Use of Laughter for the Detection of Parkinson's Disease: Feasibility Study for Clinical Decision Support Systems, Based on Speech Recognition and Automatic Classification Techniques.

Parkinson's disease (PD) is an incurable neurodegenerative disorder which affects over 10 million people worldwide. Early detection and correct evaluation of the disease is critical for appropriate medication and to slow the advance of the symptoms. In this scenario, it is critical to develop clinical decision support systems contributing to an early, efficient, and reliable diagnosis of this illness. In this paper we present a feasibility study for a clinical decision support system for the diagnosis of PD based on the acoustic characteristics of laughter. Our decision support system is based on laugh analysis with speech recognition methods and automatic classification techniques. We evaluated different cepstral coefficients to identify laugh characteristics of healthy and ill subjects combined with machine learning classification models. The decision support system reached 83% accuracy rate with an AUC value of 0.86 for PD-healthy laughs classification in a database of 20,000 samples randomly generated from a pool of 120 laughs from healthy and PD subjects. Laughter could be employed for the efficient and reliable detection of PD; such a detection system can be achieved using speech recognition and automatic classification techniques; a clinical decision support system can be built using the above techniques. Significance: PD clinical decision support systems for the early detection of the disease will help to improve the efficiency of available and upcoming therapeutic treatments which, in turn, would improve life conditions of the affected people and would decrease costs and efforts in public and private healthcare systems.

Geometrical and Monte Carlo projectors in 3D PET reconstruction.

PURPOSE: In the present work, the authors compare geometrical and Monte Carlo projectors in detail. The geometrical projectors considered were the conventional geometrical Siddon ray-tracer (S-RT) and the orthogonal distance-based ray-tracer (OD-RT), based on computing the orthogonal distance from the center of image voxel to the line-of-response. A comparison of these geometrical projectors was performed using different point spread function (PSF) models. The Monte Carlo-based method under consideration involves an extensive model of the system response matrix based on Monte Carlo simulations and is computed off-line and stored on disk. METHODS: Comparisons were performed using simulated and experimental data of the commercial small animal PET scanner rPET. RESULTS: The results demonstrate that the orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions yield better images in terms of contrast and spatial resolution than those obtained after using the conventional method and the multiray-based S-RT. Furthermore, the Monte Carlo-based method yields slight improvements in terms of contrast and spatial resolution with respect to these geometrical projectors. CONCLUSIONS: The orthogonal distance-based ray-tracer and Siddon ray-tracer using PSF image-space convolutions represent satisfactory alternatives to factorizing the system matrix or to the conventional on-the-fly ray-tracing methods for list-mode reconstruction, where an extensive modeling based on Monte Carlo simulations is unfeasible.

Studying the properties of the updating coefficients in the OSEM algorithm for iterative image reconstruction in PET.

We have studied the properties of the pixel updating coefficients in the 2D ordered subsets expectation maximization (OSEM) algorithm for iterative image reconstruction in positron emission tomography, in order to address the problem of image quality degradation-a known property of the technique after a number of iterations. The behavior of the updating coefficients has been extensively analyzed on synthetic coincidence data, using the necessary software tools. The experiments showed that the statistical properties of these coefficients can be correlated with the quality of the reconstructed images as a function of the activity distribution in the source and the number of subsets used. Considering the fact that these properties can be quantified during the reconstruction process of data from real scans where the activity distribution in the source is unknown the results of this study might be useful for the development of a stopping criterion for the OSEM algorithm.

PET image reconstruction: A stopping rule for the MLEM algorithm based on properties of the updating coefficients.

An empirical stopping criterion for the 2D-maximum-likelihood expectation-maximization (MLEM) iterative image reconstruction algorithm in positron emission tomography (PET) has been proposed. We have applied the MLEM algorithm on Monte Carlo generated noise-free projection data and studied the properties of the pixel updating coefficients (PUC) in the reconstructed images. Appropriate fitting lead to an analytical expression for the parameterization of the minimum value in the PUC vector for all non-zero pixels for a given number of detected counts, which can be employed as basis for the stopping criterion proposed. These results have been validated with simulated data from real PET images.

Analysis and interpretation of dynamic FDG PET oncological studies using data reduction techniques.

BACKGROUND: Dynamic positron emission tomography studies produce a large amount of image data, from which clinically useful parametric information can be extracted using tracer kinetic methods. Data reduction methods can facilitate the initial interpretation and visual analysis of these large image sequences and at the same time can preserve important information and allow for basic feature characterization. METHODS: We have applied principal component analysis to provide high-contrast parametric image sets of lower dimensions than the original data set separating structures based on their kinetic characteristics. Our method has the potential to constitute an alternative quantification method, independent of any kinetic model, and is particularly useful when the retrieval of the arterial input function is complicated. In independent component analysis images, structures that have different kinetic characteristics are assigned opposite values, and are readily discriminated. Furthermore, novel similarity mapping techniques are proposed, which can summarize in a single image the temporal properties of the entire image sequence according to a reference region. RESULTS: Using our new cubed sum coefficient similarity measure, we have shown that structures with similar time activity curves can be identified, thus facilitating the detection of lesions that are not easily discriminated using the conventional method employing standardized uptake values.

Blind source separation for the computational analysis of dynamic oncological PET studies.

The analysis of dynamic positron emission tomography (PET) studies provides clinically useful parametric information, but often requires complex and time-consuming compartmental or non-compartmental techniques. Independent component analysis (ICA), a statistical method used for feature extraction and signal separation, is applied to dynamic PET studies to facilitate the initial interpretation and visual analysis of these large image sequences. ICA produces parametric images, where structures with different kinetic characteristics are assigned opposite values and readily discriminated, improving the identification of lesions and facilitating the posterior detailed kinetic analysis.

Integrated telemedicine applications and services for oncological positron emission tomography.

TENPET (Trans European Network for Positron Emission Tomography) aims to evaluate the provision of integrated teleconsultation and intelligent computer supported cooperative work services for clinical positron emission tomography (PET) in Europe at its current stage, as it is a multi-centre project financially supported by the European Commission (Information Society, eTEN Program). It addresses technological challenges by linking PET centres and developing supporting services that permit remote consultation between professionals in the field. The technological platform (CE-marked) runs on Win2000/NT/XP systems and incorporates advanced techniques for image visualization, analysis and fusion, as well as for interactive communication and message handling for off-line communications. Four PET Centres from Spain, France and Germany participate to the pilot system trials. The performance evaluation of the system is carried out via log files and user-filled questionnaires on the frequency of the teleconsultations, their duration and efficacy, quality of the images received, user satisfaction, as well as on privacy, ethical and security issues. TENPET promotes the co-operation and improved communication between PET practitioners that are miles away from their peers or on mobile units, offering options for second opinion and training and permitting physicians to remotely consult patient data if they are away from their centre. It is expected that TENPET will have a significant impact in the development of new skills by PET professionals and will support the establishment of peripheral PET units. To our knowledge, TENPET is the first telemedicine service specifically designed for oncological PET. This report presents the technical innovations incorporated in the TENPET platform and the initial pilot studies at real and diverse clinical environments in the field of oncology.

Performance evaluation of principal component analysis in dynamic FDG-PET studies of recurrent colorectal cancer.

Performance evaluation of principal component analysis (PCA) of dynamic F-18-FDG-PET studies of patients with recurrent colorectal cancer. Principal component images (PCI) of 17 iteratively reconstructed data sets were visually and quantitatively evaluated. The F-18-FDG compartment model parameters were estimated using polynomial regression. All structures were present in PCI1. PCI2 was correlated with the vascular component and PCI3 with the tumor. The vessel density in the tumor was estimated with a correlation coefficient equal to 0.834. PCA supports the visual interpretation of dynamic F-18-FDG-PET studies, facilitates the application of compartment modeling and is a promising quantification technique.

Iterative image reconstruction for clinical PET using ordered subsets, median root prior, and a web-based interface.

PURPOSE: The development, implementation and validation of simple, flexible and efficient iterative image reconstruction (IIR) methods for their take-up in routine clinical positron emission tomography (PET) static or dynamic studies. PROCEDURES: The ordered subsets (OS) technique applied for the acceleration of the maximum likelihood expectation maximization (MLEM) IIR algorithm is here extended to include the weighted least-squares (WLS), image space reconstruction algorithm (ISRA) and the space alternating generalized EM (SAGE). The median root prior (MRP) has been successfully applied as a Bayesian regularization to control the noise level in the reconstructed images. All methods are implemented on distributed Pentium systems and tested using simulated PET data from a brain phantom. A Javascript is used for the initiation of the reconstruction. RESULTS: Taking into consideration the image quality and the time required for the reconstruction, the MRP-OSEM (ordered subsets expectation maximization) seems to provide best results after four to eight iterations, with four subsets and a MRP coefficient of 0.2-0.4. Iterative reconstruction of the transmission images with OS-acceleration and MRP regularization with subsequent calculation of the attenuation correction factors (ACFs) is shown to effectively remove streak artifacts in the emission images, especially along paths of high attenuation. CONCLUSIONS: An efficient implementation using distributed processing principles and a web-based interface allows the reconstruction of one frame (with 63 cross-section slices) from a dynamic determination in few minutes. This work showed that regular PC systems can provide fast execution and produce results in clinically meaningful times. This eradicates the argument of the computational burden of the method that prevented the extensive use of IIR in today's modern PET systems.

Performance characteristics of iterative image reconstruction techniques for routine use in positron emission tomography

The iterative image reconstruction (IIR) is a promising approach to achieve a better image quality in PET. However, limitations exist with respect to the required computation time and the influence of reconstruction parameters on quantitative PET data. We implemented different reconstruction algorithms in a PC based reconstruction program and evaluated the effect of the reconstruction algorithms as well as reconstruction parameters on the quantitative PET results. The following IIR algorithms were implemented: maximum likelihood expectation maximization (LMEM), weighted least squares (WLS), image space reconstruction algorithm (ISRA), space alternating generalized expectation maximization (SAGE). The ordered subsets (OS) method and the median root prior (MRP) correction were provided and can be used in combination with each reconstruction algorithm. A dynamic PET study, showing small liver metastases, was used for the evaluation of the properties of the reconstruction parameters. Regions-of-Interest (ROI) were placed in a small high uptake area as well as in a larger low uptake region for quantification purpose using standardized uptake values (SUV). The 128x128 image matrix was generally not suffient to detect the metastases as separate lesions and a 256x256 matrix was required for the delineation of the lesions. Furthermore, the use of the iterative attenuation correction improved the image quality significantly...