Biomedical subjects

Claude Comtat

Publications and source records attributed to Claude Comtat.

9 recordsLinked to original sources

PubMed2006

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.

Algorithms↗

PubMed2005

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

UNLABELLED: 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.

Algorithms↗

PubMed2004

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.

Humans↗

PubMed2004

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

UNLABELLED: 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.

Algorithms↗

PubMed2003

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

UNLABELLED: 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.

Algorithms↗

PubMed2003

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

UNLABELLED: 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.

Female↗

PubMed2002

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.

Computer Simulation↗

PubMed2002

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

UNLABELLED: 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.

Fluorodeoxyglucose F18↗

PubMed2002

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

UNLABELLED: 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.

Corpus Striatum↗