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Reduced graphene oxide and molybdenum disulfide (rGO:MoS2) are the most representative two-dimensional materials, which are promising for a humidity sensor owing to its high surface area, a large number of active sites, and excellent mechanical flexibility. Herein, we introduced a highly sensitive and stable rGO:MoS2-based humidity sensor integrated with a low-power in-plane microheater and a temperature sensor, directly insertable to transformer insulating oil, and analyzed by a newly developed customized sensor interface electronics to monitor the sensor's output variations in terms of relative humidity (RH) concentration. rGO:MoS2 sensing materials were synthesized by simple ultrasonication without using any additives or additional heating and selectively deposited on titanium/platinum (Ti/Pt) interdigitated electrodes on a SiO2 substrate using the drop-casting method. The significant sensing capability of p-n heterojunction formation between rGO and MoS2 was observed both in the air and transformer insulating oil environment. In air testing, the sensor exhibited an immense sensitivity of 0.973 kΩ/%RH and excellent linearity of ∼0.98 with a change of humidity from 30 to 73 %RH, and a constant resistance deviation with an inaccuracy rate of 0.13% over 400 h of continual measurements. In oil, the sensor showed a high sensitivity of 1.596 kΩ/%RH and stable repeatability for an RH concentration range between 34 and 63 %RH. The obtained results via the sensor interface were very similar to those measured with a digital multimeter, denoting that our developed total sensor system is a very promising candidate for real-time monitoring of the operational status of power transformers.

Bedside radiography has increasingly attracted attention because it allows for immediate image diagnosis after X-ray imaging. Currently, wireless flat-panel detectors (FPDs) are used for digital radiography. However, adjustment of the X-ray tube and FPD alignment are extremely difficult tasks. Furthermore, to prevent a poor image quality caused by scattered X-rays, scatter removal grids are commonly used. In this study, we proposed a scatter-correction processing method to reduce the radiation dose when compared with that required by the X-ray grid for the segmentation of a mass region using deep learning during bedside chest radiography. A chest phantom and an acrylic cylinder simulating the mass were utilized to verify the image quality of the scatter-corrected chest X-rays with a low radiation dose. In addition, we used the peak signal-to-noise ratio and structural similarity to quantitatively assess the quality of the low radiation dose images compared with normal grid images. Furthermore, U-net was used to segment the mass region during the scatter-corrected chest X-ray with a low radiation dose. Our results showed that when scatter correction is used, an image with a quality equivalent to that obtained by grid radiography is produced, even when the imaging dose is reduced by approximately 20%. In addition, image contrast was improved using scatter radiation correction as opposed to using scatter removal grids. Our results can be utilized to further develop bedside chest radiography systems with reduced radiation doses.

The adsorption of sulfur hexafluoride (SF6) by a transition metal-porphyrin structure embedded on the surface of graphene (MN4-graphene) was evaluated using first-principles density functional theory calculations by constructing the adsorption energy profile. The mutual balance between physisorption and chemisorption was assessed by analyzing the characteristics of the interaction between each central metal atom as an adsorption center and the SF6 molecule. CaN4- and CrN4-graphene had moderate adsorption energies of about -1.5 eV. The results indicate the feasibility of these species as reusable SF6 adsorbents, even under ambient conditions. This study provides deeper insight into the adsorption of SF6 and the potential of transition metal-porphyrin structures as SF6 capture materials for mitigating global warming.

Accurate positioning is essential for radiography, and it is especially important to maintain image reproducibility in follow-up observations. The decision on re-taking radiographs is entrusting to the individual radiological technologist. The evaluation is a visual and qualitative evaluation and there are individual variations in the acceptance criteria. In this study, we propose a method of image evaluation using a deep convolutional neural network (DCNN) for skull X-ray images.The radiographs were obtained from 5 skull phantoms and were classified by simple network and VGG16. The discrimination ability of DCNN was verified by recognizing the X-ray projection angle and the retake of the radiograph. DCNN architectures were used with the different input image sizes and were evaluated by 5-fold cross-validation and leave-one-out cross-validation.Using the 5-fold cross-validation, the classification accuracy was 99.75% for the simple network and 80.00% for the VGG16 in small input image sizes, and when the input image size was general image size, simple network and VGG16 showed 79.58% and 80.00%, respectively.The experimental results showed that the combination between the small input image size, and the shallow DCNN architecture was suitable for the four-category classification in X-ray projection angles. The classification accuracy was up to 99.75%. The proposed method has the potential to automatically recognize the slight projection angles and the re-taking images to the acceptance criteria. It is considered that our proposed method can contribute to feedback for re-taking images and to reduce radiation dose due to individual subjectivity.

When extracting lesions in medical images using deep learning, the extraction accuracy will be higher if the image quality is good. The purpose of this study is to investigate and comprehend the relationship between the spatial-resolution property of X-ray images and the accuracy of lesion extraction by deep learning, and to examine the possibility of dose reduction from the obtained results. The simulated masses arranged on different areas in dual energy subtraction (DES) chest radiographs acquired by various doses were used for the investigation. Task-based transfer functions (TTFs) as a spatial-resolution property for the DES chest radiography were calculated. The mass areas were also extracted by U-net, and Dice coefficients were obtained as the extraction accuracy. In results, regardless of mass locations, the TTFs of the reference dose images and the 75% dose images showed high frequency responses, and the Dice coefficients were also high. The TTFs and the Dice coefficients were obviously lower in the images of when the masses were located in the right supraclavicular region at 50% dose compared with the other conditions. The results of this study suggested that the spatial-resolution property was strongly related to the accuracy of mass region extracted by deep learning in DES chest radiography. In conclusion, the dose reduction of about 25% compared with the conventional dose should be feasible.

This study investigated the usefulness of deep learning methods for segmenting the whole heart region and the cardiac cavity region in pediatric cardiac CT images using U-Net. Dice similarity coefficient (DSC) was used to evaluate the segmentation accuracy by leave-one-subject-out cross-validation. The mean DSC for the whole heart was over 0.95, and analysis of variance among the four age categories (less than one year, 1y to 4y, 5y to 9y, 10y to 14y) showed no significant differences. The mean DSCs for each chamber were 0.78–0.88 when they were trained in a lump. The corresponding DSCs were 0.80–0.85 when they were trained separately. Although the size and shape of the heart varied with age in children, whole heart segmentation using U-Net showed high DSCs in all age categories. Deep learning would become a useful elemental technology in heart segmentation of pediatric cardiology.

A high-performance planar metal-polyimide-interdigital transducer (IDT) metal-type miniaturized capacitive humidity sensor was developed for real-time detection of moisture dissolved in transformer-insulating oil. The sensor with the sensitive film having a concave surface and a porous structure was fabricated through using a micromachined process. Additionally, the sensor interface electronics were implemented for the measurement system of the fabricated sensor and immediate use of the sensor in oil. COMSOL simulation was performed to determine the top metal IDT configuration and polyimide thickness prior to sensor fabrication. The performance of the developed sensor was tested in air and transformer oil. In the air test, the sensor showed a high sensitivity of 1.60 pF/% relative humidity (RH) and a large capacitance variation from 204 to 374 pF with a humidity change from 20% to 90% RH at a frequency of 3 kHz; a low hysteresis of 2.07% at 40% RH and a stable capacitance value with a maximum error rate of 0.15% over 450 h of continuous testing. In the transformer oil, the sensor exhibited a sensitivity of 0.90 pF/%RH and an extremely small long-term drift of the capacitance corresponding to humidity variation of ±2.0%RH. Overall, the modification of the polyimide film enhanced the performance of our sensor, such as high sensitivity, fast response, and long-term stability in the transformer–oil environment.

Reduced graphene oxide and molybdenum disulfide (rGO:MoS

In individualized screening mammography, a breast density is important to predict potential risks of breast cancer incidence and missing lesions in mammographic diagnosis. Segmentation of the mammary gland region is required when focusing on missing lesions. A deep-learning method was recently developed to segment the mammary gland region. A large amount of ground truth (prepared by mammary experts) is required for highly accurate deep-learning practice; however, this work is time- and labor-intensive. To streamline the ground truth in deep learning, we investigated a difference in acquired mammary gland regions among multiple radiological technologists having various experience and reading levels, who shared the criteria on segmentation. If we can ignore a skill level for image reading, we can increase a number of training images. Three certified radiological technologists segmented the mammary gland region in 195 mammograms. The degree of coincidence among them was assessed with respect to seven factors which indicated the feature of segmented regions including the breast density and mean glandular dose, using Student’s t-test and Bland-Altman analysis. The assessments made by the three radiological technologists were consistent considering all factors, except the mean pixel value. Thus, we concluded that the ground truths prepared by multiple practitioners with different experiences can be accepted for the segmentation of the mammary gland region and they are applicable for training images if they stringently share the criteria on the segmentation.

ObjectiveDespite the high risk of missing lesions in mammography, the missed lesion rate is yet to be clinically established. Further, no breast phantoms with adjustable breast density currently exist. We developed a novel, adjustable-density breast phantom with a composition identical to that of actual breasts, and determined the quantitative relationship between breast density and the missed lesion rate in mammography.MethodsAn original breast phantom consisting of adipose- and fibroglandular-equivalent materials was developed, and a receiver operating characteristic (ROC) study was performed. Breast density, which is the fraction by weight of fibroglandular to total tissue, was adjusted to 25%, 50%, and 75% by arbitrarily mixing the two materials. Microcalcification, mass lesions, and spiculated lesions, each with unique characteristics, were inserted into the phantom. For the above-mentioned fibroglandular densities, 50 positive and 50 negative images for each lesion type were used as case samples for the ROC study. Five certified radiological technologists participated in lesion detection.ResultsThe mass-lesion detection rate, according to the area under the curve, decreased by 18.0% (p= 0.0001, 95% Confidence intervals [CI] = 0.1258 to 0.1822) and 37.8% (p= 0.0003, 95% CI = 0.2453 to 0.4031) for breast densities of 50% and 75%, respectively, compared to that for a 25% breast density. A similar tendency was observed with microcalcification; however, spiculated lesions did not follow this tendency.ConclusionsWe quantified the missed lesion rate in different densities of breast tissue using a novel breast phantom, which is imperative for advancing individualized screening mammography.

We investigated the relationship between the spatial-resolution property of soft tissue images and the lesion detection ability using U-net. We aimed to explore the possibility of dose reduction during energy subtraction chest radiography. The correlation between the spatial-resolution property of each dose image and the segmentation accuracy of the tumor area in the four regions where the tumor was placed was evaluated using linear regression analysis. The spatial-resolution property was determined by task-based evaluation, and the task-based modulation transfer function (TTF) was computed as its index. TTFs of the reference dose image and the 75 % dose image showed almost the same frequency characteristics regardless of the location of the tumor, and the Dice coefficient also high. When the tumor was located in the right supraclavicular region and under 50 % dose, the frequency characteristics were significantly reduced, and the Dice coefficient was also low. Our results showed a close relationship between the spatial-resolution property and the segmentation accuracy of tumor area using deep learning in dual-energy subtraction chest radiography. In conclusion, a dose reduction of approximately 25 % compared to the conventional method can be achieved. The limitations are the shape of the simulated mass and the use of chest phantom.

Receiver operating characteristic (ROC) examination was performed to investigate the effectiveness of high-luminance monitors in digital X-ray mammography. For this purpose, an original breast phantom consisting of adipose and fibroglandular equivalent tissues with an identical X-ray absorption characteristic over the entire mammographic photon energy range was developed. Furthermore, the phantom’s fibroglandular density and distribution could be changed arbitrarily. Three types of lesions, microcalcification, mass, and spiculated, were inserted into the breast phantom, and the ROC examination was performed by five radiological technologists certified in screening mammography, to obtain the area under the curve. A liquid crystal display (LCD) monitor with 5 megapixels in a 21-inch display size calibrated to a grayscale standard display function curve was used for the observation. The monitor was set to 600, 900, and 1200 cd/m2 in maximum luminance. The experimental details were fibroglandular density of 25%, respective 50 positive and negative images, and free observation time and distance. As a result, the dependence on monitor luminance differed according to the lesion type. The detectability of microcalcification increased with the increase in the luminance of the monitor. Spiculated lesions were similar for all luminance changes. The detectability of mass lesions was significantly higher at 900 cd/m2 than at 600 cd/m2 . There was no significant difference between those at 900 cd/m2 and 1200 cd/m2 . In conclusion, the maximum luminance of the diagnostic LCD monitor for mammography should be at least 900 cd/m2 to guarantee stable detectability.

To evaluate the usefulness of deep learning-assisted diagnosis for identifying hyperdense middle cerebral artery sign (HMCAS) on non-contrast computed tomography in comparison with the diagnostic performance of neuroradiologists. We obtained 46 HMCAS-positive and 52 HMCAS-negative test samples extracted using 50-pixel-diameter circular regions of interest. Five neuroradiologists undertook an initial diagnostic performance test by describing the HMCAS-positive prediction rate in each sample. Their diagnostic performance was compared with that of a deep convolutional neural network (DCNN) model that had been trained using another dataset in our previous study. In the second test, readers could reference the prediction rate of the DCNN model in each sample. The diagnostic performance of the DCNN for HMCAS showed an accuracy of 81.6% and area under the receiver-operating characteristic curve (AUC) of 0.869, whereas the initial diagnostic performance of neuroradiologists showed an accuracy of 78.8% and AUC of 0.882. The second diagnostic test of neuroradiologists with reference to the results of the DCNN model showed an accuracy of 84.7% and AUC of 0.932. In all readers, AUC values were higher in the second test than the initial test. The ability of DCNN to identify HMCAS is comparable with the diagnostic performance of neuroradiologists.

Countermeasures against respiratory movement are important for tumors of thorax and abdomen in stereotactic body radiation therapy. In the present paper, a web-camera-based-respiratory monitoring method without contact with patient's body was proposed for respiratory study. Thoracic and abdominal motion images were taken by a web camera, and were analyzed using simple image-processing techniques for obtaining respiratory waveforms. Four motion images with different respiration rate were obtained from resusci anne simulator. Respiration waveforms were estimated from the moving images by the proposed method, and were compared with respiration waveforms obtained by the conventional respiratory monitoring device. That was found to have a strong correlation. In addition, the two waveforms were similar in Bland-Altman method comparison. The proposed method can provide non-contact, non-invasive, simple, and realistic respiratory monitoring system for radiotherapy.

In computed tomography coronary angiography (CTCA), calcification and stent make it difficult to evaluate intravascular lumen. This is a cause of low positive-predictive value of coronary stenosis. Therefore, it is expected to develop a computer-aided diagnosis (CAD) system that can automatically detect stenosis in coronary arteries. The purpose of this study is to automatically recognize calcifications or stents in coronary arteries and classify them from the normal coronary artery in CTCA. We used 4960 coronary-cross-sectional images, which consisted of 1113 images with calcification, 1353 images with a stent, and 2494 normal artery images. These images were automatically classified using the deep convolutional neural network (LeNet, AlexNet, and GoogLeNet). The classification accuracy of LeNet, AlexNet, and GoogLeNet were 58.4%, 75.9%, and 81.3%, respectively. The proposed method would be a fundamental technique of CAD in CTCA.

PURPOSE CT perfusion (CTP) is a powerful tool for the assessment of cerebrovascular disease. However, CTP maps are significantly different depending on CTP software and algorithm, even when using identical image data. We developed a phase-ratio image map (PI map), which was a novel perfusion map, without using CTP software. The purpose of this study was to investigate the usefulness of the PI map by comparing it with a positron emission tomography (PET) image. METHODS Twenty patients (16 men, 4 women; mean age: 61.6 years) with unilateral cervical and intracranial steno-occlusive disease underwent CTP. CTP source images were obtained at 1-s intervals of 23 times and 5 intervals using dynamic multiphase imaging. An early-phase image was generated by computing the average of CT images for 5 s in the vicinity of the peak enhancement curve of a normal hemisphere. A delayed-phase image was generated by computing the average of CT images for 5 s immediately after the early phase. The PI map was created by dividing the delayed-phase image by the early-phase image. We investigated the validity of the PI map compared with PET-cerebral blood flow (CBF). Lesion-to-normal ratios between a PET-CBF and the PI map or two conventional CTP-CBFs were observed and compared, and the relative errors were also compared. RESULT There was a strong correlation between the PET-CBF and the PI map (R=0.82). Correlations between the PET-CBF and two CTP-CBFs were weak (R=0.30) and middle (R=0.62), respectively. The relative error between the PI map and the PET-CBF was within 10% in most cases. CONCLUSION The PI map was more similar to the PET-CBF on perfusion evaluation, and did not depend on CTP software. The robustness and simplicity of the PI mapping method would be advantageous compared with conventional CTP mapping methods.

Mammography is obviously useful for the early detection and diagnosis of breast diseases in women. However, it usually involves anxieties and pains. This paper aimed to explore effects of the communication robot on distress reduction in mammography. Nineteen healthy women participated in the examination. They were randomly assigned to either an experimental group (n = 9) or a control group (n = 10). The participants in the experimental group talked and played with a communication robot before mammography. PALRO (FUJISOFT Inc., Tokyo, Japan) was used as the communication robot, which was a chatty, comforting robot. PALRO can communicate with the human and has several specific applications such as dancing, singing and talking about knowledge of various things. Autonomic nervous activities were observed before and during mammography. Degrees of subjective pain associated with mammography were also assessed using a visual analogue scale (VAS). As a result, autonomic nervous activities between the experimental group and the control group were not significantly different. Unfortunately, the communication robot did not help to intensify parasympathetic nerve activities, which became dominant at rest or a relaxed state. On the other hand, the VAS scores for pain in the experimental group were significantly smaller than ones in the control group (p < 0.01). This result suggested that the communication robot was useful for relieving degrees of subjective pain associated with mammography. In conclusion, communication with a robot before mammography would yield positive emotions and it would be related to the pain alleviation during mammography.

Treatment for patients with acute ischemic stroke is most commonly determined based on findings on noncontrast computerized tomography (CT). Identifying hypoattenuation of the early ischemic changes on CT images is crucial for diagnosis. However, it is difficult to identify hypoattenuation with certainty. We present an atlas-based computerized method using a convolutional neural network (CNN) to identify hypoattenuation in the lentiform nucleus and the insula, two locations where hypoattenuation appears most frequently. The algorithm for this method consisted of anatomic standardization, setting of regions, creation of input images for classification, training on the CNN and classification of hypoattenuation. The regions of the lentiform nucleus and insula were set according to the Alberta Stroke Programme Early CT score (ASPECTS) method, a visual quantitative CT scoring system. AlexNet was used in the classification of the CNN architecture. We applied this method to the lentiform nucleus and insula using a database of 20 patients with right-sided hypoattenuation, 20 patients with left-sided hypoattenuation, and 20 normal subjects. Our method was evaluated using a leave-one-case-out cross-validation test. This new method had an average accuracy of 88.3%, an average sensitivity of 87.5%, and an average specificity of 90% for identifying hypoattenuation in the two regions. These results indicate that this new method has the potential to accurately identify hypoattenuation in the lentiform nucleus and the insula in patients with acute ischemic stroke.

The aim of this study was to develop an interactive deep learning-assisted identification of the hyperdense middle cerebral artery (MCA) sign (HMCAS) on non-contrast computed tomography (CT) among patients with acute ischemic stroke. 35 HMCAS-positive and 39 HMCAS-negative samples extracted by 50-pixel-diameter circular regions of interest were obtained as training and validation datasets according to the consensus decisions of two experienced neuroradiologists. Data augmentation was performed to increase the number of training samples. A deep convolutional neural network (DCNN) (Xception) was used to classify input images as HMCAS-positive or -negative. Leave-one-case-out cross-validation was achieved to estimate sensitivity, specificity, and accuracy of the deep learning-based training model for identifying HMCAS. In terms of diagnostic performance, DCNN for HMCAS offered 82.9% sensitivity, 89.7% specificity, and 86.5% accuracy in leave-one-case-out cross-validation. Area under the receiver operating characteristic curve for HMCAS was 0.947 (95% confidence interval 0.895–0.998; P

The volume of the temporal horn of the lateral ventricle (THLV) on brain computed tomography (CT) images is important for neurologic diagnosis. Our purpose in this study was to develop a z-score-based semi-quantitative analysis for estimation of the THLV volume by using voxel-based morphometry. The THLV volume was estimated by use of a z-score mapping method that consisted of four main steps: anatomic standardization, construction of a normal reference database, calculation of the z score, and calculation of the mean z score in a volume of interest (VOI). A mean z score of the CT value obtained from a VOI around the THLV was used as an index for the THLV volume. CT scans from 50 subjects were evaluated. For evaluation of the accuracy of this method for estimating the THLV volume, the THLV volume was determined manually by neuroradiologists (serving as the reference volume). A mean z score was calculated from the VOI for each THLV of the 50 subjects by use of the proposed method. The accuracy of this method was evaluated by use of the relationship between the mean z score and the reference volume. The quadratic polynomial regression equation demonstrated a statistically significant correlation between the mean z score and the reference volume of the THLV (R (2) = 0.94; P

Many women feel a physical burden, which usually appears as a pain, during mammography. We examined the pain-related pupil dilation response during mammography. Twenty-four healthy women participated in the examination. They were randomly assigned to water group or sucrose group. Water group and sucrose group ingested distilled water and a sucrose solution during mammography, respectively. Their pupil sizes were measured during mammography excluding X-ray irradiation. The subjective intensity of pain during mammography was also assessed using visual analogue scales (VAS). The mean pupil diameter was 2.99 mm in relaxation phase before mammography, while it was 3.15 mm in a few seconds after breast compression (pressure phase). The difference between the two was statistically significant (p < 0.01). There was no correlation between the VAS scores and the pupil sizes. In the pressure phase, the mean pupil sizes of sucrose group and water group were 2.91 mm and 3.42 mm, respectively. The difference between the two reached statistical significance (p < 0.01). These results suggest that we may be able to assess objectively the degree of pain associated with mammography and sweet taste stimuli may be effective to alleviate a women’s burden received during mammography.

In order to quantitatively measure the degree of pain during mammography, we used a visual analog scale (VAS) and a device for quantitative analysis of perception and pain and performed regression analysis of the relationship between pain degree and VAS score. Twenty-four subjects underwent a typical mammogram and we found no correlation between pain degree using the device compared with VAS scoring. It is presumed that the system enables quantitative measurement and comparison more accurately than subjective markers such as VAS in quantitation of a pain produced in an examination with clearly defined physical size (such as mammography).

Mammography is widely performed as a standardized procedure for breast cancer screening; however, women often feel some degree of pain during this procedure. Currently, there are limited options available for alleviating pain related to mammography. A non-medicinal approach to the alleviation of pain involves the effects of laughter on physical and psychological wellbeing. We therefore examined the possibility that humorous stimuli would alleviate the physical burden on women undergoing mammography. We assessed 29 women, 15 women received only conventional mammography (neutral group), while 14 women (humor group) watched a funny video during the same examination. The intensity of pain experienced during mammography was assessed by visual analogue scale (VAS) and the VAS results showed a statistically significant difference (P = 0.007) between the two groups, with the humor group experiencing less pain. In an additional experiment, 14 women in the humor group also underwent conventional mammography without exposure to the funny video and pain was assessed by VAS. We found that the pain experienced during conventional mammography without the funny video was significantly greater than the pain experienced during the same mammography but with the funny video (P = 0.047). These findings suggested the possibility of alleviating pain during mammography by humorous stimuli.

Mammography is carried out in a special position, i.e. “an image is taken while the breast is compressed, stretched and kept in a fixed position”. The purpose of this study was to demonstrate quantitatively and qualitatively the physical and psychological burden due to positioning and breast compression during mammography. Muscle activity of each part of the body during positioning for the standard imaging method, to obtain craniocaudal (CC) view images, was measured in 15 adult females using surface EMG. The associated pain was analyzed using visual analogue scale (VAS) scores. During positioning for the CC view, muscle activity was highest in the biceps (24.44 iEMG/s) followed by the trapezius (17.78 iEMG/s) on the imaging side. Muscle activity of the biceps and the sternocleidomastoid on the imaging side showed significant differences compared with pre-imaging activity (biceps P < 0.031, sternocleidomastoid P < 0.005). The pain during mammography was rated as moderate to severe pain (VAS = 3.3 - 6.7) for CC views. As a result, the activities of not only the muscles directly involved in mammography positioning but also those indirectly involved were high as compared with the normal state. Measurement of muscle activity during mammography is expected to be used effectively, such as in the care to reduce pain for the subjects.

The primary purpose of this paper is to provide a novel wavelet-domain method for digital radiography with low dose examination. Approach of this study is an improved wavelet-transform-based method for potentially reducing radiation dose while maintaining clinically acceptable image quality. The wavelet algorithm integrates the advantages of wavelet-coefficient-weighted method and the existing Bayes Shrink thresholding method. In order to confirm the usefulness of the proposed method, the resolving and noise characteristics of the processed computed radiography images were measured. In addition, variations of contrast and noise with respect to radiation dose were also examined. Finally, to verify the effect of clinical examination, visual evaluations were also performed in lower abdominal area using phantom. Our quantitative results demonstrated that our wavelet algorithm could improve resolution characteristics while keeping the noise level within acceptable limits. Visual evaluation result demonstrated that the proposed method was superior to other published methods. Our proposed method recognized effect on decreasing in exposure dose in lower abdominal radiographs. As a conclusion, our proposed method’s performance is better when compared with that of the 3 conventional methods. The proposed method has the potential to improve visibility in radiographs when a lower radiation dose is applied.

Objective evaluation, e.g., using the modulation transfer function (MTF) or the noise power spectrum (NPS) of an X-ray imaging system may not correctly correspond to the subject's evaluation, e.g., when using the receiver operating characteristic (ROC). Moreover, it is difficult to measure MTF or NPS from clinical images. We therefore applied MTF fractal dimension to an X-ray imaging system. The MTF fractal dimension includes the frequency properties of the human eye in addition to quantitative complexity. In this study, we demonstrated a relationship between basic image quality and MTF fractal dimension using simulated and actual X-ray images.

In this paper, we propose a detection method for blurred regions in radiographs. The method involves edge detection using a Sobel filter, manually determining the region of interest (ROI), feature calculation, and classification using a support vector machine. We applied our method to 14 phantom images (7 normal images, 7 blurred images) and 14 clinical images (12 normal images, 2 blurred images). As a result, the average classification accuracies of ROIs with blurring and ROIs without blurring were 98% and 90% for phantom images and clinical images, respectively.

The hyperdense middle cerebral artery (MCA) dot sign representing a thromboembolus is one of the important computed tomography (CT) findings for acute stroke on unenhanced CT images. Our purpose in this study was to develop an automated method for detection of the MCA dot sign of acute stroke on unenhanced CT images. The algorithm of the method which we developed consisted of 5 major steps: extraction of the sylvian fissure region, initial identification of MCA dots based on the morphologic top-hat transformation, feature extraction of candidates, elimination of false positives (FPs) by use of a rule-based scheme, and classification of candidates using a support vector machine (SVM) classifier with four features. Our database comprised 297 CT images obtained from seven patients with the MCA dot sign. The performance of this scheme for classification of the MCA dot sign was evaluated by means of a leave-one-case out method. The performance of the classification by use of the SVM achieved a maximum sensitivity of 97.5 % (39/40) at a FP rate of 1.28 per image. The sensitivity for detection of the MCA dot sign was 97.5 % (39/40) with a FP rate of 0.5 per hemisphere. The method we developed has the potential to detect the MCA dot sign of acute stroke on unenhanced CT images.

The physical and psychological burden on patients during mammography was analyzed using the autonomic function index in 34 adult females. The indices included heart rate (HR), high frequency (HF) of the R-R interval on an electrocardiogram, and the ratio of HF to LF (low frequency) (LF/HF). HR is an indicator of the degree of mental and physical activity, HF indicates the parasympathetic index and LF/HF indicates the sympathetic index. For the mediolateral oblique (MLO) view in mammography, autonomic function indices were measured before and during mammography. Statistical analysis was performed by the Mann-Whitney U test and the significance level was set at 5%. The autonomic function indices were compared before and during mammography and all showed no significant differences. The average value before imaging and the reference value at rest were compared. The results showed that HR increased 1.3-fold, HF increased 0.4-fold, and LF/HF increased 3.2-fold over reference values. Chronological changes during mammography showed that HF increased and LF/HF decreased from 120 seconds after the start of imaging. From these observations, it is evident that the state before mammography is not the same as the resting state and that the sympathetic index is dominant before imaging. They also suggest that some aspects of the psychological burden experienced during mammography are not due to the pain of breast compression alone.

In this work, the authors present an effective denoising method to attempt reducing the noise in mammographic images. The method is based on using hierarchical correlation of the coefficients of discrete stationary wavelet transforms. The features of the proposed technique include iterative use of undecimated multi-directional wavelet transforms at adjacent scales. To validate the proposed method, computer simulations were conducted, followed by its applications to clinical mammograms. Mutual information originating from information theory was used as an evaluation measure for selection of an optimal wavelet basis function. We examined the performance of the proposed method by comparing it with the conventional undecimated discrete wavelet transform (UDWT) method in terms of processing time-consuming and image quality. Our results showed that with the use of the proposed method the computation time can be reduced to approximately 1/10 of the conventional UDWT method consumed. The results of visual assessment indicated that the images processed with the proposed UDWT method showed statistically significant superior image quality over those processed with the conventional UDWT method. Our research results demonstrate the superiority and effectiveness of the proposed approach.