Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Is there a neuroanatomical basis of the vulnerability to suicidal behavior?: a coordinate-based meta-analysis of structural and functional MRI studies

Objective: We conducted meta-analyses of functional and structural neuroimaging studies comparing adolescent and adult individuals with a history of suicidal behavior and a psychiatric disorder to psychiatric controls in order to objectify changes in brain structure and function in association with a vulnerability to suicidal behavior. Methods: Magnetic resonance imaging studies published up to July 2013 investigating structural or functional brain correlates of suicidal behavior were identified through computerized and manual literature searches. Activation foci from 12 studies encompassing 475 individuals, i.e., 213 suicide attempters and 262 psychiatric controls were subjected to meta-analytical study using anatomic or activation likelihood estimation (ALE). Result: Activation likelihood estimation revealed structural deficits and functional changes in association with a history of suicidal behavior. Structural findings included reduced volumes of the rectal gyrus, superior temporal gyrus and caudate nucleus. Functional differences between study groups included an increased reactivity of the anterior and posterior cingulate cortices. Discussion: A history of suicidal behavior appears to be associated with (probably interrelated) structural deficits and functional overactivation in brain areas, which contribute to a decision-making network. The findings suggest that a vulnerability to suicidal behavior can be defined in terms of a reduced motivational control over the intentional behavioral reaction to salient negative stimuli

Scatter modelling and compensation in emission tomography

In nuclear medicine, clinical assessment and diagnosis are generally based on qualitative assessment of the distribution pattern of radiotracers used. In addition, emission tomography (SPECT and PET) imaging methods offer the possibility of quantitative assessment of tracer concentration in vivo to quantify relevant parameters in clinical and research settings, provided accurate correction for the physical degrading factors (e.g. attenuation, scatter, partial volume effects) hampering their quantitative accuracy are applied. This review addresses the problem of Compton scattering as the dominant photon interaction phenomenon in emission tomography and discusses its impact on both the quality of reconstructed clinical images and the accuracy of quantitative analysis. After a general introduction, there is a section in which scatter modelling in uniform and non-uniform media is described in detail. This is followed by an overview of scatter compensation techniques and evaluation strategies used for the assessment of these correction methods. In the process, emphasis is placed on the clinical impact of image degradation due to Compton scattering. This, in turn, stresses the need for implementation of more accurate algorithms in software supplied by scanner manufacturers, although the choice of a general-purpose algorithm or algorithms may be difficul

The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

EMDR therapy for PTSD after motor vehicle accidents: meta-analytic evidence for specific treatment

Motor vehicle accident (MVA) victims may suffer both acute and post-traumatic stress disorders (PTSD). With PTSD affecting social, interpersonal and occupational functioning, clinicians as well as the National Institute of Health are very interested in identifying the most effective psychological treatment to reduce PTSD. From research findings, eye movement desensitization and reprocessing (EMDR) therapy is considered as one of the effective treatment of PTSD. In this paper, we present the results of a meta-analysis of fMRI studies on PTSD after MVA through activation likelihood estimation. We found that PTSD following MVA is characterized by neural modifications in the anterior cingulate cortex (ACC), a cerebral structure involved in fear-conditioning mechanisms. Basing on previous findings in both humans and animals, which demonstrate that desensitization techniques and extinction protocols act on the limbic system, the effectiveness of EMDR and of cognitive behavioral therapies (CBT) may be related to the fact that during these therapies the ACC is stimulated by desensitization

Conservative and disruptive modes of adolescent change in human brain functional connectivity

Adolescent changes in human brain function are not entirely understood. Here, we used multiecho functional MRI (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in 298 healthy adolescents scanned 520 times. Participants were aged 14 to 26 y and were scanned on 1 to 3 occasions at least 6 mo apart. We found 2 distinct modes of age-related change in FC: “conservative” and “disruptive.” Conservative development was characteristic of primary cortex, which was strongly connected at 14 y and became even more connected in the period from 14 to 26 y. Disruptive development was characteristic of association cortex and subcortical regions, where connectivity was remodeled: connections that were weak at 14 y became stronger during adolescence, and connections that were strong at 14 y became weaker. These modes of development were quantified using the maturational index (MI), estimated as Spearman’s correlation between edgewise baseline FC (at 14 y, FC14) and adolescent change in FC (ΔFC14−26), at each region. Disruptive systems (with negative MI) were activated by social cognition and autobiographical memory tasks in prior fMRI data and significantly colocated with prior maps of aerobic glycolysis (AG), AG-related gene expression, postnatal cortical surface expansion, and adolescent shrinkage of cortical thickness. The presence of these 2 modes of development was robust to numerous sensitivity analyses. We conclude that human brain organization is disrupted during adolescence by remodeling of FC between association cortical and subcortical areas

Improving quantification in non-TOF 3D PET/MR by incorporating photon energy information

Hybrid PET/MR systems combine functional information obtained from positron emission tomography (PET) and anatomical information from magnetic resonance (MR) imaging. In spite of the advantages that such systems can offer, PET attenuation correction still represents one of the biggest challenges for imaging in the thorax. This is due to the fact that the MR signal is not directly correlated to gamma-photon attenuation. In current practice, pre-defined population-based attenuation values are used. However, this approach is prone to errors in tissues such as the lung where a variability of attenuation values can be found both within and between patients. A way to overcome this limitation is to exploit the fact that stand-alone PET emission data contain information on both the distribution of the radiotracer and photon attenuation. However, attempts to estimate the attenuation map from emission data only have shown limited success unless time-of-flight PET data is available. Several groups have investigated the possibility of using scattered data as an additional source of information to overcome re- construction ambiguities. This thesis presents work to extend the previous methods by using PET emission data acquired at multiple energy windows and incorporating prior information derived from MR. This thesis is organised as follows. We first cover both the literature and mathematical theory relevant to the framework. Then, we present and discuss results on the case of attenu- ation estimation from scattered data only, when the activity distribution is known. We then give an overview of several candidates for joint reconstruction, which reconstruct both the activity and attenuation from scattered and unscattered data. We present extensive results using simulated data and compare the proposed methods to state-of-the-art MLAA from a single energy window acquisition. We conclude with suggestions for future work to bring the proposed method into clinical practice