Biomedical subjects

Jong Hyo Kim

Publications and source records attributed to Jong Hyo Kim.

12 recordsLinked to original sources

PubMed2006

Comparison of fundamental sonography, tissue-harmonic sonography, fundamental compound sonography, and tissue-harmonic compound sonography for focal hepatic lesions.

Image qualities of fundamental, tissue-harmonic, fundamental compound, and tissue-harmonic compound sonography for evaluating focal hepatic lesions were compared. Two radiologists, blinded to the type of techniques and to the final diagnosis, independently evaluated 384 images of 96 hepatic lesions: hemangiomas (n=35), hepatic cystic lesions (n=28), cirrhosis-related nodules (n=22), focal nodular hyperplasia (n=1), and metastases (n=10). All images were graded in terms of lesion conspicuity, margin sharpness, and overall image quality using a 4- or 5-point scale. In the cases of cystic lesions, posterior acoustic enhancement and internal artifacts were also analyzed. A Friedman test was used for multiple statistical comparisons of the four techniques for all parameters. Compound imaging was significantly superior to fundamental imaging regarding lesion conspicuity, margin sharpness, and overall quality (P<0.05). For posterior enhancement and internal artifacts within the cyst, harmonic ultrasonography (US) was significantly better than fundamental US (P<0.05). For evaluating focal hepatic lesions on US, compound imaging provided better lesion conspicuity, better margin sharpness, and better overall image quality than fundamental imaging did. Tissue harmonic imaging also provided better posterior enhancement and fewer internal artifacts of the cyst than fundamental imaging.

Adult↗

PubMed2005

Computer-aided diagnosis of localized ground-glass opacity in the lung at CT: initial experience.

The purpose of this study was to develop an automated scheme to facilitate detection of localized ground-glass opacity (GGO) in the lung at computed tomography (CT). Institutional review board approval and informed consent were not required. Two radiologists reviewed CT images from 14 patients (five men, nine women) who had lung cancer or metastasis and whose malignancy was classified as GGO. The lung region was sampled and completely covered with contiguous, 50% overlapping regions of interest (ROIs) measuring 30 x 30 pixels in size. The lung area within each ROI was analyzed to compute texture features and gaussian curve fitting features. Performance of the artificial neural networks (ANNs) measured by using the area under the receiver operating characteristic curve was 0.92. With a threshold of 0.9, the sensitivity of the ANN for detecting GGO ROIs was 94.3% (280 of 297 ROIs), and the positive predictive value was 29.1% (280 of 963 ROIs). A computerized scheme may hold promise in facilitating detection of localized GGO at CT.

Aged↗

PubMed2005

Appropriateness of a donor liver with respect to macrosteatosis: application of artificial neural networks to US images--initial experience.

PURPOSE: To retrospectively compare performance of artificial neural networks (ANNs) applied to ultrasonographic (US) images with that of radiologists for prediction of appropriateness of a donor liver with respect to macrosteatosis before liver transplantation. MATERIALS AND METHODS: Institutional ethics committee approved study; written informed consent was obtained. ANNs, constructed with three-layered 15-neuron back-propagation algorithm, were trained to predict appropriateness of a donor liver with respect to macrosteatosis by using statistically significant laboratory and US parameters derived from univariate analyses, together with correct diagnosis. Input variables for ANNs were alkaline phosphatase, glutamic oxaloacetic transaminase, glutamic pyruvate transaminase, gamma-glutamyltransferase, hepatorenal ratio of echogenicity, and tail area ratio and tail length of portal vein wall echogenicity. Three radiologists graded US images in 94 potential donors (71 men and 23 women) on the basis of four degrees of hepatic steatosis. After training and testing of ANNs, performance of ANNs and radiologists in predicting appropriateness of potential donors was evaluated with receiver operating characteristic (ROC) analysis and compared by means of univariate z score test. RESULTS: Among 94 potential donor livers, 76 were normal or had mild steatosis, and 18 had moderate or severe macrosteatosis at histopathologic examination. Area under ROC curve (Az) of ANNs (Az=0.9673) was significantly greater than that of radiologists (faculty, Az=0.9106, P=.048; fellow, Az= 0.9038, P=.044; resident, Az=0.8931, P=.038). No statistically significant difference in sensitivity for predicting appropriateness as a liver donor with respect to macrosteatosis was found between ANNs (88.9%) and radiologists (P >.05). However, specificity of ANNs (96.1%) was significantly better than that of radiologists (P <.003). CONCLUSION: ANNs might be a useful tool to categorize whether a donor liver is appropriate for transplantation with respect to macrosteatosis on the basis of multiple variables related to laboratory and US features. Further study is needed.

Adolescent↗

PubMed2005

Computerized scheme for assessing ultrasonographic features of breast masses.

RATIONALE AND OBJECTIVE: To evaluate the ultrasonographic features of breast masses using a computerized scheme and to correlate the feature values with radiologists' grading. MATERIALS AND METHODS: One hundred and seventy-five breast ultrasound images (one to five images per subject) from 61 women (age 17-89 years, mean 43 years) were studied. Thirty-eight of the 157 images were from 11 women with malignant lesions, and the remaining 137 were from 50 patients with benign lesions. Two breast imaging radiologists participated in an observer performance study and were asked to grade, on a scale of 3, shape (1: regular, 3: very irregular), border (1: sharp, 3: ill-defined), internal texture (1: homogeneous, 3: very heterogeneous), width/depth ratio (1: flat, 3: tall), posterior enhancement (1: strong, 3: none), and lateral shadowing (1: strong, 3: none). The computerized scheme analyzed the breast region within a region of interest that was placed by a radiologist and quantified the following parameters: shape (jag count, disperse, convex hull depth, and lobulation count), border (acutance, average maximum ascending gradient, and sigmoid curve fitting), texture (edge density, co-occurrence matrix, and fractal dimension), width-depth ratio, posterior enhancement, and lateral shadowing. Correlations between the radiologists and the computerized scheme for assessing parameters in corresponding categories were computed. RESULTS: Good agreement was seen in posterior enhancement (P < .001, r = 0.45), lateral shadowing (P < .001, r = 0.38), width-depth ratio (P < .001, r = 0.33), and shape features (all P < .001): jag count (r = 0.38), disperseness (r = 0.55), and convex hull depth (r = 0.44). The remaining parameters demonstrated a poor or weak correlation (r < 0.30). CONCLUSION: The radiologists and the computerized scheme correlated best in analysis of shape features and posterior enhancement. We have yet to determine the significance of these features for the implementation of a computer-aided diagnosis program for characterizing breast ultrasound masses.

Adolescent↗

PubMed2004

Effect of monitor luminance and ambient light on observer performance in soft-copy reading of digital chest radiographs.

PURPOSE: To examine the combined effects of monitor luminance and ambient light on observer performance for detecting abnormalities in a soft-copy interpretation of digital chest radiographs. MATERIALS AND METHODS: A total of 254 digital chest radiographs were displayed on a high-resolution cathode ray tube monitor at three luminance levels (25, 50, and 100 foot-lamberts) under three ambient light levels (0, 50, and 460 lux). Six chest radiologists reviewed each image in nine modes of combined luminance and ambient light. The observers were allowed to adjust the window width and level of the soft-copy images. The abnormalities included nodule, pneumothorax, and interstitial disease. Observer performance was analyzed in terms of the receiver operating characteristics. The observers reported their subjective level of visual fatigue with each viewing mode. A statistical test was conducted for each of the abnormalities and for fatigue score by using repeated-measures two-way analysis of variance with an interaction. RESULTS: The detection of nodules was the only reading that was affected by the ambient light with a statistically significant difference (P <.05). Otherwise, observer performance for detecting a nodule, pneumothorax, and interstitial disease was not significantly different in the nine-mode comparison. There was no evidence that the luminance of the monitors was related to the ambient light for any of the abnormalities. The fatigue score showed a statistically significant difference due to both the luminance and ambient light. CONCLUSION: When adequate window width and level are applied to soft-copy images, the primary diagnosis with chest radiographs on the monitor is unlikely to be affected under low ambient light and a monitor luminance of 25 foot-lamberts or more.

Computer Terminals↗

PubMed2004

The potential contribution of a computer-aided detection system for lung nodule detection in multidetector row computed tomography.

RATIONALE AND OBJECTIVES: We sought to evaluate the potential benefits of a computer-aided detection (CAD) system for detecting lung nodules in multidetector row CT (MDCT) scans. METHODS: A CAD system was developed for detecting lung nodules on MDCT scans and was applied to the data obtained from 15 patients. Two chest radiologists in consensus established the reference standard. The nodules were categorized according to their size and their relationship to the surrounding structures (nodule type). The differences in the sensitivities between an experienced chest radiologist and a CAD system without user interaction were evaluated using a chi2 analysis. The differences in the sensitivities also were compared in terms of the nodule size and the nodule type. RESULTS: A total of 309 nodules were identified as the reference standard. The sensitivity of a CAD system (81%) was not significantly different from that of a radiologist (85%; P > 0.05). The sensitivities of the CAD system for detecting nodules < or = 5 mm in diameter as well as detecting isolated nodules were higher than those of a radiologist (83% vs. 75%, P > 0.05; 93% vs. 76%, P < 0.001). The sensitivities of a radiologist for detecting nodules >5 mm and the nodules attached to other structures were higher than those of a CAD system (98% vs. 79%, P < 0.001; 91% vs. 71%, P < 0.001). There were 28.8 false-positive results of CAD per CT study. CONCLUSION: The CAD system developed in this study performed the nodule detection task in different ways to that of a radiologist in terms of the nodule size and the nodule type, which suggests that the CAD system can play a complementary role to a radiologist in detecting nodules from large CT data sets.

Adult↗

PubMed2003

Contrast-enhanced MR imaging of postoperative scars and VX2 carcinoma in rabbits: comparison of macromolecular contrast agent and gadopentetate dimeglumine.

PURPOSE: To compare the magnetic resonance (MR) imaging enhancement patterns of a blood pool contrast agent, SH L 643A, with those of gadopentetate dimeglumine in postoperative scars and VX2 carcinomas in rabbits and to compare these enhancement patterns with microvessel density in pathologic specimens. MATERIALS AND METHODS: Eighteen rabbits with experimentally induced postoperative scars (n = 12) or VX2 carcinoma (n = 6) in the thighs underwent sequential MR imaging first with gadopentetate dimeglumine and then, 24 hours later, with SH L 643A. The enhancement ratios (ie, the ratios of postcontrast to precontrast signal intensity) and the microvessel densities of postoperative scars and VX2 carcinomas were assessed. Differences were tested for by using the Mann-Whitney U and Wilcoxon signed rank tests. RESULTS: In postoperative scars, enhancement ratios were consistently lower with injection of SH L 643A than with injection of gadopentetate dimeglumine for up to 30 minutes (P <.05). In postoperative scars, mean peak enhancement ratios were 1.29 +/- 0.15 (SD) with injection of SH L 643A and 1.61 +/- 0.31 with injection of gadopentetate dimeglumine (P <.01). In VX2 carcinomas, the enhancement ratios were not significantly different with injection of SH L 643A than with injection of gadopentetate dimeglumine at all time points. The mean difference between the enhancement ratios of the VX2 carcinomas and postoperative scars was 0.64 +/- 0.10 (range, 0.50-0.77) with SH L 643A and 0.36 +/- 0.16 (range, 0.17-0.66) with gadopentetate dimeglumine (P <.01). The mean microvessel density (in terms of vessels per field of view) was 10.7 +/- 5.5 for postoperative scars and 30.0 +/- 7.7 for VX2 carcinoma (P <.001). CONCLUSION: The difference between the enhancement ratios of postoperative scars and VX2 carcinomas with SH L 643A was greater than that with gadopentetate dimeglumine. Enhancement ratios at SH L 643A-enhanced MR imaging corresponded well with microvessel density in postoperative scars and VX2 carcinomas.

Animals↗

PubMed2002

Classification of malignant and benign tumors using boundary characteristics in breast ultrasonograms.

We evaluated various spiculate and jagged margin shape features. These are known to be malignant characteristics in breast sonograms. A total of 79 breast ultrasonograms (60 benign, 19 malignant) containing solid breast nodules were evaluated. To determine the boundary of lesions, Markov random field segmentation was used. Our goal was to classify benign and malignant lesions on the breast sonogram. Our algorithm consisted of two steps: segmentation and classification. In the first step, a breast sonogram was segmented using low resolution and Gaussian-Markov random field. The fuzzy clustering method algorithm was then applied to the preprocessed image to initialize the segmentation. Next, to discriminate benign and malignant tumors three types of lesion characteristics were investigated: jag count, compactness, and acutance. Jag count was calculated based on the derivative of curvature, acutance was defined as gray-level variations across the lesion boundary, and compactness was defined as the ratio of boundary complexity to the enclosed area. Sensitivity of the three boundary features (jag count, compactness, and acutance) was 95.1, 94.1, and 81.1%, respectively, and their specificities were 97.2, 92.0, and 78%, respectively. The jag count performed best among the three boundary features. Our results indicate that computerized analysis of boundary characteristics can be an effective method for classifying solid breast nodules in ultrasonograms as malignant or benign. We found that curvature analysis was the best shape features. The curvature method classifies better than the compactness or acutance methods.

Breast Neoplasms↗

PubMed2002

A straightforward algorithm for the quantification of power Doppler signals.

RATIONALE AND OBJECTIVES: To document a simple algorithm that quantifies power Doppler signals by computer-based statistical image analysis. METHODS: Personal computer-based software was developed. Color pixels representing power Doppler signals were separated from the background gray pixels in power Doppler images converted in BMP format. To avoid misregistration during this segmentation, a threshold value is required to adequately distinguish between gray and color pixels. Equating the intensity of each color pixel to a Power Value (PV), a histogram of Power Values in power Doppler images was obtained. RESULTS: The software was designed to allow users to adjust two thresholds (Difference Threshold and Intensity Threshold) by observing the segmentation results in real time. The software calculates the following indexes within any region of interest as defined by the operator: mean PV, peak PV, standard deviation of PV, percentage power Doppler area, and integrated PV per unit area. CONCLUSIONS: Power Doppler signals can be objectively quantified using this straightforward algorithm. This simple and practical method can be applied to comparative or longitudinal studies upon vascularity or blood flow.

Algorithms↗

PubMed2002

Detection of simulated chest lesions by using soft-copy reading: comparison of an amorphous silicon flat-panel-detector system and a storage-phosphor system.

PURPOSE: To compare observer performance by using soft-copy images produced by an amorphous silicon flat-panel-detector system and a storage-phosphor system for the detection of simulated chest lesions. MATERIALS AND METHODS: To test the diagnostic performance of these two systems, four types of simulated lesions (nodules, micronodules, lines, and reticular opacities) were superimposed over an anthropomorphic chest phantom. Digital chest radiographs were acquired with amorphous silicon flat-panel-detector (3-K [K = 1,000] matrix, 12 bits) and storage-phosphor radiography (4-K matrix, 10 bits). Six board-certified radiologists evaluated soft-copy images on a high-resolution video monitor (2,048 x 2,560 x 8 bits). A total of 14,400 observations were analyzed in terms of receiver operating characteristics. RESULTS: Average performance in terms of nodule detection was significantly better (P <.05) with the flat-panel-detector system than with the storage-phosphor system. For micronodules, lines, and reticular opacities, no significant detection differences in averaged performance were found between the two detector systems. CONCLUSION: In the evaluation of soft-copy images, the amorphous silicon detector system appears to be superior to the storage-phosphor system for the detection of pulmonary nodules.

Lung Diseases↗

PubMedDate not supplied

Low-dose spiral CT: application to surface-rendered three-dimensional imaging of central airways.

PURPOSE: The purpose of this work was to assess the utility of low-dose spiral CT for three-dimensional imaging of the central airways. METHOD: Spiral CT was performed in 15 patients using two tube currents (50 and 240 mA), producing 30 CT data sets. Surface-rendered virtual bronchoscopy (VB) and shaded surface display (SSD) images were assessed by three radiologists for image quality. The radiologists were also asked to compare 15 matched pairs each of 50 and 240 mA VB and SSD images, blindly select the 240 mA image, and record differences in diagnostic quality between the matched images. RESULTS: No significant difference in image quality was noted with 50 or 240 mA. Only 51.1% of 240 mA SSD and 51.1% of 240 mA VB images were correctly identified. Differences in diagnostic quality, noted in 84.4% of SSD and 33% of VB image pairs, were not ascribed to image noise. CONCLUSION: Image quality of surface-rendered three-dimensional images of the central airways is preserved using a lower tube current.

Adult↗

PubMedDate not supplied

Automated lung nodule detection at low-dose CT: preliminary experience.

OBJECTIVE: To determine the usefulness of a computer-aided diagnosis (CAD) system for the automated detection of lung nodules at low-dose CT. MATERIALS AND METHODS: A CAD system developed for detecting lung nodules was used to process the data provided by 50 consecutive low-dose CT scans. The results of an initial report, a second look review by two chest radiologists, and those obtained by the CAD system were compared, and by reviewing all of these, a gold standard was established. RESULTS: By applying the gold standard, a total of 52 nodules were identified (26 with a diameter < or = 5 mm; 26 with a diameter >5 mm). Compared to an initial report, four additional nodules were detected by the CAD system. Three of these, identified only at CAD, formed part of the data used to derive the gold standard. For the detection of nodules >5 mm in diameter, sensitivity was 77% for the initial report, 88% for the second look review, and 65% for the CAD system. There were 8.0+/-5.2 false-positive CAD results per CT study. CONCLUSION: These preliminary results indicate that a CAD system may improve the detection of pulmonary nodules at low-dose CT.

Adult↗