Multiresolution analysis of emission tomography images in the wavelet domain.
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TLDR
New theory and algorithms are presented that allow current wavelet methodology to deal with the two main characteristics of nuclear medicine images: low signal-to-noise ratios and correlated noise.Abstract:
This article develops a theoretical framework for the use of the wavelet transform in the estimation of emission tomography images. The solution of the problem of estimation addresses the equivalent problems of optimal filtering, maximum compression, and statistical testing. In particular, new theory and algorithms are presented that allow current wavelet methodology to deal with the two main characteristics of nuclear medicine images: low signal-to-noise ratios and correlated noise. The technique is applied to synthetic images, phantom studies, and clinical images. Results show the ability of wavelets to model images and to estimate the signal generated by cameras of different resolutions in a wide variety of noise conditions. Moreover, the same methodology can be used for the multiscale analysis of statistical maps. The relationship of the wavelet approach to current hypothesis-testing methods is shown with an example and discussed. The wavelet transform is shown to be a valuable tool for the numerical treatment of images in nuclear medicine. It is envisaged that the methods described here may be a starting point for further developments in image reconstruction and image processing.read more
Citations
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Colored noise and computational inference in neurophysiological (fMRI) time series analysis: Resampling methods in time and wavelet domains
Edward T. Bullmore,Edward T. Bullmore,Christopher J. Long,John Suckling,Jalal M. Fadili,Gemma A. Calvert,Fernando Zelaya,T. Adrian Carpenter,Mick Brammer +8 more
TL;DR: It is concluded that wavelet resampling may be a generally useful method for inference on naturally complex time series based on random permutation after orthogonal transformation of the observed time series to the wavelet domain.
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Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing
TL;DR: A general wavelet-based denoising scheme for functional magnetic resonance imaging (fMRI) data is presented and the results show that the methods that produce smooth images introduce more false positives than Gaussian smoothing or wave let-based methods with a large smoothing effect.
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Abnormal Frontostriatal Interactions in People With Prodromal Signs of Psychosis: A Multimodal Imaging Study
Paolo Fusar-Poli,Oliver D. Howes,Paul Allen,Matthew R. Broome,Isabel Valli,Marie-Claude Asselin,Paul M. Grasby,Philip McGuire +7 more
TL;DR: In people with prodromal signs of psychosis, there are direct correlations between altered prefrontal cortical function and subcortical dopamine synthesis capacity, consistent with the notion that frontostriatal interactions play a critical role in the pathoetiology of schizophrenia.
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Presynaptic Striatal Dopamine Dysfunction in People at Ultra-high Risk for Psychosis: Findings in a Second Cohort
Alice Egerton,Christopher A. Chaddock,Christopher A. Chaddock,Toby T. Winton-Brown,Toby T. Winton-Brown,Michael A P Bloomfield,Sagnik Bhattacharyya,Sagnik Bhattacharyya,Paul Allen,Paul Allen,Philip McGuire,Philip McGuire,Oliver D. Howes,Oliver D. Howes,Oliver D. Howes +14 more
TL;DR: The findings indicate that elevated dopamine synthesis capacity in the dorsal striatum is a robust feature of individuals at UHR for psychosis and provide further evidence that dopaminergic abnormalities precede the onset of psychosis.
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Abnormal prefrontal activation directly related to pre-synaptic striatal dopamine dysfunction in people at clinical high risk for psychosis.
Paolo Fusar-Poli,Oliver D. Howes,Oliver D. Howes,Paul Allen,Matthew R. Broome,Isabel Valli,M-C Asselin,Andrew J. Montgomery,Paul M. Grasby,Philip McGuire +9 more
TL;DR: Altered prefrontal activation in subjects with an At-Risk Mental State for psychosis is related to elevated striatal dopamine function, which reflects an increased vulnerability to psychosis and predate the first episode of frank psychosis.
References
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A theory for multiresolution signal decomposition: the wavelet representation
TL;DR: In this paper, it is shown that the difference of information between the approximation of a signal at the resolutions 2/sup j+1/ and 2 /sup j/ (where j is an integer) can be extracted by decomposing this signal on a wavelet orthonormal basis of L/sup 2/(R/sup n/), the vector space of measurable, square-integrable n-dimensional functions.
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TL;DR: This paper presents a meta-analyses of the wavelet transforms of Coxeter’s inequality and its applications to multiresolutional analysis and orthonormal bases.
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Ten Lectures on Wavelets
TL;DR: In this article, the regularity of compactly supported wavelets and symmetry of wavelet bases are discussed. But the authors focus on the orthonormal bases of wavelets, rather than the continuous wavelet transform.
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Statistical parametric maps in functional imaging: A general linear approach
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TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
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