum from both analyses were concordant and showed a mean 20% lower DAT availability in drug users compared with controls. Whole-brain analysis also revealed lower DAT availability in the ventral striatum, the midbrain, the middle cingulate and the thalamus (ranging from -15 to -30%). The DAT availability was slightly lower in all regions in CTS than in subjects who smoke tobacco only, but the difference does not reach a significant level. These results support the existence of a decrease in DAT availability associated with tobacco and cannabis addictions involving all dopaminergic brain circuits. These findings are consistent with the idea of a global decrease in cerebral DA activity in dependent subjects.

Improved 3D tumour definition and quantification of uptake in simulated lung tumours using deep learning.

Objective.In clinical positron emission tomography (PET) imaging, quantification of radiotracer uptake in tumours is often performed using semi-quantitative measurements such as the standardised uptake value (SUV). For small objects, the accuracy of SUV estimates is limited by the noise properties of PET images and the partial volume effect. There is need for methods that provide more accurate and reproducible quantification of radiotracer uptake.Approach.In this work, we present a deep learning approach with the aim of improving quantification of lung tumour radiotracer uptake and tumour shape definition. A set of simulated tumours, assigned with 'ground truth' radiotracer distributions, are used to generate realistic PET raw data which are then reconstructed into PET images. In this work, the ground truth images are generated by placing simulated tumours characterised by different sizes and activity distributions in the left lung of an anthropomorphic phantom. These images are then used as input to an analytical simulator to simulate realistic raw PET data. The PET images reconstructed from the simulated raw data and the corresponding ground truth images are used to train a 3D convolutional neural network.Results.When tested on an unseen set of reconstructed PET phantom images, the network yields improved estimates of the corresponding ground truth. The same network is then applied to reconstructed PET data generated with different point spread functions. Overall the network is able to recover better defined tumour shapes and improved estimates of tumour maximum and median activities.Significance.Our results suggest that the proposed approach, trained on data simulated with one scanner geometry, has the potential to restore PET data acquired with different scanners.

[11C]glyburide PET imaging for quantitative determination of the importance of Organic Anion-Transporting Polypeptide transporter function in the human liver and whole-body.

Organic Anion-Transporting Polypeptides (OATPs) are known to control the liver uptake of many drugs. Non-hepatic expression of OATPs has been reported although functional importance for whole-body pharmacokinetics (WBPK) remains unknown. Glyburide is a well described substrate of several hepatic and non-hepatic OATPs. Dynamic whole-body positron emission tomography (DWB-PET) with [11C]glyburide was performed in humans for determination of the importance of OATPs for liver uptake and WBPK. Seven healthy male subjects (24.7 ± 3.2 years) underwent [11C]glyburide PET scan with concomitant blood sampling. All subjects underwent baseline [11C]glyburide PET scan. Five subjects underwent a subsequent [11C]glyburide PET scan after infusion of the potent OATP inhibitor rifampicin (9 mg/kg i.v.). The transfer constant (kuptake) of [11C]glyburide from blood to the liver was estimated using the integration plot method. The tissue exposure of [11C]glyburide was described by the area under the time-activity curve (AUC) and corresponding tissue/blood ratio (AUCR). [11C]glyburide was barely metabolized in both the baseline and rifampicin conditions. Parent (unmetabolized) [11C]glyburide accounted for > 90 % of the plasma radioactivity. Excellent correlation was found between radioactive counting in arterial blood samples and in the image-derived input function, in both the baseline and rifampicin conditions (R2 = 97.9 %, p < 0.01). [11C]glyburide predominantly accumulated in the liver. Rifampicin decreased liver kuptake by 77.3 ± 7.3 %, which increased exposure in blood, kidneys, spleen, myocardium and brain (p < 0.05). No significant change in AUCR was observed except in the liver (p < 0.01). [11C]glyburide benefits from metabolic stability and high sensitivity to OATP inhibition which enables quantitative determination of OATP function. DWB-PET suggests negligible role for non-hepatic OATPs in controlling the tissue distribution of [11C]glyburide.

Use of dynamic reconstruction for parametric Patlak imaging in dynamic whole body PET.

Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of PatlakKican be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images ofKi. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of PatlakKi. Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise. Additional regularisation methods need to be considered for DWB protocols to ensure both accurate quantification and acceptable noise levels for clinical applications.

Reconstruction, analysis and interpretation of posterior probability distributions of PET images, using the posterior bootstrap.

The uncertainty of reconstructed PET images remains difficult to assess and to interpret for the use in diagnostic and quantification tasks. Here we provide (1) an easy-to-use methodology for uncertainty assessment for almost any Bayesian model in PET reconstruction from single datasets and (2) a detailed analysis and interpretation of produced posterior image distributions. We apply a recent posterior bootstrap framework to the PET image reconstruction inverse problem and obtain simple parallelizable algorithms based on random weights and on existing maximuma posteriori(MAP) (posterior maximum) optimization-based algorithms. Posterior distributions are produced, analyzed and interpreted for several common Bayesian models. Their relationship with the distribution of the MAP image estimate over multiple dataset realizations is exposed. The coverage properties of posterior distributions are validated. More insight is obtained for the interpretation of posterior distributions in order to open the way for including uncertainty information into diagnostic and quantification tasks.

Challenges and Perspectives of the Hybridization of PET with Functional MRI or Ultrasound for Neuroimaging.

Hybridization of positron emission tomography (PET) with other functional neuroimaging techniques such as functional magnetic resonance imaging (fMRI) or functional ultrasound (fUS) still raises technical and methodological challenges. Beyond the co-registration of anatomical images with functional data, development of hybrid imaging systems has paved the way for a large field of research based on the concept of bimodal functional neuroimaging such as PET/fMRI. In this framework, comparison of respective performances of brain PET and fUS suggests complementarity and great potential of hybrid PET/fUS for preclinical neuroimaging. Hybridization of functional neuroimaging techniques first offers opportunities to validate or improve measurement made by each modality. Future research may propose and validate hybrid parameters that quantitatively connect the brain molecular environment and the neuro-vascular coupling, which may improve our understanding of brain function in health and disease, with perspectives in neuroscience and neuropharmacology. In the coming years, cross-fertilization of neuroimaging communities and training of young researchers on multiple imaging modalities may foster the development of hybrid neuroimaging protocols that will take the full potential and the limitations of each modality into account.

Distinct amyloid and tau PET signatures are associated with diverging clinical and imaging trajectories in patients with amnestic syndrome of the hippocampal type.

We aimed to investigate the amyloid and tau PET imaging signatures of patients with amnestic syndrome of the hippocampal type (ASHT) and study their clinical and imaging progression according to their initial PET imaging status. Thirty-six patients with a progressive ASHT and 30 controls underwent a complete neuropsychological assessment, 3 T brain MRI, [11C]-PiB and [18F]-Flortaucipir PET imaging. Subjects were clinically followed-up annually over 2 years, with a second 3 T MRI (n = 27 ASHT patients, n = 28 controls) and tau-PET (n = 20 ASHT patients) at the last visit. At baseline, in accordance with the recent biological definition of Alzheimer's disease (AD), the AD PET signature was defined as the combination of (i) positive cortical amyloid load, and (ii) increased tau tracer binding in the entorhinal cortices and at least one of the following regions: amygdala, parahippocampal gyri, fusiform gyri. Patients who did not meet these criteria were considered to have a non-AD pathology (SNAP). Twenty-one patients were classified as AD and 15 as SNAP. We found a circumscribed tau tracer retention in the entorhinal cortices and/or amygdala in 5 amyloid-negative SNAP patients. At baseline, the SNAP patients were older and had lower ApoE ε4 allele frequency than the AD patients, but both groups did not differ regarding the neuropsychological testing and medial temporal lobe atrophy. During the 2-year follow-up, the episodic memory and language decline, as well as the temporo-parietal atrophy progression, were more pronounced in the AD sub-group, while the SNAP patients had a more pronounced progression of atrophy in the frontal lobes. Longitudinal tau tracer binding increased in AD patients but remained stable in SNAP patients. At baseline, distinct amyloid and tau PET signatures differentiated early AD and SNAP patients despite identical cognitive profiles characterized by an isolated ASHT and a similar degree of medial temporal atrophy. During the longitudinal follow-up, AD and SNAP patients diverged regarding clinical and imaging progression. Among SNAP patients, tau PET imaging could detect a tauopathy restricted to the medial temporal lobes, which was possibly explained by primary age-related tauopathy.

Quantitative Tissue Pharmacokinetics and EPR Effect of AGuIX Nanoparticles: A Multimodal Imaging Study in an Orthotopic Glioblastoma Rat Model and Healthy Macaque.

AGuIX are emerging radiosensitizing nanoparticles (NPs) for precision radiotherapy (RT) under clinical evaluation (Phase 2). Despite being accompanied by MRI thanks to the presence of gadolinium (Gd) at its surface, more sensitive and quantifiable imaging technique should further leverage the full potential of this technology. In this study, it is shown that 89 Zr can be labeled on such NPs directly for positron emission tomography (PET) imaging with a simple and scalable method. The stability of such complexes is remarkable in vitro and in vivo. Using a glioblastoma orthotopic rat model, it is shown that injected 89 Zr-AGuIX is detectable inside the tumor for at least 1 week. Interestingly, the particles seem to efficiently infiltrate the tumor even in necrotic areas, which places great hope for the treatment of radioresistant tumor. Lastly, the first PET/MR whole-body imaging is performed in non-human primate (NHP), which further demonstrates the translational potential of these bimodal NP.

Fully Integrated Quantitative Multiparametric Analysis of Non-Small Cell Lung Cancer at 3-T PET/MRI: Toward One-Stop-Shop Tumor Biological Characterization at the Supervoxel Level.

INTRODUCTION: The aim of this study was to study the feasibility of a fully integrated multiparametric imaging framework to characterize non-small cell lung cancer (NSCLC) at 3-T PET/MRI. PATIENTS AND METHODS: An 18F-FDG PET/MRI multiparametric imaging framework was developed and prospectively applied to 11 biopsy-proven NSCLC patients. For each tumor, 12 parametric maps were generated, including PET full kinetic modeling, apparent diffusion coefficient, T1/T2 relaxation times, and DCE full kinetic modeling. Gaussian mixture model-based clustering was applied at the whole data set level to define supervoxels of similar multidimensional PET/MRI behaviors. Taking the multidimensional voxel behaviors as input and the supervoxel class as output, machine learning procedure was finally trained and validated voxelwise to reveal the dominant PET/MRI characteristics of these supervoxels at the whole data set and individual tumor levels. RESULTS: The Gaussian mixture model-based clustering clustering applied at the whole data set level (17,316 voxels) found 3 main multidimensional behaviors underpinned by the 12 PET/MRI quantitative parameters. Four dominant PET/MRI parameters of clinical relevance (PET: k2, k3 and DCE: ve, vp) predicted the overall supervoxel behavior with 97% of accuracy (SD, 0.7; 10-fold cross-validation). At the individual tumor level, these dimensionality-reduced supervoxel maps showed mean discrepancy of 16.7% compared with the original ones. CONCLUSIONS: One-stop-shop PET/MRI multiparametric quantitative analysis of NSCLC is clinically feasible. Both PET and MRI parameters are useful to characterize the behavior of tumors at the supervoxel level. In the era of precision medicine, the full capabilities of PET/MRI would give further insight of the characterization of NSCLC behavior, opening new avenues toward image-based personalized medicine in this field.

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data.

OBJECTIVES: To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI. MATERIAL AND METHODS: Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations). RESULTS: Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness. CONCLUSION: A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

Comparison of 3 methods of automated internal carotid segmentation in human brain PET studies: application to the estimation of arterial input function.

UNLABELLED: Quantitative brain (18)F-FDG PET studies often require the plasma time-activity curve (input function) for estimation of the cerebral metabolic rate of glucose (CMRglc). The gold standard for input function measurement is arterial blood sampling, which is invasive and time-consuming. Alternatively, input function can be estimated from dynamic images. This estimation often implies the use of manually placed regions of interest (ROIs) over cerebral vasculature, which is an operator-dependent and time-consuming task. The aim of our study was to compare 3 algorithms of image segmentation (local means analysis [LMA], soft-decision similar component analysis [SCA], and k-means) to automatically segment internal carotid arteries from dynamic (18)F-FDG brain studies. METHODS: The accuracy of automatic carotid segmentation algorithms was first tested using numeric phantoms of the human brain, by quantitatively assessing the overlap between the segmented carotids and the reference regions in the phantom. Then, the algorithm that yielded the best results was applied to data from 4 healthy volunteers, who underwent an (18)F-FDG dynamic 3-dimensional PET brain study. Concordance between manual and automatic ROIs, both uncorrected and after partial-volume effect and spillover correction, was first assessed. Linear regression was then used to compare manual versus automatic CMRglc values obtained using Patlak analysis. CMRglc values obtained by arterial sampling were used as a reference. RESULTS: In phantom studies, LMA was shown to be superior to the other segmentation algorithms. By visual inspection, volunteers' internal carotids segmented by LMA were anatomically relevant. No significant difference was found between ROI values obtained by manual and automatic segmentation, either uncorrected or corrected for partial-volume effect. Linear regression demonstrated excellent agreement between the manual and automatic image-derived CMRglc values (P < 0.0001), and both correlated well with the reference values obtained by plasma samples. CONCLUSION: The LMA segmentation algorithm allows accurate automatic delineation of internal carotids from dynamic PET brain studies. After correction for partial-volume effect, the main application would be the estimation of an image-derived input function.

Comparison of two commercial whole body PET systems based on LSO and BGO crystals respectively for brain imaging.

The prevalence of neurodegenerative diseases is growing in western countries and PET imaging is increasingly frequently used for clinical and research purposes. However, few PET cameras are dedicated to cerebral imaging. The Biograph 6 (Biograph6) (Siemens Medical Solutions) is a PET/CT dedicated to high throughput whole body studies. Its performance for cerebral imaging has not yet been assessed. The aims of this study were to compare the quantification and detectability of the Biograph for cerebral imaging with those of a well-validated PET camera, the ECAT EXACT HR+ (HR+) (Siemens Medical Solutions). A phantom measuring 19 cm long and 20 cm in diameter was filled with a 18F-fluorodeoxyglucose (18F-FDG) solution. Two 5.7 and 20 ml spheres filled with water (cold-spots), three 0.25 ml spheres (sphere-to-background: 3, 6, and 12), one 1.14 ml sphere (sphere-to-background: 3), and one 11.27 ml sphere (sphere-to-background: 2) filled with a radioactive solution were inserted into the phantom. The activity concentration was chosen so that the count rates for the phantom measurements matched those of typical brain studies on both cameras. Images were reconstructed using FORE and OSEM algorithms. The reconstruction parameters were adjusted to obtain a similar signal-to-noise ratio in images acquired with the two cameras. The contrast recovery (CR) coefficients were similar on the two scanners for the 5.7 and 20 ml spheres (cold spheres) and the 1.14 and 11.27 ml spheres (hot spheres). For the 0.25 ml spheres, the CR values were 35% higher for the sphere-to-background ratio of 12 and 39% higher for the sphere-to-background ratio of 6 on the Biograph6 for the 3 min scan. The variability of measurements was lower on the Biograph6 than on the HR+. The detectability for the smallest spheres on the Biograph6 was close to that on the HR+. The Biograph has similar performances as the HR+ reference tomograph for the detection and quantification of small hot spots and cold spots.

Time-of-flight (TOF) implementation for PET reconstruction in practice.

The time-of-flight (TOF) feature of PET scanners has been used for a long time in PET reconstruction, but many implementational aspects are still incomplete or ambiguous in the literature. Here we formalize and present theoretical and practical implementation details for the reconstruction of clinical TOF histogram and list-mode data using ML-EM. Relevant aspects include the computation of the TOF component of the system matrix, the processing of TOF bins, the use of estimations of random and scattered coincidences, and differences between histogram and list-mode ML-EM TOF reconstruction. Several approaches and approximations have been implemented in the CASToR platform and compared for OSEM reconstructions of patient data from the GE Signa PET/MR scanner. Differences between implementations are not larger than the typical bias in clinical data reconstruction. The largest difference and contrast loss occur when the processing of histogram TOF bins is simplified, and list-mode reconstruction is most sensitive to the truncation of the Gaussian TOF probability distribution.

PET Reconstruction of the Posterior Image Probability, Including Multimodal Images.

In PET image reconstruction, it would be useful to obtain the entire posterior probability distribution of the image, because it allows for both estimating image intensity and assessing the uncertainty of the estimation, thus leading to more reliable interpretation. We propose a new entirely probabilistic model: the prior is a distribution over possible smooth regions (distance-driven Chinese restaurant process), and the posterior distribution is estimated using a Gibbs Markov chain Monte Carlo sampler. Data from other modalities (here one or several MR images) are introduced into the model as additional observed data, providing side information about likely smooth regions in the image. The reconstructed image is the posterior mean, and the uncertainty is presented as an image of the size of 95% posterior intervals. The reconstruction was compared with the maximum-likelihood expectation-maximization and OSEM algorithms, with and without post-smoothing, and with a penalized ML or MAP method that also uses additional images from other modalities. Qualitative and quantitative tests were performed on realistic simulated data with statistical replicates and on several clinical examinations presenting pathologies. The proposed method presents appealing properties in terms of obtained bias, variance, spatial regularization, and use of multimodal data, and produces, in addition, potentially valuable uncertainty information.

Impact of image-space resolution modeling for studies with the high-resolution research tomograph.

UNLABELLED: Brain PET in small structures is challenged by low resolution inducing bias in the activity measurements. Improved spatial resolution may be obtained by using dedicated tomographs and more comprehensive modeling of the acquisition system during reconstruction. In this study, we assess the impact of resolution modeling (RM) during reconstruction on image quality and on the estimates of biologic parameters in a clinical study performed on a high-resolution research tomograph. METHODS: An accelerated list-mode ordinary Poisson ordered-subset expectation maximization (OP-OSEM) algorithm, including sinogram-based corrections and an experimental stationary model of resolution, has been designed. Experimental phantom studies are used to assess contrast and noise characteristics of the reconstructed images. The binding potential of a selective tracer of the dopamine transporter is also assessed in anatomic volumes of interest in a 5-patient study. RESULTS: In the phantom experiment, a slower convergence and a higher contrast recovery are observed for RM-OP-OSEM than for OP-OSEM for the same level of statistical noise. RM-OP-OSEM yields contrast recovery levels that could not be reached without RM as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with RM than without at matched resolution. In a uniform activity region, RM induces higher positive and lower negative correlations with neighboring voxels, leading to lower spatial variance. Clinical images reconstructed with RM demonstrate better delineation of cortical and subcortical structures in both time-averaged and parametric images. The binding potential in the striatum is also increased, a result similar to the one observed in the phantom study. CONCLUSION: In high-resolution PET, RM during reconstruction improves quantitative accuracy by reducing the partial-volume effects.

Segmentation of rodent whole-body dynamic PET images: an unsupervised method based on voxel dynamics.

Positron emission tomography (PET) is a useful tool for pharmacokinetics studies in rodents during the preclinical phase of drug and tracer development. However, rodent organs are small as compared to the scanner's intrinsic resolution and are affected by physiological movements. We present a new method for the segmentation of rodent whole-body PET images that takes these two difficulties into account by estimating the pharmacokinetics far from organ borders. The segmentation method proved efficient on whole-body numerical rat phantom simulations, including 3-14 organs, together with physiological movements (heart beating, breathing, and bladder filling). The method was resistant to spillover and physiological movements, while other methods failed to obtain a correct segmentation. The radioactivity concentrations calculated with this method also showed an excellent correlation with the manual delineation of organs in a large set of preclinical images. In addition, it was faster, detected more organs, and extracted organs' mean time activity curves with a better confidence on the measure than manual delineation.