Philip J. Bones

Towards direct reconstruction from a gamma camera based on Compton scattering

The Compton scattering camera (sometimes called the electronically collimated camera) has been shown by others to have the potential to better the photon counting statistics and the energy resolution of the Anger camera for imaging in SPECT. By using coincident detection of Compton scattering events on two detecting planes, a photon can be localized to having been sourced on the surface of a cone. New algorithms are needed to achieve fully three-dimensional reconstruction of the source distribution from such a camera. If a complete set of cone-surface projections are collected over an infinitely extending plane, it is shown that the reconstruction problem is not only analytically solvable, but also overspecified in the absence of measurement uncertainties. Two approaches to direct reconstruction are proposed, both based on the photons which travel perpendicularly between the detector planes. Results of computer simulations are presented which demonstrate the ability of the algorithms to achieve useful reconstructions in the absence of measurement uncertainties (other than those caused by quantization). The modifications likely to be required in the presence of realistic measurement uncertainties are discussed.

Detection of epileptiform discharges in the EEG by a hybrid system comprising mimetic, self-organized artificial neural network, and fuzzy logic stages

OBJECTIVE A multi-stage system for automated detection of epileptiform activity in the EEG has been developed and tested on pre-recorded data from 43 patients. METHODS The system is centred on the use of an artificial neural network, known as the self-organising feature map (SOFM), as a novel pattern classifier. The role of the SOFM is to assign a probability value to incoming candidate epileptiform discharges (on a single channel basis). The multi-stage detection system consists of three major stages: mimetic, SOFM, and fuzzy logic. Fuzzy logic is introduced in order to incorporate spatial contextual information in the detection process. Through fuzzy logic it has been possible to develop an approximate model of the spatial reasoning performed by the electroencephalographer. RESULTS The system was trained on 35 epileptiform EEGs containing over 3000 epileptiform events and tested on a different set of eight EEGs containing 190 epileptiform events (including one normal EEG). Results show that the system has a sensitivity of 55.3% and a selectivity of 82% with a false detection rate of just over seven per hour. CONCLUSIONS Based on these initial results the overall performance is favourable when compared with other leading systems in the literature. This encourages us to further test the system on a larger population base with the ultimate aim of introducing it into routine clinical use.

Noise equalization for detection of microcalcification clusters in direct digital mammogram images

Equalizing image noise is shown to be an important step in the automatic detection of microcalcifications in digital mammography. This study extends a well established film-screen noise equalization scheme developed by Veldkamp et al. for application to full-field digital mammogram (FFDM) images. A simple noise model is determined based on the assumption that quantum noise is dominant in direct digital X-ray imaging. Estimation of the noise as a function of the gray level is improved by calculating the noise statistics using a truncated distribution method. Experimental support for the quantum noise assumption is presented for a set of step wedge phantom images. Performance of the noise equalization technique is also tested as a preprocessing stage to a microcalcification detection scheme. It is shown that the square root model based approach which FFDM allows leads to a robust estimation of the high frequency image noise. This provides better microcalcification detection performance when compared to the film-screen noise equalization method developed by Veldkamp. Substantially better results are obtained than when noise equalization is omitted. A database of 124 direct digital mammogram images containing 28 microcalcification clusters was used for evaluation of the method.

Frequent lapses of responsiveness during an extended visuomotor tracking task in non-sleep-deprived subjects

We investigated the occurrence of lapses of responsiveness (lapses) in 15 non‐sleep‐deprived subjects performing a 1D continuous tracking task during normal working hours. Tracking behaviour, facial video, and electroencephalogram (EEG) were recorded simultaneously during two 1‐h sessions. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 ± 12.9 lapses per hour (mean ± SE) and a lapse duration of 3.4 ± 0.5 s. We also found that subjects’ performance improved towards the end of the 1‐h long session, even though no external temporal cues were available. Spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. In conclusion, lapses are a frequent phenomenon in normal subjects – even when not sleep‐deprived – engaged in an extended monotonous continuous visuomotor task. This is of particular importance to the transport sector in which there is a need to maintain sustained attention for extended periods of time and in which lapses can lead to multiple‐fatality accidents.

Wavelet Analysis of Transient Biomedical Signals and its Application to Detection of Epileptiform Activity in the EEG

Wavelet based signal analysis provides a powerful new means for the analysis of nonstationary signals such as the human EEG. The properties of the discrete wavelet transform are reviewed in illustrated application examples. The continuous wavelet transform is shown to provide better detection and representation of isolated transients. An approach to extract features of edges and transients from the continuous wavelet transform is outlined. Matching pursuit is presented as a more general transform method that covers both transients and oscillation spindles. A statistical model for the continuous wavelet transform of background EEG is found. A spike detection system based on this background model is presented. The performance of this detection system has been assessed in a preliminary clinical study of 11 EEG recordings containing epileptiform activity and shown to have a sensitivity of 84% and a selectivity of 12%. The spatial context of epileptiform activity will be incorporated to improve system performance.

Multireference adaptive noise canceling applied to the EEG

The technique of multireference adaptive noise canceling (MRANC) is applied to enhance transient nonstationarities in the electroencephalogram (EEG), with the adaptation implemented by means of a multilayer perceptron artificial neural network (ANN). The method was applied to recorded EEG segments and the performance on documented nonstationarities recorded. The results show that the neural network (nonlinear) gives an improvement in performance (i.e., signal-to-noise ratio (SNR) of the nonstationarities) compared to a linear implementation of MRANC. In both cases an improvement in the SNR was obtained. The advantage of the spatial filtering aspect of MRANC is highlighted when the performance of MRANC is compared to that of the inverse auto-regressive filtering of the EEG, a purely temporal filter.

Prior estimate-based compressed sensing in parallel MRI

Two improved compressed sensing (CS)‐based image reconstruction methods for MRI are proposed: prior estimate‐based compressed sensing (PECS) and sensitivity encoding‐based compressed sensing (SENSECS). PECS allows prior knowledge of the underlying image to be intrinsically incorporated in the image recovery process, extending the use of data sorting as first proposed by Adluru and DiBella (Int J Biomed Imaging 2008: 341648). It does so by rearranging the elements in the underlying image based on the magnitude information gathered from a prior image estimate, so that the underlying image can be recovered in a new form that exhibits a higher level of sparsity. SENSECS is an application of PECS in parallel imaging. In SENSECS, image reconstruction is carried out in two stages: SENSE and PECS, with the SENSE reconstruction being used as a image prior estimate in the following PECS reconstruction. SENSECS bypasses the conflict of sampling pattern design in directly applying CS recovery in multicoil data sets and exploits the complementary characteristics of SENSE‐type and CS‐type reconstructions, hence achieving better image reconstructions than using SENSE or CS alone. The characteristics of PECS and SENSECS are investigated using experimental data. Magn Reson Med, 2010.

Deconvolution and phase retrieval with use of zero sheets

Recent developments in the application of zeros (of the analytically continued spectrum of a compact two-dimensional image) in solving deconvolution and phase retrieval problems are reviewed. New algorithms for use in the presence of noise are described and demonstrated. These include algorithms for deconvolution where the point-spread function is approximately known, for ensemble blind deconvolution (such as is required for ensembles of astronomical speckle images), and for phase retrieval (itself a special case of blind deconvolution). Many of the ideas embodied in the algorithms were foreshadowed by Bates et al. [ J. Opt. Soc. Am. A7, 468 ( 1990)]. Simulated images are employed in the examples shown, except for phase retrieval, where successful recovery of the phase error in the aperture of a radio telescope is demonstrated.

Losing the struggle to stay awake: Divergent thalamic and cortical activity during microsleeps

Maintaining alertness is critical for safe and successful performance of most human activities. Consequently, microsleeps during continuous visuomotor tasks, such as driving, can be very serious, not only disrupting performance but sometimes leading to injury or death due to accidents. We have investigated the neural activity underlying behavioral microsleeps – brief (0.5–15 s) episodes of complete failure to respond accompanied by slow eye‐closures – and EEG theta activity during drowsiness in a continuous task. Twenty healthy normally‐rested participants performed a 50‐min continuous tracking task while fMRI, EEG, eye‐video, and responses were simultaneously recorded. Visual rating of performance and eye‐video revealed that 70% of the participants had frequent microsleeps. fMRI analysis revealed a transient decrease in thalamic, posterior cingulate, and occipital cortex activity and an increase in frontal, posterior parietal, and parahippocampal activity during microsleeps. The transient activity was modulated by the duration of the microsleep. In subjects with frequent microsleeps, power in the post‐central EEG theta was positively correlated with the BOLD signal in the thalamus, basal forebrain, and visual, posterior parietal, and prefrontal cortices. These results provide evidence for distinct neural changes associated with microsleeps and with EEG theta activity during drowsiness in a continuous task. They also suggest that the occurrence of microsleeps during an active task is not a global deactivation process but involves localized activation of fronto‐parietal cortex, which, despite a transient loss of arousal, may constitute a mechanism by which these regions try to restore responsiveness. Hum Brain Mapp 35:257–269, 2014.

Detecting Behavioral Microsleeps from EEG Power Spectra

EEG spectral power has been shown to correlate with level of arousal and alertness in humans. In this paper, we assess its usefulness in the detection of behavioral microsleeps (BMs). Eight non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG and facial video were recorded. BMs were identified independent of tracking performance by a human rater by viewing the video recordings. Spectral power, normalized spectral power, and power ratios in the standard EEG bands were calculated using the Burg method on 16 bipolar derivations to form an EEG feature matrix. PCA was used to reduce the dimensionality of the feature matrix and linear discriminant analysis used to form a classifier for each subject. The 8 classifiers were combined using stacked generalization to create an overall detection model and N-fold cross-validation used to determine its performance (Phi=0.30plusmn0.05, meanplusmnSE). While modest, the detection of BMs at such a high temporal resolution (1 s) has not been achieved previously other than by our group