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.

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.

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 PatlakK_ican 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 ofK_i. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of PatlakK_i. 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.

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 (r_s) 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 (r_s) between ADC and SUV revealed null or weak monotonic relationships (mean r_s 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.

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.

Optimized Quantification of Translocator Protein Radioligand ¹⁸F-DPA-714 Uptake in the Brain of Genotyped Healthy Volunteers

Translocator protein (TSPO) is expressed at a low level in healthy brain and is upregulated during inflammatory processes that may occur in neurodegenerative diseases. Thus, TSPO may be a suitable in vivo indicator of neurodegeneration. Here, we quantified the (18)F-DPA-714 radioligand in healthy TSPO-genotyped volunteers and developed a method to eliminate the need for invasive arterial blood sampling.

METHODS

Ten controls (7 high-affinity binders [HABs] and 3 mixed-affinity binders [MABs]) underwent (18)F-DPA-714 PET with arterial and venous sampling. (18)F-DPA-714 binding was quantified with a metabolite-corrected arterial plasma input function, using the 1- and 2-tissue-compartment models (TCMs) as well as the Logan analysis to estimate total volume distribution (V(T)) in the regions of interest. Alternative quantification methods were tested, including tissue-to-plasma ratio or population-based input function approaches normalized by late time points of arterial or venous samples.

RESULTS

The distribution pattern of (18)F-DPA-714 was consistent with the known distribution of TSPO in humans, with the thalamus displaying the highest binding and the cerebellum the lowest. The 2-TCM best described the regional kinetics of (18)F-DPA-714 in the brain, with good identifiability (percentage coefficient of variation < 5%). For each region of interest, V(T) was 47.6% ± 6.3% higher in HABs than in MABs, and estimates from the 2-TCM and the Logan analyses were highly correlated. Equilibrium was reached at 60 min after injection. V(T) calculated with alternative methods using arterial samples was strongly and significantly correlated with that calculated by the 2-TCM. Replacement of arterial with venous sampling in these methods led to a significant but lower correlation.

CONCLUSION

Genotyping of subjects is a prerequisite for a reliable quantification of (18)F-DPA-714 PET images. The 2-TCM and the Logan analyses are accurate methods to estimate (18)F-DPA-714 V(T) in the human brain of both HAB and MAB individuals. Population-based input function and tissue-to-plasma ratio with a single arterial sample are promising alternatives to classic arterial plasma input function. Substitution with venous samples is promising but still requires methodologic improvements.

Bias reduction for low-statistics PET: maximum likelihood reconstruction with a modified Poisson distribution

Positron emission tomography data are typically reconstructed with maximum likelihood expectation maximization (MLEM). However, MLEM suffers from positive bias due to the non-negativity constraint. This is particularly problematic for tracer kinetic modeling. Two reconstruction methods with bias reduction properties that do not use strict Poisson optimization are presented and compared to each other, to filtered backprojection (FBP), and to MLEM. The first method is an extension of NEGML, where the Poisson distribution is replaced by a Gaussian distribution for low count data points. The transition point between the Gaussian and the Poisson regime is a parameter of the model. The second method is a simplification of ABML. ABML has a lower and upper bound for the reconstructed image whereas AML has the upper bound set to infinity. AML uses a negative lower bound to obtain bias reduction properties. Different choices of the lower bound are studied. The parameter of both algorithms determines the effectiveness of the bias reduction and should be chosen large enough to ensure bias-free images. This means that both algorithms become more similar to least squares algorithms, which turned out to be necessary to obtain bias-free reconstructions. This comes at the cost of increased variance. Nevertheless, NEGML and AML have lower variance than FBP. Furthermore, randoms handling has a large influence on the bias. Reconstruction with smoothed randoms results in lower bias compared to reconstruction with unsmoothed randoms or randoms precorrected data. However, NEGML and AML yield both bias-free images for large values of their parameter.

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.

Image-derived input function for human brain using high resolution PET imaging with [C](R)-rolipram and [C]PBR28

BACKGROUND

The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods.

METHODS

All seven methods were tested on twelve scans with [(11)C](R)-rolipram, which has a low radiometabolite fraction, and on nineteen scans with [(11)C]PBR28 (high radiometabolite fraction). Logan V(T) values for both blood and image inputs were calculated using the metabolite-corrected input functions. The agreement of image-derived Logan V(T) values with the reference blood-derived Logan V(T) values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model.

RESULTS

For both radioligands the highest scores were obtained with two blood-based methods, while the blood-free methods generally performed poorly. All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants.

CONCLUSION

OUR STUDY SHOWS THAT: 1) Image input methods that are validated for a specific tracer and a specific machine may not perform equally well in a different setting; 2) despite the use of high resolution PET images, blood samples are still necessary to obtain a reliable image input function; 3) the accuracy of image input may also vary between radioligands depending on the magnitude of the radiometabolite fraction: the higher the metabolite fraction of a given tracer (e.g., [(11)C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling.

Quantitative organ time activity curve extraction from rodent PET images without anatomical prior

PURPOSE

Numerous new drug candidates fail because of inadequate pharmacokinetics. Positron emission tomography (PET) enables the noninvasive characterization of the drug in humans and animals. The aim of the present work was the comparison of methods for the extraction of organ time activity curves from rodent PET images without requiring resort to anatomical information.

METHODS

The rodent organs were segmented using the local means analysis method and the accuracy of the time activity curve (TAC) estimated using four methods was compared: The mean TAC (Mean), the TAC computed in a selection of organ voxels (ROIopt), and the TAC corrected for partial volume effect using the geometric transfer matrix (GTM) method. The accuracy of the TAC estimated using the three methods was compared on phantom simulations and on experimental data sets on mice injected with fluorothymidine.

RESULTS

The segmentation quality measured on phantom simulation was 80% of overlap between segmented and gold standard organs. On the phantom simulations, the error on the TAC estimation on phantom simulations was lower for ROIopt (8%) than using the GTM (18%) and the Mean (27%) methods. Similar results were achieved on the experimental data sets: ROIopt (5.8%), GTM (9.7%), and Mean (12%).

CONCLUSIONS

The new ROI optimization method was fast and precise for all homogeneous organs, while mean organ TAC computation led as expected to important errors. GTM improved the quantification accuracy but showed instabilities due to segmentation errors and to small organ sizes. Partial volume effect correction or limitation is thus possible for the extraction of precise organ TACs without requiring either manual delineation or an anatomical modality.

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

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.

Exact and approximate Fourier rebinning algorithms for the solution of the data truncation problem in 3-D PET

This paper presents an extended 3-D exact rebinning formula in the Fourier space that leads to an iterative reprojection algorithm (iterative FOREPROJ), which enables the estimation of unmeasured oblique projection data on the basis of the whole set of measured data. In first approximation, this analytical formula also leads to an extended Fourier rebinning equation that is the basis for an approximate reprojection algorithm (extended FORE). These algorithms were evaluated on numerically simulated 3-D positron emission tomography (PET) data for the solution of the truncation problem, i.e., the estimation of the missing portions in the oblique projection data, before the application of algorithms that require complete projection data such as some rebinning methods (FOREX) or 3-D reconstruction algorithms (3DRP or direct Fourier methods). By taking advantage of all the 3-D data statistics, the iterative FOREPROJ reprojection provides a reliable alternative to the classical FOREPROJ method, which only exploits the low-statistics nonoblique data. It significantly improves the quality of the external reconstructed slices without loss of spatial resolution. As for the approximate extended FORE algorithm, it clearly exhibits limitations due to axial interpolations, but will require clinical studies with more realistic measured data in order to decide on its pertinence.

Assessment of 11C-PE2I binding to the neuronal dopamine transporter in humans with the high-spatial-resolution PET scanner HRRT

The high-resolution research tomograph (HRRT), dedicated to brain imaging, may offer new perspectives for identifying small brain nuclei that remain neglected by the spatial resolution of conventional scanners. However, the use of HRRT for neuroimaging applications still needs to be fully assessed. The present study aimed at evaluating the HRRT for measurement of the dopamine transporter (DAT) binding to validate its quantification and explore the gain induced by the increased spatial resolution in comparison with conventional PET scanners.

METHODS

Fifteen and 11 healthy subjects were examined using the selective DAT radioligand (11)C-PE2I with HRRT and HR+ scanners, respectively. Quantification of the DAT binding was assessed by the calculation of binding potential (BP) values using the simplified reference tissue model in anatomic regions of interest (ROIs) defined on the dorsal striatum and in a standardized ROI defined on the midbrain.

RESULTS

Quantification of (11)C-PE2I binding to the DAT measured in the midbrain and striatum with both scanners at the same spatial resolution (smoothed HRRT images) exhibited similar BP values and intersubject variability, thus validating the quantification of DAT binding on the HRRT. For age-paired comparison, BP values of subjects examined with HRRT were significantly higher than those of the subjects examined with HR+. The increase ranged from 29% in the caudate and 35% in the putamen to 92% in the midbrain. The decline in DAT binding with age in the striatum was in good agreement between both scanners and literature, whereas no significant decrease in DAT binding with age was observed in the midbrain with either HRRT or HR+.

CONCLUSION

HRRT allows quantitative measurements of neurotransmission processes in small brain nuclei and allows recovering higher values as compared with coarser spatial resolution PET scanners. High-spatial-resolution PET appears promising for a more accurate detection of neurobiologic modifications and also for the exploration of subtle modifications in small and complex brain structures largely affected by the partial-volume effect.

Joint estimation of dynamic PET images and temporal basis functions using fully 4D ML-EM

A fully 4D joint-estimation approach to reconstruction of temporal sequences of 3D positron emission tomography (PET) images is proposed. The method estimates both a set of temporal basis functions and the corresponding coefficient for each basis function at each spatial location within the image. The joint estimation is performed through a fully 4D version of the maximum likelihood expectation maximization (ML-EM) algorithm in conjunction with two different models of the mean of the Poisson measured data. The first model regards the coefficients of the temporal basis functions as the unknown parameters to be estimated and the second model regards the temporal basis functions themselves as the unknown parameters. The fully 4D methodology is compared to the conventional frame-by-frame independent reconstruction approach (3D ML-EM) for varying levels of both spatial and temporal post-reconstruction smoothing. It is found that using a set of temporally extensive basis functions (estimated from the data by 4D ML-EM) significantly reduces the spatial noise when compared to the independent method for a given level of image resolution. In addition to spatial image quality advantages, for smaller regions of interest (where statistical quality is often limited) the reconstructed time-activity curves show a lower level of bias and a lower level of noise compared to the independent reconstruction approach. Finally, the method is demonstrated on clinical 4D PET data.

Correction methods for random coincidences in fully 3D whole-body PET: impact on data and image quality

With the advantages of the increased sensitivity of fully 3-dimensional (3D) PET for whole-body imaging come the challenges of more complicated quantitative corrections and, in particular, an increase in the number of random coincidences. The most common method of correcting for random coincidences is the real-time subtraction of a delayed coincidence channel, which does not add bias but increases noise. An alternative approach is the postacquisition subtraction of a low-noise random coincidence estimate, which can be obtained either from a smoothed delayed coincidence sinogram or from a calibration scan or directly estimated. Each method makes different trade-offs between noise amplification, bias, and data-processing requirements. These trade-offs are dependent on activity injected, the local imaging environment (e.g., near the bladder), and the reconstruction algorithm.

METHODS

Using fully 3D whole-body simulations and phantom studies, we investigate how the gains in noise equivalent count (NEC) rates from using a noiseless random coincidence estimation method are translated to improvements in image signal-to-noise ratio (SNR). The image SNR, however, depends on the image reconstruction method and the local imaging environment.

RESULTS

We show that for fully 3D whole-body imaging using a particular set of scanners and clinical protocols, a low-noise estimate of random coincidences improves sinogram and image SNRs by approximately 15% compared with online subtraction of delayed coincidences.

CONCLUSION

A 15% improvement in image SNR arises from a 32% increase in the NEC rate. Thus, scan duration can be reduced by 25% while still maintaining a constant total acquired NEC.

GATE: a simulation toolkit for PET and SPECT

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

GATE: a simulation toolkit for PET and SPECT

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

Volumetric model and human observer comparisons of tumor detection for whole-body positron emission tomography

RATIONALE AND OBJECTIVES

The performances of model and human observers were compared for detecting and localizing tumors in whole-body positron emission tomography imaging. Volumetric implementations of model observers were used instead of standard planar implementations to reproduce the common practice of using volumetric image displays to assess whole-body positron emission tomography images.

MATERIALS AND METHODS

Observer studies with simulated data were used to compare three different acquisition protocols for an average patient size. Multiple realizations of simulated whole-body data with multiple added 1-cm diameter spherical lesions (targets) per image volume for efficiency were used. The location and contrast ratio of the targets were chosen randomly within ranges determined by initial calibration studies. Human observer studies were performed using a volumetric image display routinely used in clinical practice, and human observer detection performance was quantified using an alternate free-response operating characteristic analysis. The human detection performances were compared with the performances of volumetric implementations of the non-prewhitening matched filter and the channelized hotelling observer and also to the target contrast measured in the reconstructed image.

RESULTS

Human observer detectability was generally well described as a linear function of these three figures of merit for the detection task considered. The best correlations (r = 0.96, rho = 0.98) were achieved with the channelized hotelling observer and non-prewhitening matched filter model observers.

CONCLUSION

The use of volumetric model observers provides a means for quantitative comparisons of different protocols and also provides a useful tool for the optimization of key parameters in whole-body positron emission tomography imaging.

Fast fully 3-D image reconstruction in PET using planograms

We present a method of performing fast and accurate three-dimensional (3-D) backprojection using only Fourier transform operations for line-integral data acquired by planar detector arrays in positron emission tomography. This approach is a 3-D extension of the two-dimensional (2-D) linogram technique of Edholm. By using a special choice of parameters to index a line of response (LOR) for a pair of planar detectors, rather than the conventional parameters used to index a LOR for a circular tomograph, all the LORs passing through a point in the field of view (FOV) lie on a 2-D plane in the four-dimensional (4-D) data space. Thus, backprojection of all the LORs passing through a point in the FOV corresponds to integration of a 2-D plane through the 4-D "planogram." The key step is that the integration along a set of parallel 2-D planes through the planogram, that is, backprojection of a plane of points, can be replaced by a 2-D section through the origin of the 4-D Fourier transform of the data. Backprojection can be performed as a sequence of Fourier transform operations, for faster implementation. In addition, we derive the central-section theorem for planogram format data, and also derive a reconstruction filter for both backprojection-filtering and filtered-backprojection reconstruction algorithms. With software-based Fourier transform calculations we provide preliminary comparisons of planogram backprojection to standard 3-D backprojection and demonstrate a reduction in computation time by a factor of approximately 15.

A lesion detection observer study comparing 2-dimensional versus fully 3-dimensional whole-body PET imaging protocols

We compared the impact of 2-dimensional (2D) and fully 3-dimensional (3D) acquisition modes on the performance of human observers in detecting and localizing tumors in whole-body (18)F-FDG images.

METHODS

We selected protocols based on noise equivalent count (NEC) rates derived from a series of 2D and fully 3D whole-body patient and phantom acquisitions on a dual-mode PET scanner. The fully 3D peak NEC value for a standard 70-kg patient was achieved for an injected dose of approximately 444 MBq (12 mCi) assuming a 90-min delay before acquisition, whereas the 2D peak value was never reached. The protocols were therefore set to those corresponding to a 444-MBq injected dose in fully 3D and 2D and a 740-MBq (20 mCi) injected dose in 2D that was considered as the maximum allowable dose. We used a non-Monte Carlo simulator to generate multiple realizations of whole-body PET data based on the geometry of the mathematic cardiac torso phantom (MCAT) with accurate noise properties. Two-dimensional and fully 3D acquisition times were set to 5 min per bed position. Spherical 1-cm-diameter lesions (targets) with random locations and contrasts were distributed in different organs. The simulated 2D datasets were reconstructed using attenuation-weighted ordered-subsets expectation maximization ((AW)OSEM) and the fully 3D datasets were reconstructed with FORE+(AW)OSEM (FORE = Fourier rebinning). Five human observers located and ranked the targets using a volumetric display of the whole-body PET data to replicate the clinical practice. An alternate free-response operating characteristic (AFROC) analysis of the human observer reports was performed for each protocol and each organ separately.

RESULTS

The 2D protocol corresponding to 740-MBq injected dose allowed the overall best detection performance. It was followed by the fully 3D acquisition at the peak fully 3D NEC rate from a 444-MBq injected dose. A 2D acquisition corresponding to a 444-MBq injected dose was ranked last. Differences in detection performance were organ specific.

CONCLUSION

This study showed that, for this patient size and scanner type, the fully 3D acquisition mode allowed better or equivalent detection performance than the 2D mode for an injected dose corresponding to the peak fully 3D NEC rate. The 2D acquisition protocol combined with a higher injected dose resulted in the highest detectabilities.

An analytic study of the effects of attenuation on tumor detection in whole-body PET oncology imaging

There has been considerable debate about the desirability of attenuation correction in whole-body PET oncology imaging. The advantages of attenuation correction are quantitative accuracy, whereas the perceived disadvantages are loss of contrast, noise amplification, and increased scanning time. In this work, we explain contrast changes between images reconstructed with and without attenuation correction.

METHODS

To analytically explain both well-known and surprising phenomena in images reconstructed without attenuation correction, we performed a series of simulation studies, a phantom experiment, and a patient experiment.

RESULTS

We showed that it is possible to calculate a priori the appearance of images reconstructed without attenuation correction. Compared with attenuation-corrected images, images without attenuation correction may have locally enhanced contrast in the abdomen or other regions of uniform attenuation, although the amount of enhancement varies with position in a complex manner. In regions of nonuniform attenuation, such as the thorax, it is possible that foci of increased tracer uptake disappear in images reconstructed without attenuation correction. The critical tracer concentration for this zero-contrast effect depends on the size, location, and density of the foci. Above the critical value, foci are visible in images with and without attenuation correction, whereas below the critical value, foci are visible in attenuation-corrected images but appear as photopenic regions in images without attenuation correction.

CONCLUSION

Even though images without attenuation correction may be desired, these results suggest that all studies should at least be reconstructed with attenuation correction to avoid missing regions of elevated tracer uptake.

Evaluating image reconstruction methods for tumor detection in 3-dimensional whole-body PET oncology imaging

We compare 3 image reconstruction algorithms for use in 3-dimensional (3D) whole-body PET oncology imaging. We have previously shown that combining Fourier rebinning (FORE) with 2-dimensional (2D) statistical image reconstruction via the ordered-subsets expectation-maximization (OSEM) and attenuation-weighted OSEM (AWOSEM) algorithms demonstrates improvements in image signal-to-noise ratios compared with the commonly used analytic 3D reprojection (3DRP) or FORE+FBP (2D filtered backprojection) reconstruction methods. To assess the impact of these reconstruction methods on detecting and localizing small lesions, we performed a human observer study comparing the different reconstruction methods. The observer study used the same volumetric visualization software tool that is used in clinical practice, instead of a planar viewing mode as is generally used with the standard receiver operating characteristic (ROC) methodology. This change in the human evaluation strategy disallowed the use of a ROC analysis, so instead we compared the fraction of actual targets found and reported (fraction-found) and also investigated the use of an alternative free-response operating characteristic (AFROC) analysis.

METHODS

We used a non-Monte Carlo technique to generate 50 statistically accurate realizations of 3D whole-body PET data based on an extended mathematic cardiac torso (MCAT) phantom and with noise levels typical of clinical scans performed on a PET scanner. To each realization, we added 7 randomly located 1-cm-diameter lesions (targets) whose contrasts were varied to sample the range of detectability. These targets were inserted in 3 organs of interest: lungs, liver, and soft tissues. The images were reconstructed with 3 reconstruction strategies (FORE+OSEM, FORE+AWOSEM, and FORE+FBP). Five human observers reported (localized and rated) 7 targets within each volume image. An observer's performance accuracy with each algorithm was measured, as a function of the lesion contrast and organ type, by the fraction of those targets reported and by the area below the AFROC curve. This AFROC curve plots the fraction of reported targets at each rating threshold against the fraction of cases with (> or =1) similarly rated false reports.

RESULTS

Images reconstructed with FORE+AWOSEM yielded the best overall target detection as compared with FORE+FBP and FORE+OSEM, although these differences in detectability were region specific. The FORE+FBP and FORE+AWOSEM algorithms had similar performances for liver targets. The FORE+OSEM algorithm performed significantly worse at target detection, especially in the liver. We speculate that this is the result of using an incorrect statistical model for OSEM and that the incorporation of attenuation weighting in AWOSEM largely compensates for this model inaccuracy. These results were consistent for both the fraction of actual targets found and the AFROC analysis.

CONCLUSION

We demonstrated the efficacy of performing observer detection studies using the same visualization tools as those used in clinical PET oncology imaging. These studies demonstrated that the FORE+AWOSEM algorithm led to the best overall detection and localization performance for 1-cm-diameter targets compared with the FORE+OSEM and FORE+FBP algorithms.

Correction of partial-volume effect for PET striatal imaging: fast implementation and study of robustness

PET imaging of D(2) receptors or (18)F-L-dopa metabolism are reference protocols to follow and study neurodegenerative diseases, but the accuracy of striatal PET imaging is limited by the partial-volume effect (PVE). For such studies, the geometric transfer matrix (GTM) method has been proposed to correct the regional mean values for PVE and is now widely used.

METHODS

The GTM method models the geometric interactions induced by the PET system between the anatomic regions in which PVE correction is performed. This implies estimation of the corresponding regional spread function (RSF). The literature describes 2 implementations for the RSF calculation; they differ in the way the point spread function (PSF) of the imaging system is modeled, but no comparison or discussion has been given. The first and reference implementation uses an accurate intrinsic detector PSF that is applied in the sinogram space. The second uses a global PSF that is applied in the image space. In this work, we compared the 2 GTM implementations for 3-dimensional (3D) PET striatal imaging using Monte Carlo simulations and a phantom study. We studied the robustness of the GTM correction with respect to residual registration errors between PET and anatomy and with respect to residual segmentation errors.

RESULTS

Despite the differences in RSF calculation and computation cost between the 2 implementations, similar recovery results were obtained (between 95% and 100%). The study of robustness of the GTM correction yielded 2 results. A realistic residual misregistration between the anatomic and PET images did not modify the algorithm accuracy but decreased its precision. Conversely, a realistic residual missegmentation of the anatomic regions submitted to GTM correction decreased the correction accuracy.

CONCLUSION

A simple but efficient implementation in the image space of the GTM method yields accurate PVE correction in striatal regions in studies with 3D PET and enables clinical use. The method is less sensitive to residual misregistration errors between PET and anatomy than to residual missegmentation of the anatomy. Special care should be taken with segmentation of the regions to correct for PVE.

Optimization of injected dose based on noise equivalent count rates for 2- and 3-dimensional whole-body PET

The noise equivalent count (NEC) rate index is used to derive guidelines on the optimal injected dose to the patient for 2-dimensional (2D) and 3-dimensional (3D) whole-body PET acquisitions.

METHODS

We performed 2D and 3D whole-body acquisitions of an anthropomorphic phantom modeling the conditions for (18)F-FDG PET of the torso and measured the NEC rates for different activity levels for several organs of interest. The correlations between count rates measured from the phantom and those from a series of whole-body patient scans were then analyzed. This analysis allowed validation of our approach and estimation of the injected dose that maximizes NEC rate as a function of patient morphology for both acquisition modes.

RESULTS

Variations of the phantom and patient prompt and random coincidence rates as a function of single-photon rates correlated well. On the basis of these correlations, we demonstrated that the patient NEC rate can be predicted for a given single-photon rate. Finally, we determined that patient single-photon rates correlated with the mean dose per weight at acquisition start when normalized by the body mass index. This correlation allows modifying the injected dose as a function of patient body mass index to reach the peak NEC rate in 3D mode. Conversely, we found that the peak NEC rates were never reached in 2D mode within an acceptable range of injected dose.

CONCLUSION

The injected dose was adapted to patient morphology for 2D and 3D whole-body acquisitions using the NEC rate as a figure of merit of the statistical quality of the sinogram data. This study is a first step toward a more comprehensive comparison of the image quality obtained using both acquisition modes.

Clinically feasible reconstruction of 3D whole-body PET/CT data using blurred anatomical labels

We present the results of utilizing aligned anatomical information from CT images to locally adjust image smoothness during the reconstruction of three-dimensional (3D) whole-body positron emission tomography (PET) data. The ability of whole-body PET imaging to detect malignant neoplasms is becoming widely recognized. Potentially useful, however, is the role of whole-body PET in quantitative estimation of tracer uptake. The utility of PET in oncology is often limited by the high level of statistical noise in the images. Reduction in noise can be obtained by incorporating a priori image smoothness information from correlated anatomical information during the reconstruction of PET data. A combined PET/CT scanner allows the acquisition of accurately aligned PET and x-ray CT whole-body data. We use the Fourier rebinning algorithm (FORE) to accurately convert the 3D PET data to two-dimensional (2D) data to accelerate the image reconstruction process. The 2D datasets are reconstructed with successive over-relaxation of a penalized weighted least squares (PWLS) objective function to model the statistics of the acquisition, data corrections, and rebinning. A 3D voxel label model is presented that incorporates the anatomical information via the penalty weights of the PWLS objective function. This combination of FORE + PWLS + labels was developed as it allows for both reconstruction of 3D whole-body data sets in clinically feasible times and also the inclusion of anatomical information in such a way that convergence can be guaranteed. Since mismatches between anatomical (CT) and functional (PET) data are unavoidable in practice, the labels are 'blurred' to reflect the uncertainty associated with the anatomical information. Simulated and experimental results show the potential advantage of incorporating anatomical information by using blurred labels to calculate the penalty weights. We conclude that while the effect of this method on detection tasks is complicated and unclear, there is an improvement on the estimation task.

Comparison of 3-D reconstruction with 3D-OSEM and with FORE+OSEM for PET

The combination of Fourier rebinning (FORE) and the ordered subsets expectation-maximization (OSEM), a fast statistical algorithm, appears as a promising alternative to the fully three-dimensional (3-D) iterative approach for clinical positron emission tomography (PET) data. In this paper, we evaluated the properties of FORE+OSEM and compared it with fully 3-D OSEM using both simulations and data acquired by commercial scanners. The aim is to determine to what extent the speed advantage of FORE+OSEM is paid for by a possible degradation of image quality in the case of noisy clinical PET data. A forward- and back-projection pair based on a line integral model was used in two-dimensional OSEM and 3-D OSEM (3D-OSEM) instead of a system matrix. Different variants of both approaches have been studied with simulations in terms of contrast-noise tradeoff. Two variants--FORE+OSEM with attenuation weighting (AW) [FORE+OSEM(AW)] and 3D-OSEM with attenuation-normalization weighting (ANSP) and a shifted-Poisson (SP) model [3D-OSEM(ANSP)]--were compared with measured phantom data and patient data. Based on the results from both simulations and measured data, we conclude that: 1) both attenuation (-normalization) weighting and the SP model improve the image quality but slow down the convergence and 2) despite its approximate nature, FORE+OSEM does not show apparent image degradation compared with 3D-OSEM for data with a noise level typical of a whole-body FDG scan.

Iterative crystal efficiency calculation in fully 3-D PET

The calculation of the intrinsic efficiency of individual crystals is one of the steps needed to obtain accurate images of the radioisotope distribution in positron emission tomography (PET). These efficiencies can be computed by comparing the number of coincidence counts obtained when the crystals are equally illuminated by the same source. However, because the number of coincidence counts acquired for one crystal also depends on the efficiency of the other crystals in coincidence, most methods of crystal efficiency calculation need to assume that the influence of the other crystals is negligible. If there are large crystal efficiency variations, this approximation may lead to systematic errors. We have recently implemented an iterative method for a single ring of detectors that does not rely on this assumption. In this paper, we describe a fully three-dimensional (3-D) iterative method that better exploits the sensitivity of the tomograph and allows reduced acquisition times or the use of narrow energy windows. We compare the performance of the iterative method (single-ring and extended to fully 3-D) with noniterative techniques for different acquisition times of a uniform cylinder. Two different energy windows were used to assess the performance of each method with different levels of variations of crystal efficiency. The results showed that the iterative methods are more accurate when large efficiency variations exist and that only the fully 3-D methods provided good efficiency estimates with very low duration scans. We, thus, conclude that iterative fully 3-D methods provide the best estimations and can be used in a larger range of situations than can the other methods tested.

Comparative evaluation of MR-based partial-volume correction schemes for PET

Because of limitations of spatial resolution, quantitative PET measurements of cerebral blood flow, glucose metabolism and neuroreceptor binding are influenced by partial-volume averaging among neighboring tissues with differing tracer concentrations.

METHODS

Two MR-based approaches to partial-volume correction of PET images were compared using simulations and a multicompartment phantom. The two-compartment method corrects PET data for the diluting effects of cerebrospinal fluid (CSF) spaces. The more complex three-compartment method also accounts for the effect of partial-volume averaging between gray and white matter. The effects of the most significant sources of error on MR-based partial-volume correction, including misregistration, resolution mismatch, segmentation errors and white matter heterogeneity, were evaluated. We also examined the relative usefulness of both approaches in PET studies of aging and neurodegenerative disease.

RESULTS

Although the three-compartment method was highly accurate (with 100% gray matter recovery achieved in simulations), it was also more sensitive to all errors tested, particularly image segmentation and PET-MR registration.

CONCLUSION

Based on these data, we conclude that the two-compartment approach is better suited for comparative PET studies, whereas the three-compartment algorithm is capable of greater accuracy for absolute quantitative measures.