Search

Background and objective Breast cancer is the major cause of cancer-related mortality in women. However, the death rate can be effectively decreased if the breast cancer can be detected early and treated appropriately. In recent years, many studies have indicated that the elastography has the better diagnosis performance than conventional ultrasound (US). Method In this study, the 3-D tumor contour is obtained by using the proposed segmentation methods and then the features containing texture information, shape information, ellipsoid fitting information are extracted respectively by using the segmented 3-D tumor contour and B-mode images, and the features containing elasticity information are calculated using the same contour and elastographic images. Results In this experiment, totally 40 biopsy-proved lesions containing 20 benign tumors and 20 malignant tumors are used to evaluate the proposed computer-aided diagnosis (CAD) system. From the experimental results, the combination of shape, ellipsoid fitting and elastographic features has the best performance with accuracy 90.50% (36/40), sensitivity 85.00% (17/20), specificity 95.00% (19/20), and the area under the ROC curve Az 0.987. Conclusion The result shows that tumors can be diagnosed more precisely by using the elastography images.

Ten years have passed since the first elastography application: Real-time Tissue Elastography™. Now there are several elastography applications in existence. The Quality Control Research Team of The Japan Association of Breast and Thyroid Sonology (JABTS) and the Breast Elasticity Imaging Terminology and Diagnostic Criteria Subcommittee, Terminology and Diagnostic Criteria Committee of the Japan Society of Ultrasonics in Medicine (JSUM) have advocated breast elastography classifications for exact knowledge and good clinical use. We suggest two types of classifications: the technical classification and the classification for interpretation. The technical classification has been created to use vibration energy and to make images, and also shows how to obtain a good elastic image. The classification for interpretation has been prepared on the basis of interpretation of evidence in this decade. Finally, we describe the character and specificity of each vender equipment. We expect the present guidelines to be useful for many physicians and examiners throughout the world.

Ten years have passed since the first elastography application: Real-time Tissue Elastography™. Now there are several elastography applications in existence. The Quality Control Research Team of The Japan Association of Breast and Thyroid Sonology (JABTS) and the Breast Elasticity Imaging Terminology and Diagnostic Criteria Subcommittee, Terminology and Diagnostic Criteria Committee of the Japan Society of Ultrasonics in Medicine (JSUM) have advocated breast elastography classifications for exact knowledge and good clinical use. We suggest two types of classifications: the technical classification and the classification for interpretation. The technical classification has been created to use vibration energy and to make images, and also shows how to obtain a good elastic image. The classification for interpretation has been prepared on the basis of interpretation of evidence in this decade. Finally, we describe the character and specificity of each vender equipment. We expect the present guidelines to be useful for many physicians and examiners throughout the world.

To apply the acceleration time (AcT) ratio as an additional marker for diagnosing internal carotid artery (ICA) stenosis. Carotid artery sonography was performed in 140 patients, and the AcT ratio was calculated as the AcT of the ICA divided by the AcT of the ipsilateral common carotid artery, and compared with diameter stenosis. There was a significant correlation between diameter stenosis and the AcT ratio. The receiver operating characteristic curve revealed a cutoff level of 1.5, with 90.0 % sensitivity and 93.5 % specificity for stenosis >65 %. Our results indicate that applying the AcT ratio can help in the diagnosis of ICA stenosis.

目 的:パーキンソン病 (Parkinson disease:PD),多系統萎縮症 (multiple system atrophy:MSA),進行性核上性麻痺 (progressive supranuclear palsy:PSP) の患者において経頭蓋超音波検査 (transcranial sonography:TCS) よる中脳黒質の高輝度変化を検討した.方 法:パーキンソン関連疾患連続110 例 (PD 86 例,MSA 12 例,PSP 12 例) と健常者34 例に対しTCSを施行した.中脳黒質を観察しえたPD 47 例,MSA 10 例,PSP 6 例,健常者32 例を解析対象として中脳黒質高輝度所見を評価した.定性評価は高輝度の程度によって視察的にI:none or faint,II:equivocal,III:definite,IV:marked の4 段階に分類した.定量評価は中脳黒質で高輝度変化の面積が0.20 cm2 以上のとき,病的な黒質高輝度変化と定義した.結 果:定性評価では,高輝度範囲が視察的に病的と判定されるIII+IVの割合は,PD 72.4%,MSA 10.0%,PSP 66.7%,健常者3.1%であった.定量評価では,PD 63.8%,MSA 20.0%,PSP 66.7%,健常者9.4%で病的な高輝度変化をみとめた.PD,PSP で病的な高輝度変化の割合が多かった.PSP をPSP-parkinsonism( PSPP)とRichardson's syndrome の2 群に分けた場合,前者では病的な高輝度変化を3 例中3 例 (100%), 後者では3 例中1 例( 33.3%) に認められ,PSP-P で割合が高かった.MSA では10 例中2 例( 20%) に病的な高輝度を認め,いずれもパーキンソン病型の多系統萎縮症であった.結 論:パーキンソン関連疾患における病的な中脳黒質高輝度変化は,疾患特異性というよりも,パーキンソニズムの症候と関連し,ドパミン神経細胞の脆弱性を示す所見と推察された., Objective:We investigated substantia nigra (SN) hyperechogenicitydetermined by transcranial sonography(TCS) to detect abnormalities, and compare findings withthose from Parkinson disease (PD), multiple system atrophy(MSA), progressive supranuclear palsy (PSP) or controlsubjects.Method:In this study, echogenicity of SN was examinedin consecutive 110 parkisonian disorders patients with PD86, MSA12, PSP 11, and 34 control subjects. A sufficientbone window for TCS was available in 47 of 86( 71.2%) inthe PD group, 10 of 12( 86.3 %) in the MSA group, 6 of 11(54.5%) in the PSP group and 32 of 34( 94.1%) in the controlgroup. SN hyperechogenicity was scored using a fourpointscale as follows:I=none or faint, II=equivocal, III=definite, IV=marked. In accordance with previously reportedcut-off values, areas of echogenicity £ 0.19 cm2 wereclassified as normal and areas of echogenicity £ 0.20 cm2were classified as pathological SN hyperechogenicity.Results:The frequency of SN hyperechogenicity, assessedas III and IV scales, was significantly increased in PDpatients, and observed in 72 . 4 % of assessable SN(34/47);qui-squire;p=0.001, vs. controls). The meansize of the SN hyperechogenic area in the PD group, MSAgroup and PSP group was 0.26 cm2±0.13, 0.11 cm2±0.11and 0.23 cm2±0.04, respectively, compared with 0.07 cm2±0.06 in the control group.We have identified two clinical phenotypes, such as Richardson'ssyndrome (RS) and PSP-parkinsonism (PSP-P).All of three PSP-P (100%) patients showed a pathologicalSN hyperechogenicity.Conclusion:SN hyperechogenicity was associated with asymptom of parkinsonism rather than disease specificity,and suggested a vulnerability marker of the dopaminergicneuron.

Purpose: Breast ultrasound(US) is recently becoming more and more popular for detecting breast lesions. However, screening results in hundreds of USimages for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole breast into a series of full-view images, and a fully automatic screening system that works off these images is also presented. Methods: Using the registration technique based on the simple sum of absolute block-mean difference (SBMD) measure, three-pass images were merged into full-view USimages. An automatic screening system was then developed for detecting tumors from these full-view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray-level slicing method was then used to divide images into numerous regions. Finally, seven computerized features—darkness, uniformity, width-height ratio, area size, nonpersistence, coronal area size, and region continuity—were defined and used to determine whether or not each region was a part of a tumor. Results: In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CADsystem was 92.3% with 1.76 false positives per case. Conclusions: The proposed automatic screening system can be applied to the whole breast images stitched together via SBMD-based registration in order to detect tumors.

The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24-75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Student's t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features- mean (0.87), median (0.86) and mode (0.83)-were significantly higher than the Az values for the six B-mode features (0.54-0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors.

Breast density has been established as an independent risk factor associated with the development of breast cancer. The terms mammographic density and breast density are often used interchangeably, since most breast density studies are performed with projection mammography. It is known that increase in mammographic density is associated with an increased cancer risk. A sensitive method that allows for the measurement of small changes in breast density may provide useful information for risk management. Despite the efforts to develop quantitative breast density measurements from projection mammograms, the measurements show large variability as a result of projection imaging, differing body position, differing levels of compression, and variation of the x-ray beam characteristics. This study used two separate computer-aided methods, threshold-based and proportion-based evaluations, to analyze breast density on whole breast ultrasound(US)imaging and to compare with the grading results of three radiologists using projection mammography. Thirty-two female subjects with 252 images per case were included in this study. Whole breast USimages were obtained from an Aloka SSD-5500 ultrasound machine with an ASU-1004 transducer (Aloka, Japan). Before analyzing breast density, an adaptive speckle reduction filter was used for removing speckle noise, and a robust thresholding algorithm was used to divide breast tissue into fatty or fibroglandular classifications. Then, the proposed approaches were applied for analysis. In the threshold-based method, a statistical model was employed to determine whether each pixel in the breast region belonged to fibroglandular or fatty tissue. The proportion-based method was based on three-dimensional information to calculate the volumetric proportion of fibroglandular tissue to the total breast tissue. The experimental cases were graded by the proposed analysis methods and compared with the ground standard density classification assigned by a majority voting of three experienced breast radiologists. For the threshold-based method, 28 of 32 US test cases and for the proportion-based density classifier, 27 of 32 US test cases were found to be in agreement with the radiologist “ground standard” mammographic interpretations, resulting in overall accuracies of 87.5% and 84.4%, respectively. Moreover, the concordance values of the proposed methods were between 0.0938 and 0.1563, which were less than the average interobserver concordance of 0.3958. The experiment result showed that the proposed methods could be a reference opinion and offer concordant and reliable quantification of breast density for the radiologist.

Ultrasonography has been an important imaging technique for detecting breast tumors. As opposed tothe conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imagingthe elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicatesthe soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy.In this paper, the authors proposed a computer-aided diagnosis( CAD) system on elastography tomeasure whether this system is effective and accurate to classify the tumor into benign and malignant. Accordingto the features of elasticity, the color elastography was transferred to hue, saturation, and value(HSV) color space and extracted meaningful features from hue images. Then the neural network was utilizedin multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including113 benign and 67 malignant cases used to examine the classification. The results of the proposedsystem showed an accuracy of 83.89 %, a sensitivity of 82.09 % and a specificity of 84.96 %. Compared withthe physician's diagnosis, an accuracy of 78.33 %, a sensitivity of 53.73 % and a specificity of 92.92 %, theproposed CAD system had better performance. Moreover, the agreement of the proposed CAD system andthe physician's diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreementof observers.

A computerized classification scheme to recognize breast parenchymal patterns in whole breast ultrasound (US) images was developed. A preliminary evaluation of the system performance was performed. Breast parenchymal patterns were classified into three categories: mottled pattern (MP), intermediate pattern (IP), and atrophic pattern (AP). Each classification was defined as proposed by an experienced physician. A total of 281 image features were extracted from a volume of interest which was automatically segmented. Canonical discriminant analysis with stepwise feature selection was employed for the classification of the parenchymal patterns. The classification scheme accuracy was computed to be 83.3% (10/12 cases) in MP cases, 91.7% (22/24 cases) in IP cases, 92.9% (13/14 cases) in AP cases, and 90.0% (45/50 cases) in all the cases. The feasibility of an automated ultrasonography classifier for parenchymal patterns was demonstrated with promising results in whole breast US images.

Ultrasonography has been used for breast cancer screening in Japan. Screening using a conventional hand-held probe is operator dependent and thus it is possible that some areas of the breast may not be scanned. To overcome such problems, a mechanical whole breast ultrasound (US) scanner has been proposed and developed for screening purposes. However, another issue is that radiologists might tire while interpreting all images in a large-volume screening; this increases the likelihood that masses may remain undetected. Therefore, the aim of this study is to develop a fully automatic scheme for the detection of masses in whole breast US images in order to assist the interpretations of radiologists and potentially improve the screening accuracy. The authors database comprised 109 whole breast US imagoes, which include 36 masses (16 malignant masses, 5 fibroadenomas, and 15 cysts). A whole breast US image with 84 slice images (interval between two slice images: 2 mm) was obtained by the ASU-1004 US scanner (ALOKA Co., Ltd., Japan). The feature based on the edge directions in each slice and a method for subtracting between the slice images were used for the detection of masses in the authors proposed scheme. The Canny edge detector was applied to detect edges in US images; these edges were classified as near-vertical edges or near-horizontal edges using a morphological method. The positions of mass candidates were located using the near-vertical edges as a cue. Then, the located positions were segmented by the watershed algorithm and mass candidate regions were detected using the segmented regions and the low-density regions extracted by the slice subtraction method. For the removal of false positives (FPs), rule-based schemes and a quadratic discriminant analysis were applied for the distribution between masses and FPs. As a result, the sensitivity of the authors scheme for the detection of masses was 80.6% (29/36) with 3.8 FPs per whole breast image. The authors scheme for a computer-aided detection may be useful in improving the screening performance and efficiency.

Ultrasound imaging plays an important role in the field of breast cancer diagnosis because of its convenience and non-invasive. Recently, the development of computer-aided diagnosis (CAD) provides a convenient way for doctors in detecting breast cancer using ultrasound images. However, the previous CAD systems have some limits with the requirements of human intervention. Hence, in this paper, a novel automatic CAD system is proposed to find suspicious frames among whole breast ultrasound images. After applying watershed segmentation, suspicious segmented regions can be identified through several criteria defined according to the statistic and geometric features of a tumour. By examining 13 US test cases, almost all the tumours and cysts could successfully be detected when an average of two false positives for each case is produced. The experimental results prove the accuracy of this proposed system.

To confirm whether the microbubble activated by sonication causes liver damage, the authors performed two experiments. Experiment 1 followed Shigeta's experiment. As an ultrasound contrast agent (UCA), Levovist was used and sonicated by Acuson Sequoia™ 512 by Siemens. Wister rats of 300 g were divided into four groups: (A) administration of ultrasonic contrast agent (UCA) and ultrasonic exposure, (B) administration of UCA, (C) ultrasonic exposure, and (D) control. Experiment 1 did not show the evidence of hepatic damage such as Shigeta et al. reported. Hence, we conducted Experiment 2 with a much higher acoustic output. But no clear damage was observed in both optical and electron microscopic specimens.

The objective of our study was to compare the efficacy of contrast-enhanced ultrasound (CEUS) using the ultrasound contrast agent Sonazoid (perflubutane) with unenhanced ultrasound and supplementary contrast-enhanced MRI in the differential diagnosis (benign vs malignant) of focal breast lesions. The safety of Sonazoid was also assessed in this study.A total of 127 patients with focal breast lesions were enrolled in this study at five centers in Japan. Three reviewers who were blinded to the patient characteristics independently assessed the ultrasound images and MR images in a randomized sequence. The accuracy, sensitivity, and specificity of CEUS, unenhanced ultrasound, and supplementary contrast-enhanced MRI for the differential diagnosis were compared using generalized estimating equation analyses. Diagnostic confidence was also assessed.The accuracy of CEUS was significantly higher than that of unenhanced ultrasound (87.2% vs 65.5%, respectively; p0.001). In addition, CEUS showed significantly higher specificity, although the improvement in sensitivity was not statistically significant. The accuracy and specificity were significantly higher with CEUS than with contrast-enhanced MRI, but the improvement in sensitivity was not statistically significant. The area under the curve in a receiver operating characteristic analysis was significantly greater with CEUS than with unenhanced ultrasound. The incidence of adverse events was 11.4% and the incidence of adverse drug reactions was 3.3%. All adverse drug reactions were mild.CEUS using Sonazoid was confirmed to be superior to unenhanced ultrasound for the differential diagnosis (benign vs malignant) of focal breast lesions in terms of diagnostic accuracy with no serious adverse reactions.

This study aimed at evaluating the usefulness of 3D real-time tissue elastography (RTE) images for diagnosing breast tumors.A total of 166 breast tumor patients who underwent conventional ultrasound and 2D and 3D RTE examinations were retrospectively analyzed. The 3D RTE images were divided into three types according to the occupancy of blue contrast images. Elastic scores (ES) were obtained by 2D RTE examination. The diagnostic accuracy of 3D and 2D RTE images in differentiating malignant from benign breast tumors was evaluated.Of all cases, 89.4 % of the malignant cases were Type 3 and 86.6 % of the benign cases were Type 1 or 2 according to 3D RTE images. 3D RTE Type 3 and ES ≥ 4 on 2D RTE examination diagnosed malignant tumors with an accuracy of 87.4 and 88.6 %, respectively. In addition, among 34 patients with an ES of 3, the presence of 3D RTE Type 3 diagnosed malignant tumors with an accuracy of 82.4 %.In this study, the utility of 3D RTE images was comparable to that of ES on 2D RTE examination in differentiating malignant from benign breast tumors. In addition, utilizing 3D RTE images in cases with an ES of 3 on 2D RTE may increase the diagnostic accuracy for breast tumors.

BACKGROUND: Cytologic findings of toxoplasmic lymphadenitis (TL) have been only sporadically reported. Intramammary lymph node is an extremely rare site for TL. CASE: A 47-year-old, healthy, female presented with a breast tumor, which was aspirated. The cytomorphologic features were interpreted as suggestive of TL. Histopathology of the excisional biopsy specimen and subsequent serologic examination confirmed the diagnosis. CONCLUSION: We obtained several characteristic findings in aspiration of TL. Of these, epithelioid cell clusters and monocytoid cells were the most diagnostic.

It is relatively easy to detect early gastric cancer in the vicinity of the pylorous ring or invading the duodenum by a use of endoscopy, but the satisfactory precise precise diagnosis of the depth and extent of tumor invasion has not been established as yet. This study was designed to elucidate some problems in the diagnosing ability of gastric endoscopy and EUS for such early gastric cancers. Subjects were 26 patients undergone gastrectomy for early gastric caner in a recent 5-year period, consisting of 21 patients whose center of the lesion situated within 2cm from the pylorous ring and five patients whose tumors invaded the duodenum. The correct diagnosing rate of depth of tumor invasion by gastric endoscopy was 69.2•(18/26) in all patients. On the other hand, it was 57.1•(12/21) in 21 patients who underwent EUS. There was a tendency for EUS to give a read-out of the depth which was deeper than actual depth. Furthermore, early gastric cancer invading the duodenum present difficulty in determining the extent by gastric endoscopy especially on the duodenal side of the lesion. It is probably better that doctors use high frequency EUS equipments.

We report a case of gastric cancer with high serum levels of IL-6 and G-CSF. A 76-year-old man was admitted to the hospital presented with high grade fever. Although biochemical tests indicated an inflammatory reaction (WBC 11, 800/mm3, CRP 13.9mg/dl) and consequently infection, no other evidence of infection was odtained upon further clinical examinations. Furthermore, a possibility of autoimmune disease was ruled out based on the laboratory data and physical findings. Endoscopic examination, prompted by occult blood in the stool, identified two separate gastric cancer lesions in the antrum and body of the stomach. The fever was resistant to various antibiotics, but administration of Naproxen at a dose of 600mg per day p.o. was effective. We reasoned that the fever was in some way associated with the gastric cancer. After a distal partial gastrectomy was performed, a dramatic crisis, involving rapid reduction of fever and a simultaneous decrease in the levels of IL-6 and G-CSF, occurred. Immunohistochemical stainings for both IL-6 and G-CSF on surgical specimens were negative. We believe that the high serum IL-6 and G-CSF levels were the result of immunoreaction to gastric cancer rather than direct secretion from the cancer cells.

1987年より栃木県保健衛生事業団が実施している超音波併用乳癌住民検診を紹介し, さらに1992年度より導入した超音波画像の静止画記録による読影システムの精度評価を行った。われわれが行っている栃木県内の乳癌検診は医師による触診と続いてこの医師の指示により検査技師が行う超音波検査で, 画像は静止画としてディジタル光磁気ディスクに記録され読影医に送られ, 検査技師立ち合いのもと読影されて要精検者が判定される。過去6年間の総受診者は105,845名で発見乳癌は70例, 乳癌発見率は0.07%である。要精検率は超音波検査による絞り込み効果により低率で, 平均2.1%, 1993年度では1.7%であった。また発見乳癌のうち早期乳癌は46.4%であり, 特に継年受診者の中では73.3%であった。超音波静止画記録読影法を採用した1992年度と1993年度の要精検者の超音波診断と精検調査票の結果との照合から超音波検査システムのROCカーブを作成すると, カーブ特性は良好で検査感度をかなり維持しながら要精検率をさらに低下できる可能性が示唆された。存在診断に徹した視・触診と質的診断を重視した超音波検査の組合せによる本検診システムは比較的少ない設備とスタッフにより充分な検診効率を持つことが示された。

We compared the contrast effect of three doses of DD-723 in subjects with breast tumors to determine the recommended dose. We then evaluated differential diagnosis results using plain ultrasonography, contrast-enhanced ultrasonography (plain + enhanced), and contrast-enhanced magnetic resonance imaging (MRI) compared to the pathological diagnosis. To evaluate the contrast effect, contrast-enhanced ultrasonic images were independently evaluated in a randomized sequence by three blinded reviewers trained in the evaluation method beforehand. Multiple evaluation results from the three reviewers were used to assess the overall contrast effect. The differential diagnosis was evaluated independently by three blinded reviewers using contrast-enhanced ultrasonic images and contrast-enhanced magnetic resonance images in a randomized sequence; reviewers were also blinded to subject characteristics. Multiple evaluation results from the three reviewers were used to assess the overall differential diagnosis. The recommended dose of DD-723 is an intermediate dose of 0.12 μL MB/kg. Accuracy, sensitivity, and specificity were improved more in the differential diagnosis by contrast-enhanced ultrasonography than in plain ultrasonography. Accuracy and specificity were better and sensitivity similar compared to contrast-enhanced MRI. An intermediate dose showed the highest efficacy in terms of overall contrast effect. Contrast-enhanced ultrasonography is safe and useful when used in differential diagnosis.

Purpose The Ultrasound Equipment and Safety Committee of The Japan Society of Ultrasonics in Medicine performed experiments to confirm whether contrast-enhanced ultrasonography damages liver cells. Methods Rats were injected with 0.1 ml of 300 mg/ml ultrasound contrast agent (UCA). Diagnostic ultrasound pulses with a center frequency of 6 MHz and a mechanical index of 1.9 were applied to rat livers with a water bag as a coupler to maintain a distance of 2–6 cm between the ultrasound probe surface and the liver. Contrast-enhanced ultrasonography was carried out for 10 s to visualize the entire liver. Then, specimens of liver tissue were fixed using two types of fixation: immersion and perfusion fixation. Results Although some variations were found in electron micrographs of liver tissue fixed using immersion fixation, none of three blinded readers found any significant differences between micrographs of liver tissue from rats receiving UCA with sonication and those from sham-treated control rats. Changes observed were not thought to be group-specific but instead due to differences between individual rats. When the livers were fixed using perfusion fixation and the hepatic vein was cut after injection of physiological saline for perfusion, a large number of vacuoles ≥2 μm in diameter were observed. This finding suggested that hepatic cell damage observed in this study was caused by high perfusion pressure during the liver fixation process rather than by sonication with UCA. Conclusion Blinded readings of electron micrographs showed no clear evidence that the use of Levovist in ADI mode ultrasonography causes significant damage to liver tissue.

Breast ultrasound (US) is recently becoming more and more popular for detecting breast lesions. However, screening results in hundreds of US images for each subject. This magnitude of images can lead to fatigue in radiologist, causing failure in the detection of lesions of a subtle nature. In this study, an image stitching technique is proposed for combining multipass images of the whole breast into a series of full-view images, and a fully automatic screening system that works off these images is also presented.Using the registration technique based on the simple sum of absolute block-mean difference (SBMD) measure, three-pass images were merged into full-view US images. An automatic screening system was then developed for detecting tumors from these full-view images. The preprocessing step was used to reduce the tumor detection time of the system and to improve image quality. The gray-level slicing method was then used to divide images into numerous regions. Finally, seven computerized features--darkness, uniformity, width-height ratio, area size, nonpersistence, coronal area size, and region continuity--were defined and used to determine whether or not each region was a part of a tumor.In the experiment, there was a total of 25 experimental cases with 26 lesions, and each case was composed of 252 images (three passes, 84 images/pass). The processing time of the proposed stitching procedure for each case was within 30 s with a Pentium IV 2.0 processor, and the detection sensitivity of the proposed CAD system was 92.3% with 1.76 false positives per case.The proposed automatic screening system can be applied to the whole breast images stitched together via SBMD-based registration in order to detect tumors.

The aim of this paper is to investigate a 2.5-dimensional approach in classifying masses as benign or malignant in volumetric anisotropic voxel whole breast ultrasound data In this paper, the term 2.5-dimensional refers to the use of a series of 2-dimensional images While mammography is very effective in breast cancer screening in general, it is less sensitivity in detecting breast cancer in younger women or women with dense breasts Breast ultrasonography does not have the same limitation and is a valuable adjunct in breast cancer detection We have previously reported on the clinical value of volumetric data collected from a prototype whole breast ultrasound scanner The current study focuses on a new 2.5-dimensional approach in analyzing the volumetric whole breast ultrasound data for mass classification Sixty-three mass lesions were studied Of them 33 were malignant and 30 benign Features based on compactness, orientation, shape, depth-to-width ratio, homogeneity and posterior echo were measured Linear discriminant analysis and receiver operating characteristic (ROC) analysis were employed for classification and performance evaluation The area under the ROC curve (AUC) was 0.91 using all breast masses for training and testing and 0.87 using the leave-one-mass-out cross-validation method Clinically significance of the results will be evaluated using a larger dataset from multi-clinics.

Breast density has been established as an independent risk factor associated with the development of breast cancer. The terms mammographic density and breast density are often used interchangeably, since most breast density studies are performed with projection mammography. It is known that increase in mammographic density is associated with an increased cancer risk. A sensitive method that allows for the measurement of small changes in breast density may provide useful information for risk management. Despite the efforts to develop quantitative breast density measurements from projection mammograms, the measurements show large variability as a result of projection imaging, differing body position, differing levels of compression, and variation of the x-ray beam characteristics. This study used two separate computer-aided methods, threshold-based and proportion-based evaluations, to analyze breast density on whole breast ultrasound (US) imaging and to compare with the grading results of three radiologists using projection mammography. Thirty-two female subjects with 252 images per case were included in this study. Whole breast US images were obtained from an Aloka SSD-5500 ultrasound machine with an ASU-1004 transducer (Aloka, Japan). Before analyzing breast density, an adaptive speckle reduction filter was used for removing speckle noise, and a robust thresholding algorithm was used to divide breast tissue into fatty or fibroglandular classifications. Then, the proposed approaches were applied for analysis. In the threshold-based method, a statistical model was employed to determine whether each pixel in the breast region belonged to fibroglandular or fatty tissue. The proportion-based method was based on three-dimensional information to calculate the volumetric proportion of fibroglandular tissue to the total breast tissue. The experimental cases were graded by the proposed analysis methods and compared with the ground standard density classification assigned by a majority voting of three experienced breast radiologists. For the threshold-based method, 28 of 32 US test cases and for the proportion-based density classifier, 27 of 32 US test cases were found to be in agreement with the radiologist "ground standard" mammographic interpretations, resulting in overall accuracies of 87.5% and 84.4%, respectively. Moreover, the concordance values of the proposed methods were between 0.0938 and 0.1563, which were less than the average interobserver concordance of 0.3958. The experiment result showed that the proposed methods could be a reference opinion and offer concordant and reliable quantification of breast density for the radiologist.

Background Substantia nigra (SN) hyperechogenicity determined by transcranial sonography (TCS) and olfactory dysfunction are common findings in Parkinson disease (PD), which may reveal a prodromal synucleinopathy in idiopathic REM sleep behavior disorder (iRBD). Methods TCS and the Odor Stick Identification Test for Japanese (OSIT-J) were performed in 34 consecutive patients with iRBD (67.9±6.1years), 17 consecutive patients with PD (66.4±6.7years), and 21 control group subjects (64.4±5.8years). Results There was a significantly increased area of echogenicity in the SN in the iRBD group (0.20±0.13cm 2 ) and PD group (0.22±0.11cm 2 ) compared with the control group (0.06±0.06cm 2 ). We found pathological SN hyperechogenicity (⩾0.20cm 2 ) in 41.2% of the iRBD group, 52.6% of the PD group, and 9.5% of the control group. Further, there were abnormal findings of both pathological SN hyperechogenicity (⩾0.20cm 2 ) and functional anosmia or hyposmia in 4 (11.8%) or 9 (26.5%) of the iRBD group subjects, respectively, and 7 (57.9%) or 2 (11.8%) of the PD group subjects, respectively. Conclusion Pathological SN hyperechogenic abnormality and functional anosmia in iRBD may be a disease state in the transition to a neurodegenerative disease.

Ultrasonography is one of the most important methods for breast cancer diagnosis in Japan. In general, the ultrasonography using conventional handheld probe is operator-dependent, therefore, the quality of imaging depends on the operator technique, and the ultrasound images have less objectivity. Moreover, it is difficult for radiologists to interpret using comparison of left and right breast ultrasound images, comparison of previous ultrasound images, and double reading because ultrasound images are poor reproducibility and it is difficult to record entire breast images. In order to improve such problems, we have developed whole breast viewing system with computer-aided detection approach. All of the whole breast images were obtained by the AUS-1004 (ALOKA Co., Ltd., Japan). The detection performance was 80% with 3.8 FP marks per breast. The diagnostic effectiveness of the detection results and the possible browsing methods were implied.

Several whole breast ultrasound (US) scanners have recently been developed for breast cancer screening. In ultrasonographic screening techniques that utilize scanners, assessment of the mammary gland pattern in US images by a radiologist is required. We developed a method of mammary gland analysis to automatically classify whole breast US images into three categories: mottled pattern (MP), intermediate pattern (IP), and atrophic pattern (AP). Our database included 50 patients who underwent US of the entire breast, and they were classified as 12 MP, 24 IP, and 14 AP cases. First, we extracted a volume of interest (VOI) including mammary gland regions. Following this, we extracted image features, i.e., the average pixel value (APV), the number of small hypoechoic regions (SHR), and Haralick's texture features, from the VOI. Finally, a canonical discriminant analysis with APV, SHR, and four texture features was applied for classification of mammary gland patterns. The performance of this classification method was 82.0% (41/50). We found that it is possible to classify whole breast ultrasound images based on mammary gland patterns. The classification method can be applied to estimate the risk of breast cancer based on US images, and it could also be applied in computer-aided diagnosis (CAD) systems for the detection of ultrasonographic breast cancer.

The aim of this paper is to study the use of geometric and echo features in classifying masses in ultrasound images as benign or malignant. While mammography is very effective in detecting masses and other lesions, breast ultrasound is a valuable adjunct in distinguishing solid and fluid-filled masses where the former is mostly malignant and the latter benign. Six features including two geometric features and four echo features derived from the segmented mass and its neighboring regions are employed in this study. They are the compactness and orientation of the mass, two intensity ratios of the mass and its neighboring regions, homogeneity, and depth-to-width ratio of the mass. Linear discriminant analysis and receiver operating characteristic (ROC) analysis are employed for classification and performance evaluation. The area under the ROC curve (AUC) has a value of 0.940 using all breast masses for training and testing and 0.923 using the leave-one-mass-out cross-validation method. Clinically significance of the results will be evaluated using a larger dataset.