Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection

Accurate pulmonary nodule detection is a crucial step in lung cancer screening. Computer-aided detection (CAD) systems are not routinely used by radiologists for pulmonary nodule detection in clinical practice despite their potential benefits. Maximum intensity projection (MIP) images improve the detection of pulmonary nodules in radiological evaluation with computed tomography (CT) scans. Inspired by the clinical methodology of radiologists, we aim to explore the feasibility of applying MIP images to improve the effectiveness of automatic lung nodule detection using convolutional neural networks (CNNs). We propose a CNN-based approach that takes MIP images of different slab thicknesses (5 mm, 10 mm, 15 mm) and 1 mm axial section slices as input. Such an approach augments the two-dimensional (2-D) CT slice images with more representative spatial information that helps discriminate nodules from vessels through their morphologies. Our proposed method achieves sensitivity of 92.67% with 1 false positive per scan and sensitivity of 94.19% with 2 false positives per scan for lung nodule detection on 888 scans in the LIDC-IDRI dataset. The use of thick MIP images helps the detection of small pulmonary nodules (3 mm-10 mm) and results in fewer false positives. Experimental results show that utilizing MIP images can increase the sensitivity and lower the number of false positives, which demonstrates the effectiveness and significance of the proposed MIP-based CNNs framework for automatic pulmonary nodule detection in CT scans. The proposed method also shows the potential that CNNs could gain benefits for nodule detection by combining the clinical procedure.Comment: Submitted to IEEE TM

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

Parallel MRI is a fast imaging technique that enables the acquisition of highly resolved images in space or/and in time. The performance of parallel imaging strongly depends on the reconstruction algorithm, which can proceed either in the original k-space (GRAPPA, SMASH) or in the image domain (SENSE-like methods). To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated. In this paper, we extend this approach using 3D-wavelet representations in order to handle all slices together and address reconstruction artifacts which propagate across adjacent slices. The gain induced by such extension (3D-Unconstrained Wavelet Regularized -SENSE: 3D-UWR-SENSE) is validated on anatomical image reconstruction where no temporal acquisition is considered. Another important extension accounts for temporal correlations that exist between successive scans in functional MRI (fMRI). In addition to the case of 2D+t acquisition schemes addressed by some other methods like kt-FOCUSS, our approach allows us to deal with 3D+t acquisition schemes which are widely used in neuroimaging. The resulting 3D-UWR-SENSE and 4D-UWR-SENSE reconstruction schemes are fully unsupervised in the sense that all regularization parameters are estimated in the maximum likelihood sense on a reference scan. The gain induced by such extensions is illustrated on both anatomical and functional image reconstruction, and also measured in terms of statistical sensitivity for the 4D-UWR-SENSE approach during a fast event-related fMRI protocol. Our 4D-UWR-SENSE algorithm outperforms the SENSE reconstruction at the subject and group levels (15 subjects) for different contrasts of interest (eg, motor or computation tasks) and using different parallel acceleration factors (R=2 and R=4) on 2x2x3mm3 EPI images.Comment: arXiv admin note: substantial text overlap with arXiv:1103.353

Quantification of the density of cooperative neighboring synapses required to evoke endocannabinoid signaling

The spatial pattern of synapse activation may impact on synaptic plasticity. This applies to the synaptically-evoked endocannabinoid-mediated short-term depression at the parallel fiber (PF) to Purkinje cell synapse, the occurrence of which requires close proximity between the activated synapses. Here, we determine quantitatively this required proximity, helped by the geometrical organization of the cerebellar molecular layer. Transgenic mice expressing a calcium indicator selectively in granule cells enabled the imaging of action potential-evoked presynaptic calcium rise in isolated, single PFs. This measurement was used to derive the number of PFs activated within a beam of PFs stimulated in the molecular layer, from which the density of activated PFs (input density) was calculated. This density was on average 2.8μm in sagittal slices and twice more in transverse slices. The synaptically-evoked endocannabinoid-mediated suppression of excitation (SSE) evoked by ten stimuli at 200Hz was determined from the monitoring of either postsynaptic responses or presynaptic calcium rise. The SSE was significantly larger when recorded in transverse slices, where the input density is larger. The exponential description of the SSE plotted as a function of the input density suggests that the SSE is half reduced when the input density decreases from 6 to 2μm. We conclude that, although all PFs are truncated in an acute sagittal slice, half of them remain respondent to stimulation, and activated synapses need to be closer than 1.5μm to synergize in endocannabinoid signaling. © 2013 The authors

Separation of Parallel Encoded Complex-Valued Slices (SPECS) From A Single Complex-Valued Aliased Coil Image

Purpose Achieving a reduction in scan time with minimal inter-slice signal leakage is one of the significant obstacles in parallel MR imaging. In fMRI, multiband-imaging techniques accelerate data acquisition by simultaneously magnetizing the spatial frequency spectrum of multiple slices. The SPECS model eliminates the consequential inter-slice signal leakage from the slice unaliasing, while maintaining an optimal reduction in scan time and activation statistics in fMRI studies. Materials and Methods When the combined k-space array is inverse Fourier reconstructed, the resulting aliased image is separated into the un-aliased slices through a least squares estimator. Without the additional spatial information from a phased array of receiver coils, slice separation in SPECS is accomplished with acquired aliased images in shifted FOV aliasing pattern, and a bootstrapping approach of incorporating reference calibration images in an orthogonal Hadamard pattern. Result The aliased slices are effectively separated with minimal expense to the spatial and temporal resolution. Functional activation is observed in the motor cortex, as the number of aliased slices is increased, in a bilateral finger tapping fMRI experiment. Conclusion The SPECS model incorporates calibration reference images together with coefficients of orthogonal polynomials into an un-aliasing estimator to achieve separated images, with virtually no residual artifacts and functional activation detection in separated images

The dynamics of single spike-evoked adenosine release in the cerebellum

The purine adenosine is a potent neuromodulator in the brain, with roles in a number of diverse physiological and pathological processes. Modulators such as adenosine are difficult to study as once released they have a diffuse action (which can affect many neurones) and, unlike classical neurotransmitters, have no inotropic receptors. Thus rapid postsynaptic currents (PSCs) mediated by adenosine (equivalent to mPSCs) are not available for study. As a result the mechanisms and properties of adenosine release still remain relatively unclear. We have studied adenosine release evoked by stimulating the parallel fibres in the cerebellum. Using adenosine biosensors combined with deconvolution analysis and mathematical modelling, we have characterised the release dynamics and diffusion of adenosine in unprecedented detail. By partially blocking K+ channels, we were able to release adenosine in response to a single stimulus rather than a train of stimuli. This allowed reliable sub-second release of reproducible quantities of adenosine with stereotypic concentration waveforms that agreed well with predictions of a mathematical model of purine diffusion. We found no evidence for ATP release and thus suggest that adenosine is directly released in response to parallel fibre firing and does not arise from extracellular ATP metabolism. Adenosine release events showed novel short-term dynamics, including facilitated release with paired stimuli at millisecond stimulation intervals but depletion-recovery dynamics with paired stimuli delivered over minute time scales. These results demonstrate rich dynamics for adenosine release that are placed, for the first time, on a quantitative footing and show strong similarity with vesicular exocytosis

Gated Recurrent Neural Tensor Network

Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling temporal and sequential data need to capture long-term dependencies on datasets and represent them in hidden layers with a powerful model to capture more information from inputs. For modeling long-term dependencies in a dataset, the gating mechanism concept can help RNNs remember and forget previous information. Representing the hidden layers of an RNN with more expressive operations (i.e., tensor products) helps it learn a more complex relationship between the current input and the previous hidden layer information. These ideas can generally improve RNN performances. In this paper, we proposed a novel RNN architecture that combine the concepts of gating mechanism and the tensor product into a single model. By combining these two concepts into a single RNN, our proposed models learn long-term dependencies by modeling with gating units and obtain more expressive and direct interaction between input and hidden layers using a tensor product on 3-dimensional array (tensor) weight parameters. We use Long Short Term Memory (LSTM) RNN and Gated Recurrent Unit (GRU) RNN and combine them with a tensor product inside their formulations. Our proposed RNNs, which are called a Long-Short Term Memory Recurrent Neural Tensor Network (LSTMRNTN) and Gated Recurrent Unit Recurrent Neural Tensor Network (GRURNTN), are made by combining the LSTM and GRU RNN models with the tensor product. We conducted experiments with our proposed models on word-level and character-level language modeling tasks and revealed that our proposed models significantly improved their performance compared to our baseline models.Comment: Accepted at IJCNN 2016 URL : http://ieeexplore.ieee.org/document/7727233

Brain Mechanisms Supporting the Modulation of Pain by Mindfulness Meditation

The subjective experience of one’s environment is constructed by interactions among sensory, cognitive, and affective processes. For centuries, meditation has been thought to influence such processes by enabling a nonevaluative representation of sensory events. To better understand how meditation influences the sensory experience, we used arterial spin labeling functional magnetic resonance imaging to assess the neural mechanisms by which mindfulness meditation influences pain in healthy human participants. After 4 d of mindfulness meditation training, meditating in the presence of noxious stimulation significantly reduced pain unpleasantness by 57% and pain intensity ratings by 40% when compared to rest. A two-factor repeated-measures ANOVA was used to identify interactions between meditation and pain-related brain activation. Meditation reduced pain-related activation of the contralateral primary somatosensory cortex. Multiple regression analysis was used to identify brain regions associated with individual differences in the magnitude of meditation-related pain reductions. Meditation-induced reductions in pain intensity ratings were associated with increased activity in the anterior cingulate cortex and anterior insula, areas involved in the cognitive regulation of nociceptive processing. Reductions in pain unpleasantness ratings were associated with orbitofrontal cortex activation, an area implicated in reframing the contextual evaluation of sensory events. Moreover, reductions in pain unpleasantness also were associated with thalamic deactivation, which may reflect a limbic gating mechanism involved in modifying interactions between afferent input and executive-order brain areas. Together, these data indicate that meditation engages multiple brain mechanisms that alter the construction of the subjectively available pain experience from afferent information

Introduction to fMRI: experimental design and data analysis

This provides an introduction to functional MRI, experimental design and data analysis procedures using statistical parametric mapping approach