Combining dual-tree complex wavelets and multiresolution in iterative CT reconstruction with application to metal artifact reduction.

BACKGROUND: This paper investigates the benefits of data filtering via complex dual wavelet transform for metal artifact reduction (MAR). The advantage of using complex dual wavelet basis for MAR was studied on simulated dental computed tomography (CT) data for its efficiency in terms of noise suppression and removal of secondary artifacts. Dual-tree complex wavelet transform (DT-CWT) was selected due to its enhanced directional analysis of image details compared to the ordinary wavelet transform. DT-CWT was used for multiresolution decomposition within a modified total variation (TV) regularized inversion algorithm. METHODS: In this study, we have tested the multiresolution TV (MRTV) approach with DT-CWT on a 2D polychromatic jaw phantom model with Gaussian and Poisson noise. High noise and sparse measurement settings were used to assess the performance of DT-CWT. The results were compared to the outcome of the single-resolution reconstruction and filtered back-projection (FBP) techniques as well as reconstructions with Haar wavelet basis. RESULTS: The results indicate that filtering of wavelet coefficients with DT-CWT effectively removes the noise without introducing new artifacts after inpainting. Furthermore, adoption of multiple resolution levels yield to a more robust algorithm compared to varying the regularization strength. CONCLUSIONS: The multiresolution reconstruction with DT-CWT is also more robust when reconstructing the data with sparse projections compared to the single-resolution approach and Haar wavelets.

A Bayesian approach for suppression of limited angular sampling artifacts in single particle 3D reconstruction.

In the single particle reconstruction, the initial 3D structure often suffers from the limited angular sampling artifact. Selecting 2D class averages of particle images generally improves the accuracy and efficiency of the reference-free 3D angle estimation, but causes an insufficient angular sampling to fill the information of the target object in the 3D frequency space. Similarly, the initial 3D structure by the random-conical tilt reconstruction has the well-known "missing cone" artifact. Here, we attempted to solve the limited angular sampling problem by sequentially applying maximum a posteriori estimate with expectation maximization algorithm (sMAP-EM). Using both simulated and experimental cryo-electron microscope images, the sMAP-EM was compared to the direct Fourier method on the basis of reconstruction error and resolution. To establish selection criteria of the final regularization weight for the sMAP-EM, the effects of noise level and sampling sparseness on the reconstructions were examined with evenly distributed sampling simulations. The frequency information filled in the missing cone of the conical tilt sampling simulations was assessed by developing new quantitative measurements. All the results of visual and numerical evaluations showed the sMAP-EM performed better than the direct Fourier method, regardless of the sampling method, noise level, and sampling sparseness. Furthermore, the frequency domain analysis demonstrated that the sMAP-EM can fill the meaningful information in the unmeasured angular space without detailed a priori knowledge of the objects. The current research demonstrated that the sMAP-EM has a high potential to facilitate the determination of 3D protein structures at near atomic-resolution.

Automatic FRAP analysis with inhomogeneous fluorescence distribution and movement compensation.

The analysis of fluorescence recovery after photobleaching (FRAP) data is complicated by the measurement noise, inhomogeneous fluorescence distribution, and image movement during experiment. Conventionally, these issues are tackled by data preprocessing and averaging, which causes loss of quantitative properties. In this study, we present a method which automatically estimates and compensates both the movement and inhomogeneous fluorescence distribution within the data analysis. The method is based on modeling the raw FRAP data with a parametric matrix and searching for maximum likelihood parameters between the model and the data. The developed method also automatically estimates also the bleach profile, immobile fraction, and noise variance. Suitable numerical computational method was developed and implemented in a computer grid. Simulated and experimental FRAP data was created and analyzed to evaluate the method.

Evaluation of the automatic three-dimensional delineation of caudate and putamen for PET receptor occupancy studies.

OBJECTIVE: PET receptor occupancy studies with a baseline study and an intervention study are increasingly used as an aid in dose-finding procedures for central nervous system drug development. The aim of this study was to evaluate, and confirm the feasibility of two automatic, paired, three-dimensional delineation methods of striatal structures (caudate and putamen) for the purposes of PET receptor occupancy studies. METHODS: The automatic extraction was done with the deformable surface models from PET binding potential images. The segmentation result of the baseline study was utilized for segmenting the intervention study. The methods were evaluated with Monte Carlo simulated phantom images, a test-retest dataset with 11C-raclopride, and two receptor occupancy datasets (11C-raclopride and 18F-SPA-RQ). With the test-retest dataset, the reproducibility (normalized absolute differences (NAD)) and reliability (intraclass correlation coefficient (ICC)) of binding potential values were assessed with the proposed methods designed specifically for receptor occupancy and compared with the manual segmentation. A similar comparison was also performed for the receptor occupancy estimates. RESULTS: In the test-retest dataset, the two automatic three-dimensional segmentation methods had mean NADs ranging from 2.44 to 5.11% and ICCs from 0.88 to 0.98. The corresponding values for the manual two-dimensional segmentation were 3.45 to 6.65% and 0.82 to 0.96 (NAD differences non-significant). The automatic methods also yielded similar receptor occupancy values to the manual segmentation method in a range of high and low occupancy values. CONCLUSION: We believe that the automated three-dimensional segmentation will be useful in enhancing the analysis of large-scale PET receptor occupancy studies with high-resolution scanners.

Voxel-based NK1 receptor occupancy measurements with [(18)F]SPA-RQ and positron emission tomography: a procedure for assessing errors from image reconstruction and physiological modeling.

PURPOSE: Receptor occupancy studies with positron emission tomography (PET) are widely used as aids in the drug development process. This study introduces a general procedure for assessing errors that arise from the applied image processing methods in PET receptor occupancy studies using the neurokinin-1 (NK1) receptor occupancy study as an example. PROCEDURES: The bias and variance among eight combinations of image reconstruction and model calculation methods for estimating voxel-level receptor occupancy results were examined. The tests were performed using a dynamic numerical phantom based on a previous PET drug occupancy study with the NK1 receptor antagonist tracer [(18)F]SPA-RQ. RESULTS: The simplified reference tissue model with basis functions (SRTM BF) was best at estimating receptor occupancy in terms of average bias. On the other hand, median root prior (MRP) image reconstruction produced the lowest variances in the occupancy estimates. These results suggest that SRTM BF and MRP is, in this case, the combination of choice in voxel-based receptor occupancy calculation. In the calculation of regional binding potential values, the commonly used sample mean is not applicable and, e.g., the median could be used instead. CONCLUSIONS: This study shows that even this kind of complicated receptor study can be statistically evaluated. The reconstruction methods had an effect on the variance in the voxel-based receptor occupancy calculation. The model calculation methods influenced the average bias. The test method was found useful in assessing the methodological sources of systematic and random error in receptor occupancy estimation with PET.

Alignment of 3-dimensional cardiac structures in O-15-labeled water PET emission images with mutual information.

BACKGROUND: The aim of this study was to develop a method to correct the heart position between two oxygen 15-labeled water cardiac positron emission tomography (PET) image sets to be able to use the equivalent regions of interest for the quantification of the perfusion values in the same myocardial segments. METHODS AND RESULTS: Independent component analysis was applied to the dynamic image sets (simulated phantom and 6 rest-pharmacologic stress and 10 rest-rest image sets of healthy female volunteers) acquired at different time points to separate the cardiac structures (ventricles and myocardium). The separated component images from independent component analysis from the 2 studies of the same individual were aligned with a normalized mutual information-based registration method. The alignment parameters were applied to position the regions of interest in the floating image sets for calculation of the myocardial blood flow values. In the rest case the mean myocardial blood flow value was 0.76 +/- 0.12 mL x g(-1) x min(-1) for the manual method and 0.79 +/- 0.10 mL x g(-1) x min(-1) for the proposed method (by use of the right ventricle component in the alignment), and in the stress case these values were 3.39 +/- 0.70 mL x g(-1) x min(-1) and 4.01 +/- 0.71 mL x g(-1) x min(-1), respectively. No statistically significant difference was found between the methods. CONCLUSION: In the tests with the phantom and patient images the alignment of cardiac structures was shown to be successful. The alignment could be done without the use of information from the myocardial compartment.

Genetic algorithms for finite mixture model based voxel classification in neuroimaging.

Finite mixture models (FMMs) are an indispensable tool for unsupervised classification in brain imaging. Fitting an FMM to the data leads to a complex optimization problem. This optimization problem is difficult to solve by standard local optimization methods, such as the expectation-maximization (EM) algorithm, if a principled initialization is not available. In this paper, we propose a new global optimization algorithm for the FMM parameter estimation problem, which is based on real coded genetic algorithms. Our specific contributions are two-fold: 1) we propose to use blended crossover in order to reduce the premature convergence problem to its minimum and 2) we introduce a completely new permutation operator specifically meant for the FMM parameter estimation. In addition to improving the optimization results, the permutation operator allows for imposing biologically meaningful constraints to the FMM parameter values. We also introduce a hybrid of the genetic algorithm and the EM algorithm for efficient solution of multidimensional FMM fitting problems. We compare our algorithm to the self-annealing EM-algorithm and a standard real coded genetic algorithm with the voxel classification tasks within the brain imaging. The algorithms are tested on synthetic data as well as real three-dimensional image data from human magnetic resonance imaging, positron emission tomography, and mouse brain MRI. The tissue classification results by our method are shown to be consistently more reliable and accurate than with the competing parameter estimation methods.

Robust estimation of bioaffinity assay fluorescence signals.

In this paper, the challenging problem of robust mean-signal estimation of a single-step microparticle bioaffinity assay is investigated. For this purpose, a density estimation-based robust algorithm (DER) was developed. The DER algorithm was comparatively evaluated with four other parameter estimation methods (mean value, median filtering, least square estimation, Welsch robust m-estimator). Two important questions were raised and investigated: 1) Which of the five methods can robustly estimate the mean bioaffinity signal? and 2) How many microparticles need to be measured in order to obtain an accurate estimate of the mean signal value? To answer the questions, bootstrap and coefficient of variation (CV) analyses were performed. In the CV analysis, the DER algorithm gave the best results: The CV ranged from 0.8% to 4.9% when the number of microparticles used for the mean signal estimation varied from 800 to 30. In the bootstrap analysis of the standard error, the DER algorithm had the smallest variance. As a conclusion, it can be underlined that: 1) of all methods tested, the DER algorithm gave the most consistent and reproducible results according to the bootstrap and CV analysis; 2) using the DER algorithm accurate estimates could be calculated based on 80-100 particles, corresponding to a typical assay measurement time of 1 min; and 3) the investigated bioaffinity signals contained a large number of outliers (observations that severely deviate from the majority of data) and therefore robust techniques were necessary for the mean signal estimation tasks.

Adaptive multiresolution method for MAP reconstruction in electron tomography.

3D image reconstruction with electron tomography holds problems due to the severely limited range of projection angles and low signal to noise ratio of the acquired projection images. The maximum a posteriori (MAP) reconstruction methods have been successful in compensating for the missing information and suppressing noise with their intrinsic regularization techniques. There are two major problems in MAP reconstruction methods: (1) selection of the regularization parameter that controls the balance between the data fidelity and the prior information, and (2) long computation time. One aim of this study is to provide an adaptive solution to the regularization parameter selection problem without having additional knowledge about the imaging environment and the sample. The other aim is to realize the reconstruction using sequences of resolution levels to shorten the computation time. The reconstructions were analyzed in terms of accuracy and computational efficiency using a simulated biological phantom and publically available experimental datasets of electron tomography. The numerical and visual evaluations of the experiments show that the adaptive multiresolution method can provide more accurate results than the weighted back projection (WBP), simultaneous iterative reconstruction technique (SIRT), and sequential MAP expectation maximization (sMAPEM) method. The method is superior to sMAPEM also in terms of computation time and usability since it can reconstruct 3D images significantly faster without requiring any parameter to be set by the user.

Comparison of manual and automatic techniques for substriatal segmentation in 11C-raclopride high-resolution PET studies.

BACKGROUND: The striatum is the primary target in regional C-raclopride-PET studies, and despite its small volume, it contains several functional and anatomical subregions. The outcome of the quantitative dopamine receptor study using C-raclopride-PET depends heavily on the quality of the region-of-interest (ROI) definition of these subregions. The aim of this study was to evaluate subregional analysis techniques because new approaches have emerged, but have not yet been compared directly. MATERIALS AND METHODS: In this paper, we compared manual ROI delineation with several automatic methods. The automatic methods used either direct clustering of the PET image or individualization of chosen brain atlases on the basis of MRI or PET image normalization. State-of-the-art normalization methods and atlases were applied, including those provided in the FreeSurfer, Statistical Parametric Mapping8, and FSL software packages. Evaluation of the automatic methods was based on voxel-wise congruity with the manual delineations and the test-retest variability and reliability of the outcome measures using data from seven healthy male participants who were scanned twice with C-raclopride-PET on the same day. RESULTS: The results show that both manual and automatic methods can be used to define striatal subregions. Although most of the methods performed well with respect to the test-retest variability and reliability of binding potential, the smallest average test-retest variability and SEM were obtained using a connectivity-based atlas and PET normalization (test-retest variability=4.5%, SEM=0.17). CONCLUSION: The current state-of-the-art automatic ROI methods can be considered good alternatives for subjective and laborious manual segmentation in C-raclopride-PET studies.

Automatic extraction of brain surface and mid-sagittal plane from PET images applying deformable models.

In this study, we propose and evaluate new methods for automatic extraction of the brain surface and the mid-sagittal plane from functional positron emission tomography (PET) images. Designing methods for these segmentation tasks is challenging because the spatial distribution of intensity values in a PET image depends on the applied radiopharmaceutical and the contrast to noise ratio in a PET image is typically low. We extracted the brain surface with a deformable model which is based on a global optimization algorithm. The global optimization allows reliable automation of the extraction task. Based on the extracted brain surface, the mid-sagittal plane was determined. The method was tested with the image of the Hoffman brain phantom (FDG) and the images from the brain studies with the FDG (17 images) and the C11-Raclopride tracers (4 images). In addition to the brain surfaces, we applied the deformable model for extraction of the coarse cortical structure based on the tracer uptake from FDG-PET brain images. The proposed segmentation methods provide a promising direction for automatic processing and analysis of PET brain images.

Neuroscience data and tool sharing: a legal and policy framework for neuroinformatics.

The requirements for neuroinformatics to make a significant impact on neuroscience are not simply technical--the hardware, software, and protocols for collaborative research--they also include the legal and policy frameworks within which projects operate. This is not least because the creation of large collaborative scientific databases amplifies the complicated interactions between proprietary, for-profit R&D and public "open science." In this paper, we draw on experiences from the field of genomics to examine some of the likely consequences of these interactions in neuroscience. Facilitating the widespread sharing of data and tools for neuroscientific research will accelerate the development of neuroinformatics. We propose approaches to overcome the cultural and legal barriers that have slowed these developments to date. We also draw on legal strategies employed by the Free Software community, in suggesting frameworks neuroinformatics might adopt to reinforce the role of public-science databases, and propose a mechanism for identifying and allowing "open science" uses for data whilst still permitting flexible licensing for secondary commercial research.

Generalization of median root prior reconstruction.

Penalized iterative algorithms for image reconstruction in emission tomography contain conditions on which kind of images are accepted as solutions. The penalty term has commonly been a function of pairwise pixel differences in the activity in a local neighborhood, such that smooth images are favored. Attempts to ensure better edge and detail preservation involve difficult tailoring of parameter values or the penalty function itself. The previously introduced median root prior (MRP) favors locally monotonic images. MRP preserves sharp edges while reducing locally nonmonotonic noise at the same time. Quantitative properties of MRP are good, because differences in the neighboring pixel values are not penalized as such. The median is used as an estimate for a penalty reference, against which the pixel value is compared when setting the penalty. In order to generalize the class of MRP-type of priors, the standard median was replaced by other order statistic operations, the L and finite-impluse-response median hybrid (FMH) filters. They allow for smoother appearance as they apply linear weighting together with robust nonlinear operations. The images reconstructed using the new MRP-L and MRP-FMH priors are visually more conventional. Good quantitative properties of MRP are not significantly altered by the new priors.

Accelerated median root prior reconstruction for pinhole single-photon emission tomography (SPET).

Pinhole collimation can be used to improve spatial resolution in SPET. However, the resolution improvement is achieved at the cost of reduced sensitivity, which leads to projection images with poor statistics. Images reconstructed from these projections using the maximum likelihood expectation maximization (ML-EM) algorithms, which have been used to reduce the artefacts generated by the filtered backprojection (FBP) based reconstruction, suffer from noise/bias trade-off: noise contaminates the images at high iteration numbers, whereas early abortion of the algorithm produces images that are excessively smooth and biased towards the initial estimate of the algorithm. To limit the noise accumulation we propose the use of the pinhole median root prior (PH-MRP) reconstruction algorithm. MRP is a Bayesian reconstruction method that has already been used in PET imaging and shown to possess good noise reduction and edge preservation properties. In this study the PH-MRP algorithm was accelerated with the ordered subsets (OS) procedure and compared to the FBP, OS-EM and conventional Bayesian reconstruction methods in terms of noise reduction, quantitative accuracy, edge preservation and visual quality. The results showed that the accelerated PH-MRP algorithm was very robust. It provided visually pleasing images with lower noise level than the FBP or OS-EM and with smaller bias and sharper edges than the conventional Bayesian methods.

Neuroinformatics: the integration of shared databases and tools towards integrative neuroscience.

There is significant interest amongst neuroscientists in sharing neuroscience data and analytical tools. The exchange of neuroscience data and tools between groups affords the opportunity to differently re-analyze previously collected data, encourage new neuroscience interpretations and foster otherwise uninitiated collaborations, and provide a framework for the further development of theoretically based models of brain function. Data sharing will ultimately reduce experimental and analytical error. Many small Internet accessible database initiatives have been developed and specialized analytical software and modeling tools are distributed within different fields of neuroscience. However, in addition large-scale international collaborations are required which involve new mechanisms of coordination and funding. Provided sufficient government support is given to such international initiatives, sharing of neuroscience data and tools can play a pivotal role in human brain research and lead to innovations in neuroscience, informatics and treatment of brain disorders. These innovations will enable application of theoretical modeling techniques to enhance our understanding of the integrative aspects of neuroscience. This article, authored by a multinational working group on neuroinformatics established by the Organization for Economic Co-operation and Development (OECD), articulates some of the challenges and lessons learned to date in efforts to achieve international collaborative neuroscience.

Delineation of brain structures from positron emission tomography images with deformable models.

Segmentation of positron emission tomography (PET) images is a difficult task. In this study, we propose a new method for delineation of brain structures according to the tracer uptake. The method is based on a new deformable model which is particularly designed for extracting surfaces automatically from noisy images. The automation is achieved by using a global optimization algorithm for minimizing the energy of the deformable model. As an example, the coarse cortical structure was extracted from FDG PET brain images by delineating first the brain surface and then the white matter surface. We have tested the method with the image of the brain phantom and images from a small number (N = 17) of FDG brain studies. The cortical structure was automatically and reliably found from all the images. The proposed method provides new opportunities for automatic and repeatable structure extraction applicable for regional quantification of the tracer uptake.

Evaluation of analytical reconstruction with a new gap-filling method in comparison to iterative reconstruction in [¹¹C]-raclopride PET studies.

AIM: The aim of the study was (1) to evaluate the reconstruction strategies with dynamic [¹¹C]-raclopride human positron emission tomography (PET) studies acquired from ECAT high-resolution research tomograph (HRRT) scanner and (2) to justify for the selected gap-filling method for analytical reconstruction with simulated phantom data. METHODS: A new transradial bicubic interpolation method has been implemented to enable faster analytical 3D-reprojection (3DRP) reconstructions for the ECAT HRRT PET scanner data. The transradial bicubic interpolation method was compared to the other gap-filling methods visually and quantitatively using the numerical Shepp-Logan phantom. The performance of the analytical 3DRP reconstruction method with this new gap-filling method was evaluated in comparison with the iterative statistical methods: ordinary Poisson ordered subsets expectation maximization (OPOSEM) and resolution modeled OPOSEM methods. The image reconstruction strategies were evaluated using human data at different count statistics and consequently at different noise levels. In the assessments, 14 [¹¹C]-raclopride dynamic PET studies (test-retest studies of 7 healthy subjects) acquired from the HRRT PET scanner were used. Besides the visual comparisons of the methods, we performed regional quantitative evaluations over the cerebellum, caudate and putamen structures. We compared the regional time-activity curves (TACs), areas under the TACs and binding potential (BPND) values. RESULTS AND CONCLUSIONS: The results showed that the new gap-filling method preserves the linearity of the 3DRP method. Results with the 3DRP after gap-filling method exhibited hardly any dependency on the count statistics (noise levels) in the sinograms while we observed changes in the quantitative results with the EM-based methods for different noise contamination in the data. With this study, we showed that 3DRP with transradial bicubic gap-filling method is feasible for the reconstruction of high-resolution PET data with missing sinogram bins. The calculated intraclass correlation coefficient (ICC) values were similar for all tested methods and validated the test-retest study. The gap-filling and 3DRP method can be used for quantitative PET studies in which high temporal information is crucial and can serve as a reference method for comparison studies of the other reconstruction methods.

Compensation of missing wedge effects with sequential statistical reconstruction in electron tomography.

Electron tomography (ET) of biological samples is used to study the organization and the structure of the whole cell and subcellular complexes in great detail. However, projections cannot be acquired over full tilt angle range with biological samples in electron microscopy. ET image reconstruction can be considered an ill-posed problem because of this missing information. This results in artifacts, seen as the loss of three-dimensional (3D) resolution in the reconstructed images. The goal of this study was to achieve isotropic resolution with a statistical reconstruction method, sequential maximum a posteriori expectation maximization (sMAP-EM), using no prior morphological knowledge about the specimen. The missing wedge effects on sMAP-EM were examined with a synthetic cell phantom to assess the effects of noise. An experimental dataset of a multivesicular body was evaluated with a number of gold particles. An ellipsoid fitting based method was developed to realize the quantitative measures elongation and contrast in an automated, objective, and reliable way. The method statistically evaluates the sub-volumes containing gold particles randomly located in various parts of the whole volume, thus giving information about the robustness of the volume reconstruction. The quantitative results were also compared with reconstructions made with widely-used weighted backprojection and simultaneous iterative reconstruction technique methods. The results showed that the proposed sMAP-EM method significantly suppresses the effects of the missing information producing isotropic resolution. Furthermore, this method improves the contrast ratio, enhancing the applicability of further automatic and semi-automatic analysis. These improvements in ET reconstruction by sMAP-EM enable analysis of subcellular structures with higher three-dimensional resolution and contrast than conventional methods.

Automatic statistical shape analysis of cerebral asymmetry in 3D T1-weighted magnetic resonance images at vertex-level: application to neuroleptic-naïve schizophrenia.

The study of the structural asymmetries in the human brain can assist the early diagnosis and progression of various neuropsychiatric disorders, and give insights into the biological bases of several cognitive deficits. The high inter-subject variability in cortical morphology complicates the detection of abnormal asymmetries especially if only small samples are available. This work introduces a novel automatic method for the local (vertex-level) statistical shape analysis of gross cerebral hemispheric surface asymmetries which is robust to the individual cortical variations. After segmentation of the cerebral hemispheric volumes from three-dimensional (3D) T1-weighted magnetic resonance images (MRI) and their spatial normalization to a common space, the right hemispheric masks were reflected to match with the left ones. Cerebral hemispheric surfaces were extracted using a deformable model-based algorithm which extracted the salient morphological features while establishing the point correspondence between the surfaces. The interhemispheric asymmetry, quantified by customized measures of asymmetry, was evaluated in a few thousands of corresponding surface vertices and tested for statistical significance. The developed method was tested on scans obtained from a small sample of healthy volunteers and first-episode neuroleptic-naïve schizophrenics. A significant main effect of the disease on the local interhemispheric asymmetry was observed, both in females and males, at the frontal and temporal lobes, the latter being often linked to the cognitive, auditory, and memory deficits in schizophrenia. The findings of this study, although need further testing in larger samples, partially replicate previous studies supporting the hypothesis of schizophrenia as a neurodevelopmental disorder.

F-18 fluorodeoxyglucose uptake and water-perfusable tissue fraction in assessment of myocardial viability.

OBJECTIVES: (15)O-water-perfusable tissue fraction (PTF) has been shown to be a potential index for assessing myocardial viability in PET, an alternative to (18)F-fluorodeoxyglucose (FDG). This study aimed to directly compare these two independent methods in assessing myocardial viability in patients with abnormal wall motion. METHODS: PET study was performed on 16 patients with previous myocardial infarction, before coronary artery bypass graft operation (CABG). The protocol included a (15)O-carbonmonoxide static, a (15)O-water dynamic and an (18)F-FDG dynamic scan, during the euglycemic hyperinsulinemic clamp. Echocardiography was performed at the time of PET and 5-12 months after the CABG, and the wall motion recovery was evaluated on segmental and global bases. Consistency between PTF and (18)F-FDG was evaluated visually and also in a quantitative manner. Predictive values for the wall motion recovery were also compared between the two approaches. RESULTS: The image quality of (18)F-FDG was superior to that of (15)O-water. The qualitative PTF showed significantly smaller defects than (18)F-FDG, and the quantitative PTF showed slightly greater values than (18)F-FDG in the infarcted region. The two methods were, however, consistent visually and also quantitatively. The predictive values of the wall motion recovery were almost equal between the two approaches. The absolute (18)F-FDG uptake was varied in normal segments, and predictive values for the wall motion recovery by the absolute (18)F-FDG was less (accuracy: 80 %) compared with those by the relative (18)F-FDG (accuracy: 87 %) and the quantitative PTF (accuracy: 89 %). CONCLUSION: Despite the small sample size, PTF appears to give consistent results with the (18)F-FDG approach, and might be an alternative viability assessment.