Tadanori Fukami

Imaging renal structures by X-ray phase-contrast microtomography

X-ray crystal interferometer-based X-ray phase-contrast microtomography (phase-contrast microtomography) is able to image microstructures within soft tissue without the use of a contrast agent. Here we determined the feasibility of using this technique in the non-destructive inspection of formalin-fixed kidney tissue from certain hamsters that spontaneously develop mesangial thickening with focal and segmental glomerulosclerosis, and from age-matched Syrian hamsters. We used a triple Laue-case X-ray interferometer with a 40 microm-thick analyzer, a sample cell, and an X-ray charge-coupled-device camera with a 4.34 microm pixel size. Images of glomeruli and tubular structures were similar to those seen using 40-100 x magnification on an optical microscope. In samples from two female glomerulosclerotic hamsters, seven scattered lesions were detected. The wedge-shaped pathological lesions included mild atrophic tubular walls, markedly dilated tubular lumen, high-density glomeruli, and widening of Bowmans space. The microvasculature was distinctly visualized in the specimens without any contrast agents. Hence, phase-contrast microtomography can detect small scattered lesions in diseased kidney tissue and is a powerful auxiliary tool for pre-histological evaluations.

A neural network based human identification framework using ear images

This paper presents a framework that uses ear images for human identification. The framework makes use of Principal Component Analysis (PCA) for ear image feature extraction and Multilayer Feed Forward Neural Network for classification. Framework are proposed to improve recognition accuracy of human identification. The framework was tested on an ear image database to evaluate its reliability and recognition accuracy. The experimental results showed that our framework achieved higher stable recognition accuracy and over-performed other existing methods. The recognition accuracy stability and computation time with respect to different image sizes and factors were investigated thoroughly as well in the experiments.

Quantitative evaluation of myocardial function by a volume-normalized map generated from relative blood flow.

Our study aimed to quantitatively evaluate blood flow in the left ventricle (LV) of apical hypertrophic cardiomyopathy (APH) by combining wall thickness obtained from cardiac magnetic resonance imaging (MRI) and myocardial perfusion from single-photon emission computed tomography (SPECT). In this study, we considered paired MRI and myocardial perfusion SPECT from ten patients with APH and ten normals. Myocardial walls were detected using a level set method, and blood flow per unit myocardial volume was calculated using 3D surface-based registration between the MRI and SPECT images. We defined relative blood flow based on the maximum in the whole myocardial region. Accuracies of wall detection and registration were around 2.50 mm and 2.95 mm, respectively. We finally created a bulls-eye map to evaluate wall thickness, blood flow (cardiac perfusion) and blood flow per unit myocardial volume. In patients with APH, their wall thicknesses were over 10 mm. Decreased blood flow per unit myocardial volume was detected in the cardiac apex by calculation using wall thickness from MRI and blood flow from SPECT. The relative unit blood flow of the APH group was 1/7 times that of the normals in the apex. This normalization by myocardial volume distinguishes cases of APH whose SPECT images resemble the distributions of normal cases.

Estimation of temporary change of activation areas by moving an analysis time window in fMRI measurement

In this paper, we propose a method to acquire temporal changes of activations by moving an analysis time window. An advantage of this method is that it can acquire rough changes of activated areas even with the data having low time resolution. We ascertained that activations from our method do not contradict previous reports on the oddball paradigm, thus showing its effectiveness. Eight normal subjects participated in the study, which consisted of a random series of 30 target and 70 nontarget stimuli. We investigated the activated area in three kinds of analysis time sections, from stimulus onset to 5 s after the stimulus (time section A), from 2 to 7 s after (B) and from 4 to 9 s after (C). In time section A, representative activated areas were regions including the left and supplementary motor areas (SMA), and cerebellum. In B, regions including the left motor area and SMA, right parahippocampal gyrus (Broadmann Area (BA) 30), right limbic lobe and cerebellum were activated. In C, bilaterally postcentral gyrus (BA 3,40), right anterior cingulate (ACC, BA 32), left middle frontal gyrus (BA 9) and right parahippocampal gyrus were activated. Most activations were consistent with previous studies.

Quantitative evaluation for the wakefulness state using complexity-based decision threshold value in EEG signals

Fully awake state of the subjects tends to be an early drowsy state as a result from the prolonged time of electroencephalography (EEG) measurements. Such situations can complicate the interpretation of EEG signals and hence, the wakefulness of the subject should be considered in the inspection. Thus, in the present study, a new index for quantitative evaluation of the wakefulness (whether either early drowsy or fully awake) state of subjects by using a complexity-based decision threshold value was developed. The proposed index was based on approximate entropy (ApEn) to quantify the complexity metric, but with new parameter values by using a new systematic approach. This index was evaluated using occipital-alpha rhythm during eye closure for 45 healthy adult subjects for each one of two groups: fully awake and drowsy groups. Our index could show more superiority than other conventional spectral-based indices used for evaluating the wakefulness state of subjects including relative delta sub band power (R.δ), relative theta sub band power (R.θ), power ratio between theta and alpha (Pθ/α), and between theta and beta (Pθ/β) over occipital lobe. Our index is superior than R.δ, R.θ, Pθ/α and Pθ/β with 10%, 5.5%, 8.9% and 24.4% respectively.

Quantitative evaluation of photic driving response for computer-aided diagnosis

The aim of our research is the quantification of the photic driving response, a routine electroencephalogram (EEG) examination, for computer-aided diagnosis. It is well known that the EEG responds not only to the fundamental frequency but also to all sub and higher harmonics of a stimulus. In this study, we propose a method for detecting and evaluating responses in screening data for individuals. This method consists of two comparisons based on statistical tests. One is an intraindividual comparison between the EEG at rest and the photic stimulation (PS) response reflecting enhancement and suppression by PS, and the other is a comparison between data from an individual and a distribution of normals reflecting the position of the individuals data in the distribution of normals in the normal database. These tests were evaluated using the Z-value based on the Mann-Whitney U-test. We measured EEGs from 130 normal subjects and 30 patients with any of schizophrenia, dementia and epilepsy. Normal data were divided into two groups, the first consisting of 100 data for database construction and the second of 30 data for test data. Using our method, a prominent statistical peak of the Z-value was recognized even if the harmonics and alpha band overlapped. Moreover, we found a statistical difference between patients and the normal database at diagnostically helpful frequencies such as subharmonics, the fundamental wave, higher harmonics and the alpha frequency band.

Fundamental study on subharmonic imaging by irradiation of amplitude-modulated ultrasound waves

The second harmonic and subharmonic components, the frequencies of which are twice and one half the fundamental frequency, are included in echoes from contrast agents. An imaging method, which employs a second harmonic (second harmonic imaging), is widely used in medical diagnoses. On the other hand, subharmonic is expected to provide a higher contrast between biological tissues and blood flow because echo signals are generated only from blood containing the contrast agents. However, the subharmonic component echo signal power from contrast agents is relatively low. This has resulted in little progress in the field of subharmonic imaging. In this study, a new imaging method is proposed using amplitude-modulated waves as transmitted waves combined with the pulse inversion method to enhance subharmonic echo signals. Two optimal frequencies are set, including the modulated waves, F(1) and F(2), so that the subharmonic frequency of F(1) and the second harmonic frequency of F(2) may result in the same value. This allows a more powerful signal at the frequency band because the second harmonic and subharmonic components are integrated. Furthermore, a B-mode ultrasound image of an agar phantom that imitated biological tissue and showed the effectiveness of our method was reconstructed. As a result, the echo power of the subharmonic component was enhanced by approximately 11.8 dB more than the conventional method and the signal to noise ratio showed an improvement of 7.6 dB.

Fusion imaging of fluorescent and phase-contrast X-ray computed tomography using synchrotron radiation in medical biology

We integrated fluorescent X-ray computed tomography (FXCT) and phase-contrast X-ray computed tomography (PCCT), and the feasibility of this fusion imaging was assessed for small animals. Brain tumor model of mouse and cardiomyopathic model of hamsters were examined. The brain and heart were extracted after intravenous injection of cerebral perfusion agent 127I-IMP and myocardial fatty acid metabolic agent 127I-BMIPP, respectively. Each target organ was fixed by formalin for FXCT and PCCT. Images were obtained three-dimensionally (3D), and the surface contour of brain and heart were determined from 3D-image after re-sampling for the description with the same spatial resolution. These images were fused interactively on displayed images by 3D image manipulation software. In FXCT, cerebral perfusion image with IMP and fatty acid metabolic image with BMIPP were clearly demonstrated at 0.5 mm and 0.2 mm spatial resolution, respectively. PCCT image with 0.03 mm spatial resolution depicted clearly the morphological structures of brain such as cerebral cortex, hippocampus, lateral ventricle and cerebellum, and for heart such as cardiac lumen, papillary muscle, left and right ventricle. On fusion image, localization and degree of abnormality of cerebral perfusion and myocardial fatty acid metabolism were easily recognized. Our results suggested that the integration of FXCT and PCCT is very useful to understand biological state corresponding to its anatomical localization even in small animal.

Human brain mapping of judgment system of movement of visualized material

Human visual system has been frequently studied. Anatomical and physiological investigations indicate functional streams within the extrastriate visual cortex of the macaque monkey. It shows that V5 area participates in motion perception. However, the region participating motion judgment was not very investigated. In this paper, we tried to reveal the region of motion judgment. We used functional magnetic resonance imaging (fMRI) to explore. The short videos of moving ball (lasting 1s) and stationary ball (lasting 1s) were randomly presented to subjects repeatedly. Each paradigm consisted of two main conditions (control and activation condition). During control, subjects continued tapping regardless of stimuli. During activation condition, subjects tap only when the video of moving ball was presented (motion judgment). When subjects judge motion, activity was revealed in frontal cortex (Brodmann 9).

Robust estimation of event-related potentials via particle filter

BACKGROUND AND OBJECTIVE In clinical examinations and brain-computer interface (BCI) research, a short electroencephalogram (EEG) measurement time is ideal. The use of event-related potentials (ERPs) relies on both estimation accuracy and processing time. We tested a particle filter that uses a large number of particles to construct a probability distribution. METHODS We constructed a simple model for recording EEG comprising three components: ERPs approximated via a trend model, background waves constructed via an autoregressive model, and noise. We evaluated the performance of the particle filter based on mean squared error (MSE), P300 peak amplitude, and latency. We then compared our filter with the Kalman filter and a conventional simple averaging method. To confirm the efficacy of the filter, we used it to estimate ERP elicited by a P300 BCI speller. RESULTS A 400-particle filter produced the best MSE. We found that the merit of the filter increased when the original waveform already had a low signal-to-noise ratio (SNR) (i.e., the power ratio between ERP and background EEG). We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging, and determined that the particle filter yielded a maximum 42.8% reduction in measurement time. The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency. For EEG data produced by the P300 speller, we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method, irrespective of the amount of averaging. CONCLUSIONS We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR. Additionally, particle filters can perform robust ERP estimation for EEG data produced via a P300 speller.