In this study, an intelligent system is developed for the three dimensional reconstruction of cerebral arteries from biplane cineangiograms. The system is composed of two blocks, i. e., an inferencing-control-block and a processing-block. The inferencing-control block controls the flow of the image-processing by inferencing with the knowledge stored in the block and is a production system based on “IF, THEN” rule. The processing-block is a collection of image processing procedures activated by a call from the inferencing-control-block. On the other hand, the flow of the image-processing is outlined as follows: After the extraction of vessel center lines from the angiograms, the blood flow directions and connectivity states of vessels are determined and the vessel graph is translated to a vessel connectivity tree. Then, by utilizing the knowledge about anatomic structure of cerebral arteries and characteristics of angiograms, important arteries are distinguished and vessel groups classified. Finally, by using a shape-oriented matching method, the vessels on the two projected planes are matched and the three dimensional structure of vessels constructed. An example is presented to demonstrate the effectiveness of the use of the knowledge which enables the system to improve the efficiency and precision of the processing, such as vessel analysis and matching.

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The purpose of this study is to make a quantitative search for possibility of deterministic dynamics in MEG (Magnetoencephalogram) data by analysing the correlation dimensions. The time series data of MEG are analyzed not only by the conventional G-P (Grassberger-Procaccia) algorithm but also by an improved estimator of the correlation dimension proposed by K. Judd to overcome some weaknesses of the G-P method (Physica D, 56, 1992). The improved method utilizes the distribution of the interpoint distances directly, where the dimension can be estimated with multinomial maximum-likelihood estimation. The results show that the correlation dimension of MEG data mostly takes values between 6 and 14. We also discuss influence of low-pass filter and noise upon the estimated dimension.

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A method for automatic detection of vigilance level was developed based on EEG data only, as for pre-processing of automatic EEG interpretation (previous work by the authors). As EEG records during sleep or drowsy condition are quite different from those of wakefulness, drowsy situation of subjects during the course of EEG recording should be strongly avoided. Clinically, however, drowsy EEG data are often obtained despite the recorders' efforts. The detection of drowsiness from EEG data is the most important task for EEGers in interpreting awake EEGs. The task of detecting vigilance level was automatized in this study. The equations for detecting vigilance level were determined so that they would conform to the procedure that a EEGer (one of the authors, H. S.) usually adopts for visual inspection of the actual EEG record, and all threshold values in the equations were determined. The proposed automatic detection method was applied to EEG data of 32 subjects and gave the correct judgement as to the wakefulness in all EEG data. The combined use of the proposed automatic method of the vigilance detection and the automatic EEG interpretation method will be effectively applicable to the clinical use.

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Ambient noise such as machine noise and human voice often disturbs lung sound measurement. Frequency domain filtering is usually ineffective because of the spectral overlap of lung sound and ambient noise. Ambient noise is transmitted to the microphone measuring lung sound through the chest wall around it. Cancelling of the noise component may be possible by identifying this transfer function. The function, however, may vary with subject and measuring site, so that it should be modified dynamically. We applied adaptive filtering technique to solve this problem. An off-line adaptive noise canceller having a transversal filter was implemented in a workstation. Filter coefficients were controlled by LMS algorithm. Wide-band random noise and human voice were used as ambient noise. Noise corrupted lung sound and ambient noise were simultaneously recorded as the primary and reference input of the noise canceller. The performance of the canceller was assessed by changing canceller parameters and ambient noise level. Noise reduction was calculated as the ratio of the power of the canceller output over that of the canceller primary input for the data in the case when the subject held his breath. The result showed that the ambient noise was reduced by about 30dB using a 256-tap filter with the convergence time of several seconds. Clear lung sound can be heard by D/A conversion of the canceller output. The results show that this method is very effective as a preprocessing tool for the lung sound analysis, and that it is promising to realize an electronic stethoscope with high ambient noise immunity by real-time digital signal processing.

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A monitor for simultaneous recording of heart and respiratory rates has been developed using digital filters and photoplethysmography (PPG) with plastic optical fiber. PPG signal includes both heart and respiratory synchronous components. Digital filtering technique was employed to differentiate between those. With increasing heart and respiratory rates, the cut-off frequency of filter was changed empirically. Finite impulse response digital filters were designed using the Remez exchange algorithm. One filter for the heart-related signal was 40th order of Chebyshev type and the other filters for the respiration-related signal were 60th order of Chebyshev type. A suitable cut-off frequency of filter was selected empirically. The heart-related signal was detected by using a impulse response, while the respiration-related signal was detected by three impulse responses. One out of three impulse response was selected for the respiration-related signal. Heart and respiratory rate were increased with whole body heart stress i. e. bathing. The heart rates from 65 to 87 beats/min and the respiratory rates from 13 to 27 breath/min agreed with those obtained with ECG and thermistor. The digital filtering technique may provide on-line monitor for heart and respiratory rates.

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We have conducted photoimmunotherapy experiments on a malignant tumor using a photosensitizer, lumin, with a laser light. Lumin is a potent cell activating agent and a photosensitizer characterized by typical absorption peaks at 670nm and 770nm. To clarify macrophage activity enhanced by lumin administration in light irradiation in the vicinity of absorption peaks, three light sources were used; i. e., a white lamp, a He-Ne laser (632.8nm) and a semiconductor laser diode (780nm). The results showed that a small dose of lumin (5ng/ml) with near-infrared laser light of 780nm (energy: about 2×10^-3J/cm²) dramatically enhanced the macrophage activity. To study the efficacy of photoimmunotherapy, a human lung cancer was grafted onto nude mice. After the tumor hypertrophied, lumin was injected into the tumor six times. The tumor was exposed to near-infrared laser light (2mW, 1min) every other day for two weeks. The whole tumor was strongly cicatrized by collagen fiber from the stroma and removed 15 weeks after the treatment. The immunological function was investigated using flow cytometric assay, which was analyzed by the two-color method based on immunofluorescence labeling with specific monoclonal antibodies for the sampling of 10⁴cells harvested from mouse peripheral blood. At 40 weeks, the ratio of T and B cells of the non-treated mice (hypertrophied tumor) was 0.29, whereas that of the treated mice (cicatrized tumor) was 0.71. Thus, it was clarified that the immune function was enhanced by the photoimmunological treatment. These results demonstrated that photoimmunotherapy with lumin administration and laser light irradiation is a reliable method of cancer treatment without any side effects.

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