Pawel J. Markiewicz

Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimer's disease.

For finite and noisy samples extraction of robust features or patterns which are representative of the population is a formidable task in which over-interpretation is not uncommon. In this work, resampling techniques have been applied to a sample of 42 FDG PET brain images of 19 healthy volunteers (HVs) and 23 Alzheimers disease (AD) patients to assess the robustness of image features extracted through principal component analysis (PCA) and Fisher discriminant analysis (FDA). The objective of this work is to: 1) determine the relative variance described by the PCA to the population variance; 2) assess the robustness of the PCA to the population sample using the largest principal angle between PCA subspaces; 3) assess the robustness and accuracy of the FDA. Since the sample does not have histopathological data the impact of possible clinical misdiagnosis on the discrimination analysis is investigated. The PCA can describe up to 40% of the total population variability. Not more than the first three or four PCs can be regarded as robust on which a robust FDA can be build. Standard error images showed that regions close to the falx and around ventricles are less stable. Using the first three PCs, sensitivity and specificity were 90.5% and 96.9% respectively. The use of resampling techniques in the evaluation of the robustness of many multivariate image analysis methods enables researchers to avoid over-analysis when using these methods applied to many different neuroimaging studies often with small sample sizes.

CSF total and phosphorylated tau protein, regional glucose metabolism and dementia severity in Alzheimer’s disease

Background and purpose:  We investigated associations between severity of cognitive impairment, cerebrospinal fluid (CSF) concentrations of total‐tau (t‐tau) protein and tau phosphorylated at threonin 181 (p‐tau181) and regional glucose metabolism measured with 18F‐fluorodeoxyglucose‐positron emission tomography (18F‐FDG‐PET) in patients with probable Alzheimer’s disease (AD).

Direct reconstruction of parametric images using any spatiotemporal 4D image based model and maximum likelihood expectation maximisation

Direct application of the expectation maximisation (EM) algorithm to the spatiotemporal maximum likelihood problem results in a convenient separation of the image based problem from the projection based problem. This enables any spatiotemporal 4D image model to be incorporated into MLEM image reconstruction with relative ease, only requiring tailored calculation of the fitting weights. As a preliminary example, assessment using direct estimation of spectral analysis coefficients is presented, exploiting an image based non-negative least squares algorithm, where a specially-weighted least squares update is equivalent to the required update towards the maximum likelihood estimate. The proposed approach demonstrates a reduced root mean square error (RMSE) in the estimates of volume of distribution. Future work will include the exploration of alternative spatiotemporal models.

A custom-built PET phantom design for quantitative imaging of printed distributions

This note presents a practical approach to a custom-made design of PET phantoms enabling the use of digital radioactive distributions with high quantitative accuracy and spatial resolution. The phantom design allows planar sources of any radioactivity distribution to be imaged in transaxial and axial (sagittal or coronal) planes. Although the design presented here is specially adapted to the high-resolution research tomograph (HRRT), the presented methods can be adapted to almost any PET scanner. Although the presented phantom design has many advantages, a number of practical issues had to be overcome such as positioning of the printed source, calibration, uniformity and reproducibility of printing. A well counter (WC) was used in the calibration procedure to find the nonlinear relationship between digital voxel intensities and the actual measured radioactive concentrations. Repeated printing together with WC measurements and computed radiography (CR) using phosphor imaging plates (IP) were used to evaluate the reproducibility and uniformity of such printing. Results show satisfactory printing uniformity and reproducibility; however, calibration is dependent on the printing mode and the physical state of the cartridge. As a demonstration of the utility of using printed phantoms, the image resolution and quantitative accuracy of reconstructed HRRT images are assessed. There is very good quantitative agreement in the calibration procedure between HRRT, CR and WC measurements. However, the high resolution of CR and its quantitative accuracy supported by WC measurements made it possible to show the degraded resolution of HRRT brain images caused by the partial-volume effect and the limits of iterative image reconstruction.

Optimized data preprocessing for multivariate analysis applied to 99mTc-ECD SPECT data sets of Alzheimer's patients and asymptomatic controls

Multivariate image analysis has shown potential for classification between Alzheimers disease (AD) patients and healthy controls with a high-diagnostic performance. As image analysis of positron emission tomography (PET) and single photon emission computed tomography (SPECT) data critically depends on appropriate data preprocessing, the focus of this work is to investigate the impact of data preprocessing on the outcome of the analysis, and to identify an optimal data preprocessing method. In this work, technetium-99methylcysteinatedimer (99mTc-ECD) SPECT data sets of 28 AD patients and 28 asymptomatic controls were used for the analysis. For a series of different data preprocessing methods, which includes methods for spatial normalization, smoothing, and intensity normalization, multivariate image analysis based on principal component analysis (PCA) and Fisher discriminant analysis (FDA) was applied. Bootstrap resampling was used to investigate the robustness of the analysis and the classification accuracy, depending on the data preprocessing method. Depending on the combination of preprocessing methods, significant differences regarding the classification accuracy were observed. For 99mTc-ECD SPECT data, the optimal data preprocessing method in terms of robustness and classification accuracy is based on affine registration, smoothing with a Gaussian of 12 mm full width half maximum, and intensity normalization based on the 25% brightest voxels within the whole-brain region.

High accuracy multiple scatter modelling for 3D whole body PET

A new technique for modelling multiple-order Compton scatter which uses the absolute probabilities relating the image space to the projection space in 3D whole body PET is presented. The details considered in this work give a valuable insight into the scatter problem, particularly for multiple scatter. Such modelling is advantageous for large attenuating media where scatter is a dominant component of the measured data, and where multiple scatter may dominate the total scatter depending on the energy threshold and object size. The model offers distinct features setting it apart from previous research: (1) specification of the scatter distribution for each voxel based on the transmission data, the physics of Compton scattering and the specification of a given PET system; (2) independence from the true activity distribution; (3) in principle no scaling or iterative process is required to find the distribution; (4) explicit multiple scatter modelling; (5) no scatter subtraction or addition to the forward model when included in the system matrix used with statistical image reconstruction methods; (6) adaptability to many different scatter compensation methods from simple and fast to more sophisticated and therefore slower methods; (7) accuracy equivalent to that of a Monte Carlo model. The scatter model has been validated using Monte Carlo simulation (SimSET).

PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets

The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) offers unique possibilities. In this paper we aim to exploit the high spatial resolution of MRI to enhance the reconstruction of simultaneously acquired PET data. We propose a new prior to incorporate structural side information into a maximum a posteriori reconstruction. The new prior combines the strengths of previously proposed priors for the same problem: it is very efficient in guiding the reconstruction at edges available from the side information and it reduces locally to edge-preserving total variation in the degenerate case when no structural information is available. In addition, this prior is segmentation-free, convex and no a priori assumptions are made on the correlation of edge directions of the PET and MRI images. We present results for a simulated brain phantom and for real data acquired by the Siemens Biograph mMR for a hardware phantom and a clinical scan. The results from simulations show that the new prior has a better trade-off between enhancing common anatomical boundaries and preserving unique features than several other priors. Moreover, it has a better mean absolute bias-to-mean standard deviation trade-off and yields reconstructions with superior relative l2-error and structural similarity index. These findings are underpinned by the real data results from a hardware phantom and a clinical patient confirming that the new prior is capable of promoting well-defined anatomical boundaries.

Verification of predicted robustness and accuracy of multivariate analysis

The assessment of accuracy and robustness of multivariate analysis of FDG-PET brain images as presented in [Markiewicz, P.J., Matthews, J.C., Declerck, J., Herholz, K., 2009. Robustness of multivariate image analysis assessed by resampling techniques and applied to FDG-PET scans of patients with Alzheimers disease. Neuroimage 46, 472-485.] using a homogeneous sample (from one centre) of small size is here verified using a heterogeneous sample (from multiple centres) of much larger size. Originally the analysis, which included principal component analysis (PCA) and Fisher discriminant analysis (FDA), was established using a sample of 42 subjects (19 Normal Controls (NCs) and 23 Alzheimers disease (AD) patients) and here the analysis is verified using an independent sample of 166 subjects (86 NCs and 80 ADs) obtained from the ADNI database. It is shown that bootstrap resampling combined with the metric of the largest principal angle between PCA subspaces as well as the deliberate clinical misdiagnosis simulation can predict robustness of the multivariate analysis when used with new datasets. Cross-validation (CV) and the .632 bootstrap overestimated the predictive accuracy encouraging less robust solutions. Also, it is shown that the type of PET scanner and image reconstruction method has an impact on such analysis and affects the accuracy of the verification sample.

Noise Properties of Four Strategies for Incorporation of Scatter and Attenuation Information in PET Reconstruction Using the EM-ML Algorithm

Conventional methods for dealing with attenuation and scatter in PET can limit the reconstructed image quality, particularly if the attenuating medium is large (as in whole body 3D PET). In such cases, often a substantial scatter subtraction is performed followed by amplification of the remaining data (to correct for attenuation) resulting in noisy reconstructions. More recent iterative reconstruction methods include the attenuation in the system model in conjunction with either pre-scatter subtraction or a separate addition of the scatter component after each application of the forward model. This work compares these more conventional approaches of including attenuation and scatter to the case where attenuation and scatter information are both included within the system matrix used by the expectation maximization maximum likelihood (EM-ML) algorithm. For this case all acquired data are used and regarded as a source of information by the reconstruction algorithm. Multiple realisations of simulated data sets have been used to compare the performance of the unified attenuation and scatter model with other methods. For a large attenuating medium and low counts there are notable differences between the four main ways of including attenuation and scatter within the reconstruction-with full pre-correction of the data being inferior compared to all the other methods, and the method which models scatter and attenuation within the system matrix showing some advantages. This work suggests that if regularisation of the EM algorithm is carried out by early termination of the iterative process, the subtraction method is the better approach among the techniques considered. In contrast, if a post-reconstruction smoothing approach to regularisation is to be used (whereby highly iterated, noisy image estimates are smoothed), the full modeling method for attenuation and scatter yields the better results, albeit at the computational cost of many more iterations being required

Impact of erroneous kinetic model formulation in Direct 4D image reconstruction

Direct parametric image reconstruction has the potential to reduce variance in parameter estimates when applied to PET/CT data. One complication when estimating parametric maps in the body is the difficulty of finding one single model to describe all the different kinetics in the field of view (FOV). Contrary to the post-reconstruction kinetic analysis though, any errors (bias) from the discrepancy between the model and the observed kinetics in the direct 4D reconstruction can potentially propagate spatially from unimportant areas to areas of interest. In this work we investigate this effect on simulated 4-D datasets based on a digital body phantom. Different realistic cases were simulated including differential input functions in the FOV and organs with different kinetics. Micro-parameters (K1, k2,Vd, bv) where estimated using a newly proposed spatiotemporal 4D image reconstruction algorithm as well as using post-reconstruction kinetic analysis on noiseless and noisy datasets simulating [15O] H2O kinetics in the body. Bias analysis both in noiseless and noisy data showed a bias from badly modelled areas spatially propagates to other regions of interest in the direct reconstruction. Critically though under noisy conditions even with the bias propagation, the direct reconstruction method still outperforms the conventional post-reconstruction methodology. Nevertheless there is a need to ensure that appropriate models are chosen to describe the kinetics in the entire FOV with approaches such as data-driven adaptive kinetic modelling worth exploring.