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.

[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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction.

In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multi-dimensional data. The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture. The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer's software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speed-up increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.

Subject-specific bone attenuation correction for brain PET/MR: can ZTE-MRI substitute CT scan accurately?

In brain PET/MR applications, accurate attenuation maps are required for accurate PET image quantification. An implemented attenuation correction (AC) method for brain imaging is the single-atlas approach that estimates an AC map from an averaged CT template. As an alternative, we propose to use a zero echo time (ZTE) pulse sequence to segment bone, air and soft tissue. A linear relationship between histogram normalized ZTE intensity and measured CT density in Hounsfield units ([Formula: see text]) in bone has been established thanks to a CT-MR database of 16 patients. Continuous AC maps were computed based on the segmented ZTE by setting a fixed linear attenuation coefficient (LAC) to air and soft tissue and by using the linear relationship to generate continuous µ values for the bone. Additionally, for the purpose of comparison, four other AC maps were generated: a ZTE derived AC map with a fixed LAC for the bone, an AC map based on the single-atlas approach as provided by the PET/MR manufacturer, a soft-tissue only AC map and, finally, the CT derived attenuation map used as the gold standard (CTAC). All these AC maps were used with different levels of smoothing for PET image reconstruction with and without time-of-flight (TOF). The subject-specific AC map generated by combining ZTE-based segmentation and linear scaling of the normalized ZTE signal into [Formula: see text] was found to be a good substitute for the measured CTAC map in brain PET/MR when used with a Gaussian smoothing kernel of [Formula: see text] corresponding to the PET scanner intrinsic resolution. As expected TOF reduces AC error regardless of the AC method. The continuous ZTE-AC performed better than the other alternative MR derived AC methods, reducing the quantification error between the MRAC corrected PET image and the reference CTAC corrected PET image.

Transmodal Learning of Functional Networks for Alzheimer's Disease Prediction.

Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomarker of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.

Quantification of [(11)C]PIB PET for imaging myelin in the human brain: a test-retest reproducibility study in high-resolution research tomography.

An accurate in vivo measure of myelin content is essential to deepen our insight into the mechanisms underlying demyelinating and dysmyelinating neurological disorders, and to evaluate the effects of emerging remyelinating treatments. Recently [(11)C]PIB, a positron emission tomography (PET) tracer originally conceived as a beta-amyloid marker, has been shown to be sensitive to myelin changes in preclinical models and humans. In this work, we propose a reference-region methodology for the voxelwise quantification of brain white-matter (WM) binding for [(11)C]PIB. This methodology consists of a supervised procedure for the automatic extraction of a reference region and the application of the Logan graphical method to generate distribution volume ratio (DVR) maps. This approach was assessed on a test-retest group of 10 healthy volunteers using a high-resolution PET tomograph. The [(11)C]PIB PET tracer binding was shown to be up to 23% higher in WM compared with gray matter, depending on the image reconstruction. The DVR estimates were characterized by high reliability (outliers <1%) and reproducibility (intraclass correlation coefficient (ICC) >0.95). [(11)C]PIB parametric maps were also found to be significantly correlated (R(2)>0.50) to mRNA expressions of the most represented proteins in the myelin sheath. On the contrary, no correlation was found between [(11)C]PIB imaging and nonmyelin-associated proteins.