Absolute quantitation in neurological PET: do we need it?

This article addresses the question posed in the title by examining the effects of parameters traditionally associated with improved absolute quantitation, on the analysis of 12 acquired immune deficiency syndrome dementia complex (ADC) patients compared to a normal control group. Results are discussed within the framework of the subprofile scaling model (SSM) for analyzing patterns of regional covariation. It is demonstrated that the ability to extract measures of group discrimination and disease progression are unaffected by (1) limited improvements in image resolution, (2) the use of transmission scan smoothing, (3) the application of a scatter deconvolution correction, and (4) converting region-of-interest measurements of counts per voxel to measurements of regional CMRglc. This "robustness" of the SSM approach is partly due to the extraction of disease-related subject weights, independent of any subject's global scaling effects. It is argued that other analysis techniques that initially reduce intersubject variation (e.g., using regional ratios or normalizing by global metabolic rates before applying traditional multivariate procedures) lack analytic features that may be important to identify multidimensional, disease-related image patterns. Based on the ADC patient data, it is concluded that measures of group discrimination and disease progression will not necessarily benefit from the organization of parameters traditionally associated with improved absolute quantitation.

Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. "Functional connectivity" of the human motor system studied with [15O]water PET.

Using [15O]water PET and a previously well studied motor activation task, repetitive finger-to-thumb opposition, we compared the spatial activation patterns produced by (1) global normalization and intersubject averaging of paired-image subtractions, (2) the mean differences of ANCOVA-adjusted voxels in Statistical Parametric Mapping, (3) ANCOVA-adjusted voxels followed by principal component analysis (PCA), (4) ANCOVA-adjustment of mean image volumes (mean over subjects at each time point) followed by F-masking and PCA, and (5) PCA with Scaled Subprofile Model pre- and postprocessing. All data analysis techniques identified large positive focal activations in the contralateral sensorimotor cortex and ipsilateral cerebellar cortex, with varying levels of activation in other parts of the motor system, e.g., supplementary motor area, thalamus, putamen; techniques 1-4 also produced extensive negative areas. The activation signal of interest constitutes a very small fraction of the total nonrandom signal in the original dataset, and the exact choice of data preprocessing steps together with a particular analysis procedure have a significant impact on the identification and relative levels of activated regions. The challenge for the future is to identify those preprocessing algorithms and data analysis models that reproducibly optimize the identification and quantification of higher-order sensorimotor and cognitive responses.

Improved resolution for PET volume imaging through three-dimensional iterative reconstruction.

UNLABELLED: It has been demonstrated that in two-dimensional iterative reconstruction, a resolution model can improve image resolution while controlling noise. With the lower noise levels of three-dimensional PET volume imaging, these iterative reconstruction advantages may be extended to three dimensions to further improve the reconstructed image resolution. METHODS: We have implemented three-dimensional versions of iterative filtered backprojection (IFBP) and the maximum likelihood by expectation maximization (ML-EM) reconstruction algorithms and applied them to three-dimensional PET volume datasets. The results were compared to images obtained using the standard three-dimensional reprojection reconstruction (3DRP) algorithm. RESULTS: For IFBP with 15 iterations and no regularization compared to 3DRP, both using a ramp filter, the transaxial resolution improved 52%, and the axial resolution improved 39%. With a strong regularization, the transaxial and axial resolution improvements were reduced to 6% and 5%, respectively. If a Hanning roll-off is applied to the ramp filter in the transaxial direction, the transaxial resolution for IFBP without regularization improved 35% compared to 3DRP; with regularization the improvement dropped to 19%. The axial resolution for IFBP and 3DRP was unaffected by this transaxial smoothing in the reconstruction filter. With the same Hanning roll-off, the noise for IFBP without regularization increased by a factor of 6 compared to 3DRP; with regularization the noise was increased only by a factor of 3. Compared to IFBP, the three-dimensional ML-EM reconstruction produced similar resolution improvements with a much smaller increase in noise and slower convergence. Resolution improvements from both IFBP and ML-EM reconstructions are visually apparent in three-dimensional FDG brain images and result in increased activation signals in a three-dimensional [15O]water functional activation study. CONCLUSION: Our results demonstrate that resolution improvement is possible for IFBP and ML-EM compared to 3DRP with or without noise increase.

Comparison of voxel- and volume-of-interest-based analyses in FDG PET scans of HIV positive and healthy individuals.

UNLABELLED: Abnormal glucose metabolic patterns in the brain have been reported for HIV-1 seropositive (HIV+) patients when compared with seronegative healthy individuals. The metabolic covariance pattern obtained from voxel- or volume-of-interest (VOI)-based multivariate data analysis techniques can be used to characterize disease and potentially to detect and monitor disease progression in the early stage of AIDS dementia complex. However, the arbitrary smoothing typically applied to PET data during reconstruction and processing to reduce noise has an unknown effect on the data, especially for the voxel-based analysis. METHODS: To investigate the impact of smoothing on a discrimination task, we applied principal component analysis with scaled subprofile-model preprocessing (SSM/PCA) followed by Fisher discriminant analysis to FDG PET data that were reconstructed and processed with different degrees of smoothing. Receiver operating characteristic curves were used to compare the ability of derived metabolic covariance patterns to discriminate HIV+ patients from healthy volunteers. RESULTS: For the voxel-based analysis, we found that the spatial distribution of voxel weights from the SSM/PCA analysis suggested edge effects along major tissue and cerebrospinal fluid boundaries, indicative of a disease-specific pattern of cerebral atrophy for the HIV+ patients. In terms of its discrimination performance, this metabolic covariance pattern is stable and insensitive to a wide range of smoothing kernels, except for ramp reconstruction and Hanning reconstruction with 7 x 7 x 7 block smoothing. In these reconstructions, the discrimination performance decreased as a result of high image noise and excessive smoothing, respectively. Our results also indicated that if sufficient variance from the VOI measurements is included, the overall performance of a conventional VOI-based analysis can be similar to that of the voxel-based analysis for the same discrimination task. However, the VOI-based analysis performed poorly at low false-positive fraction and is less tolerant to noise in the metabolic covariance pattern than the voxel-based analysis. CONCLUSION: We have obtained a unique covariance pattern of brain glucose metabolism for HIV+ patients compared with healthy volunteers. Discrimination based on this covariance pattern was found to be insensitive to a wide range of image smoothness.

Iterative algebraic reconstruction algorithms for emission computed tomography: a unified framework and its application to positron emission tomography.

In this paper, a unified framework of iterative algebraic reconstruction for emission computed tomography (ECT) and its application to positron emission tomography (PET) is presented. The unified framework is based on an algebraic image restoration model and contains conventional iterative algebraic reconstruction algorithms: ART, SIRT, Landweber iteration (LWB), the generalized Landweber iteration (GLWB), the steepest descent method (STP), as well as iterative filtered backprojection (IFBP) reconstruction algorithms: Chang's method, Walters' method, and a modified iterative MAP. The framework provides an effective tool to systematically study conventional iterative algebraic algorithms and IFBP algorithms. Based on this framework, conventional iterative algebraic algorithms and IFBP algorithms are generalized. It is shown from the algebraic point of view that IFBP algorithms are not only excellent methods for correction of attenuation (either uniform or nonuniform) but are also good general iterative reconstruction algorithms (they can be applied to either attenuated or attenuation-free projections and converge very fast). The convergence behavior of iterative algebraic algorithms is discussed and insight is drawn into the fast convergence property of IFBP algorithms. A simulated PET system is used to evaluate IFBP algorithms and LWB in comparison with the maximum likelihood estimation via expectation maximization algorithm (MLE-EM) and the filtered backprojection (FBP) algorithm. The simulation results indicate that for both attenuation-free projection and attenuated projection cases IFBP algorithms have a significant computational advantage over LWB and MLE-EM, and have performance advantages over FBP in terms of contrast recovery and/or noise-to-signal ratios (NSRs) in regions of interest.

Comparison of matched BOLD and FAIR 4.0T-fMRI with [15O]water PET brain volumes.

Valid comparisons of functional activation volumes from fMRI and PET require accurate registration, matched spatial resolution, and if possible matched noise. We coregistered 4.0T-fMRI and PET volumes, using a series of linear and nonlinear transformations applied to the PET volumes. Because of the limited number of fMRI slices that were available, PET volumes were transformed to the fMRI space. Since 4.0T-fMRI and 4.0T-MRI volumes have significant spatial distortion due to magnet inhomogeneities, high resolution 1.5T-MRI volumes were nonlinearly transformed to 4.0T-MRI volumes as part of the transformation chain. The smoothing effects of these registration transformations were measured, in order to match the spatial resolution of the coregistered fMRI and PET volumes. Spatial resolution of the transformed PET volumes in the fMRI space was degraded by up to 60% due to the transformation process. Due to both the image acquisition characteristics and the coregistration process, the transformed PET volumes had a spatial resolution that was lower than that of tMRI. Therefore, significant smoothing of fMRI volumes was necessary to match their spatial resolution with that of the transformed PET volumes. Matching the spatial resolution of the fMRI volumes to those of the transformed PET volumes was achieved by matching the shape of their point spread functions. In order to do this, Gaussian kernels were employed to smooth the fMRI volumes. We were unable to simultaneously match the resolution and noise of fMRI and PET signals in the motor cortex. Activation maps derived from transformed PET and smoothed fMRI volumes were compared. Contralateral motor cortex was active in all modalities but there were large variations in the size of the activated region and its signal to noise ratio across BOLD, FAIR, and PET images within each subject. Nevertheless, the relative CBF changes measured by FAIR were consistent with those determined by PET.

Practical tradeoffs between noise, quantitation, and number of iterations for maximum likelihood-based reconstructions.

Emission computerised tomography images reconstructed using a maximum likelihood-expectation maximization (ML)-based method with different reconstruction kernels and 1-200 iterations are compared to images reconstructed using filtered backprojection (FBP). ML-based reconstructions using a single pixel (SP) kernel with or without a sieve filter show no quantitative advantage over FBP except in the background where a reduction of noise is possible if the number of iterations is kept small (<50). ML-based reconstructions using a Gaussian kernel with a multipixel full-width-at-half-maximum (FWHM) and a large number of iterations (200) require a sieve filtering step to reduce the noise and contrast overshoot in the final images. These images have some small quantitative advantages over FBP depending on the structures being imaged. It is demonstrated that a feasibility stopping criterion controls the noise in a reconstructed image, but is insensitive to quantitation errors, and that the use of an appropriate overrelaxation parameter can accelerate the convergence of the ML-based method during the iterative process without quantitative instabilities.

Evaluating performance of reconstruction algorithms for 3-D [15O] water PET using subtraction analysis.

Positron emission tomography (PET) [15O] activation studies have benefited significantly from three-dimensional (3-D) data acquisition. However, they have been slow to take advantage of new 3-D reconstruction techniques. Compared with the widely used 3-D reprojection reconstruction (3DRP), the advantage of signal and noise for iterative algorithms has been outweighed by concern about long and complicated reconstruction procedures and inconsistent performance. Most pseudo-3-D algorithms, such as rebinning methods, aim at increasing the speed of reconstruction but lack further resolution improvement or noise control. Although many evaluations have been conducted through simulations and phantom experiments, the spatially varying nature of signal and noise and the complexity of biological effects have complicated the interpretation of real data based on simulation or phantom results. We have taken a different approach and used the analysis of real data directly as a measure with which to compare three reconstruction algorithms: 3DRP, iterative filtered backprojection with median root prior (IFBP-MRP), and Fourier rebinning followed by two-dimensional (2-D) filtered backprojection (FORE-FBP) for [15O] PET. Two subjects, each with 32 scans acquired in four sessions during a finger opposition motor task, are analyzed using subtraction. A fixed volume-of-interest (VOI) measurement in regions related to the task demonstrates that at high resolution, IFBP-MRP has the best signal-to-noise performance followed by 3DRP and FORE-FBP; however, this advantage gradually diminishes as the resolution decreases. For a voxel measurement derived from the image of each reconstruction, all three algorithms are capable of detecting highly activated regions. Although there are some differences in the size, shape, and center location of the activated foci, our preliminary results suggest that IFBP-MRP does offer enhanced signal with some noise control compared with 3DRP for the analysis of high-resolution images. If images are to be analyzed at an intermediate to lower resolution, FORE-FBP provides a significant reduction of reconstruction time compared with 3DRP.

The convergence of object dependent resolution in maximum likelihood based tomographic image reconstruction.

Study of the maximum likelihood by EM algorithm (ML) with a reconstruction kernel equal to the intrinsic detector resolution and sieve regularization has demonstrated that any image improvements over filtered backprojection (FBP) are a function of image resolution. Comparing different reconstruction algorithms potentially requires measuring and matching the image resolution. Since there are no standard methods for describing the resolution of images from a nonlinear algorithm such as ML, we have defined measures of effective local Gaussian resolution (ELGR) and effective global Gaussian resolution (EGGR) and examined their behaviour in FBP images and in ML images using two different measurement techniques. For FBP these two resolution measures are equal and exhibit the standard convolution behaviour of linear systems. For ML, the FWHM of the ELGR monotonically increased with decreasing Gaussian object size due to slower convergence rates for smaller objects. For the simple simulated phantom used, this resolution dependence is independent of object position. With increasing object size, number of iterations and sieve size the object size dependence of the ELGR decreased. The FWHM of the EGGR converged after approximately 200 iterations, masking the fact that the ELGR for small objects was far from convergence. When FBP is compared to a nonlinear algorithm such as ML, it is recommended that at least the EGGR be matched; for ML this requires more than the number of iterations (e.g., < 100) that are typically run to minimize the mean square error or to satisfy a feasibility or similar stopping criterion. For many tasks, matching the EGGR of ML to FBP images may be insufficient and >> 200 iterations may be needed, particularly for small objects in the ML image because their ELGR has not yet converged.

Noise and signal decoupling in maximum-likelihood reconstructions and Metz filters for PET brain images.

Images reconstructed with the maximum-likelihood-by-expectation-maximization (ML) algorithm have lower noise in some regions, particularly low count areas, compared with images reconstructed with filtered backprojection (FBP). The use of statistically correct noise model coupled with the positivity constraint in the ML algorithm provides this noise improvement, but whether this model confers a general advantage for ML over FBP with no noise model and any reconstruction filter, is unclear. We have studied the quantitative impact of the correct noise model in the ML algorithm applied to simulated and real PET fluoro-deoxyglucose (FDG) brain images, given a simplified but accurate reconstruction model with spatially invariant resolution. For FBP reconstruction, several Metz filters were chosen and images with different resolution were obtained depending on the order (1-400) of the Metz filters. Comparisons were made based on the mean Fourier spectra of the projection amplitudes, the noise-power spectra, and the mean region-of-interest signal and noise behaviour in the images. For images with resolution recovery beyond the intrinsic detector resolution, the noise increased significantly for FBP compared with ML. This indicates that in the process of signal recovery using ML, the noise is decoupled from the signal. Such noise decoupling is not possible for FBP. However, for image resolution equivalent to or less than the intrinsic detector resolution, FBP with Metz filters of various orders can achieve a performance similar to ML. The significance of the noise decoupling advantage in ML is dependent on the reconstructed image resolution required for specific imaging tasks.

A system for continuous production and infusion of [15O]H2O for PET activation studies.

A system for continuous production and infusion of [15O]H2O has been designed for Positron Emission Tomography brain activation studies. The infusion system consists of two Horizon Nxt infusion pumps, a four-port-two-position valve and a sterile 50 ml vial. The line and the back check valve between the furnace and the reservoir were heated in order to reduce vapor condensation in the line. The variation of the production of [15O]H2O was < 1%. The activity delivered as measured by scanner counts varied < 2% during the steady state period. The system has been demonstrated to be capable of delivering activity over a wide range of conditions.

Quantitative comparisons of image registration techniques based on high-resolution MRI of the brain.

OBJECTIVE: A variety of methods for matching intrasubject MRI-MRI, PET-PET, or MRI-PET image pairs have been proposed. Based on the rigid body transformations needed to align pairs of high-resolution MRI scans and/or simulated PET scans (derived from these MRI scans), we obtained general comparisons of four intrasubject image registration techniques: Talairach coordinates, head and hat, equivalent internal points, and ratio image uniformity. In addition, we obtained a comparison of stereotaxic Z frames with a customized head mold for MRI-MRI image pairs. MATERIALS AND METHODS AND RESULTS: Each technique was quantitatively evaluated using the mean and maximum voxel registration errors for matched voxel pairs within the brain volumes being registered. CONCLUSION: We conclude that fiducial markers such as stereotaxic Z frames that are not rigidly fixed to a patient's skull are inaccurate compared with other registration techniques, Talairach coordinate transformations provide surprisingly good registration, and minimizing the variance of MRI-MRI, PET-PET, or MRI-PET ratio images provides significantly better registration than all other techniques tested. Registration optimization based on measurement of the similarity of spatial distributions of voxel values is superior to techniques that do not use such information.

Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects.

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms with different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscientific application, we evaluated the performance of six NUC algorithms. We used simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of the nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in order to relate differences in algorithmic performance to intersubject variability and/or differences in scanner performance. In phantom studies, the correlation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial (top-to-bottom) direction. Two of the six algorithms demonstrated a high degree of stability, as measured by the iterative application of the algorithm to its corrected output. While none of the algorithms performed ideally under all circumstances, locally adaptive methods generally outperformed nonadaptive methods.