• Journal of Radiopharmaceuticals and Molecular Probes
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• v.1 no.2
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• pp.130-136
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• 2015

We evaluate the influence of MR contrast agent on positron emission tomography (PET) image using phantom, animal and human studies. Phantom consisted of 15 solutions with the mixture of various concentrations of Gd-based MR contrast agent and fixed activity of [$^{18}F$]FDG. Animal study was performed using rabbit and two kinds of MR contrast agents. After injecting contrast agent, CT or MRI scanning was performed at 1, 2, 5, 10, and 20 minutes. PET image was obtained using clinical PET/CT scan, and attenuation correction was performed using the all CT images. The values of HU, PET activity and MRI intensity were obtained from ROIs in each phantom and organ regions. In clinical study, patients (n=20) with breast cancer underwent sequential acquisitions of early [$^{18}F$]FDG PET/CT, MRI and delayed PET/CT. In phantom study, as the concentration increased, the CT attenuation and PET activity also increased. However, there was no relationship between the PET activity and the concentration in the clinical dose range of contrast agent. In animal study, change of PET activity was not significant at all time point of CT scan both MR contrast agents. There was no significant change of HU between early and delayed CT, except for kidney. Early and delayed SUV in tumor and liver showed significant increase and decrease, respectively (P<0.05). Under the condition of most clinical study (< 0.2 mM), MR contrast agent did not influence on PET image quantitation.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.493-497
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• 2016

Infrared (IR) imaging is capable of detecting targets that are not visible at night, thus it has been widely used for the security and defense system. However, the quality of the IR image is often degraded by low resolution and noise corruption. This paper addresses target segmentation with the IR image. Multiple regions of interest (ROI) are extracted by the multi-level segmentation and targets are segmented from the individual ROI. Each level of the multi-level segmentation is composed of a k-means clustering algorithm an expectation-maximization (EM) algorithm, and a decision process. The k-means clustering algorithm initializes the parameters of the Gaussian mixture model (GMM) and the EM algorithm iteratively estimates those parameters. Each pixel is assigned to one of clusters during the decision. This paper proposes the selection and the merging of the extracted ROIs. ROI regions are selectively merged in order to include the overlapped ROI windows. In the experiments, the proposed method is tested on an IR image capturing two pedestrians at night. The performance is compared with conventional methods showing that the proposed method outperforms others.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.45-48
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• 2019

Detection of crosswalk is an important issue for the blind to walk without the help of others. There is a braille block on the sidewalk, which helps the blind to walk. On the other hand, crosswalk is more dangerous due to the moving vehicles. However, there is no appropriate means to induce the blind. In this paper, we propose a method to detect crosswalk in front of a blind and estimate its direction using an image sensor. We adopt multi-ROIs and make their binary versions. In order to determine whether it is a crosswalk, two features are extracted; one is the number of crossing in the binary image and the other is the ratio of white area. We can also estimate the direction of the crosswalk through the slope of the projection data. We evaluated the performance using experimental dataset and the proposed algorithm showed 80% accuracy of detection.

• Progress in Medical Physics
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• v.32 no.4
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• pp.92-98
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• 2021

Purpose: This study automatically discriminates homogeneous and structure edge regions on computed tomography (CT) images, and it evaluates the noise level and edge preservation ratio (EPR) according to the different types of iterative reconstruction (IR). Methods: The dataset consisted of CT scans of 10 patients reconstructed with filtered back projection (FBP), statistical IR (iDose⁴), and iterative model-based reconstruction (IMR). Using the 10th and 85th percentiles of the structure coherence feature, homogeneous and structure edge regions were localized. The noise level was estimated using the averages of the standard deviations for five regions of interests (ROIs), and the EPR was calculated as the ratio of standard deviations between homogeneous and structural edge regions on subtraction CT between the FBP and IR. Results: The noise levels were 20.86±1.77 Hounsfield unit (HU), 13.50±1.14 HU, and 7.70±0.46 HU for FBP, iDose⁴, and IMR, respectively, which indicates that iDose⁴ and IMR could achieve noise reductions of approximately 35.17% and 62.97%, respectively. The EPR had values of 1.14±0.48 and 1.22±0.51 for iDose⁴ and IMR, respectively. Conclusions: The iDose⁴ and IMR algorithms can effectively reduce noise levels while maintaining the anatomical structure. This study suggested automated evaluation measurements of noise levels and EPRs, which are important aspects in CT image quality with patients' cases of FBP, iDose⁴, and IMR. We expect that the inclusion of other important image quality indices with a greater number of patients' cases will enable the establishment of integrated platforms for monitoring both CT image quality and radiation dose.

• Korean Journal of Radiology
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• v.20 no.4
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• pp.599-608
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• 2019

Objective: To evaluate the effectiveness of computed tomography (CT) Hounsfield unit histogram analysis (HUHA) in postoperative pancreatic fistula (PF) prediction. Materials and Methods: Fifty-four patients (33 males and 21 females; mean age, 65.6 years; age range, 37-89 years) who had undergone preoperative CT and pancreaticoduodenectomy were included in this retrospective study. Two radiologists measured mean CT Hounsfield unit (CTHU) values by drawing regions of interest (ROIs) at the level of the pancreaticojejunostomy site on preoperative pre-contrast images. The HUHA values were arbitrarily divided into three categories, comprising HUHA-A ≤ 0 HU, 0 HU < HUHA-B < 30 HU, and HUHA-C ≥ 30 HU. Each HUHA value within the ROI was calculated as a percentage of the entire area using commercial 3-dimensional analysis software. Pancreas texture was evaluated as soft or hard by manual palpation. Results: Fifteen patients (27.8%) had clinically relevant PFs. The PF group had significantly higher HUHA-A (p < 0.01) and significantly lower mean CTHU (p < 0.01) values than those of the non-PF group. The HUHA-A value had a moderately strong correlation with PF occurrence (r = 0.60, p < 0.01), whereas the mean CTHU had a weak negative correlation with PF occurrence (r = -0.27, p < 0.01). The HUHA-A and mean CTHU areas under the curve (AUCs) for predicting PF occurrence were 0.86 and 0.65, respectively, with significant difference (p < 0.01). The HUHA-A and mean CTHU AUCs for predicting pancreatic softness were 0.86 and 0.64, respectively, with significant difference (p < 0.01). Conclusion: The HUHA-A values on preoperative pre-contrast CT images demonstrate a strong correlation with PF occurrence.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.191-197
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• 2014

Purpose : This study presents the usefulness assessment of metal artifact reduction for orthopedic implants(O-MAR) to decrease metal artifacts from materials with high density when acquired CT images. Materials and Methods : By CT simulator, original CT images were acquired from Gammex and Rando phantom and those phantoms inserted with high density materials were scanned for other CT images with metal artifacts and then O-MAR was applied to those images, respectively. To evaluate CT images using Gammex phantom, 5 regions of interest(ROIs) were placed at 5 organs and 3 ROIs were set up at points affected by artifacts. The averages of standard deviation(SD) and CT numbers were compared with a plan using original image. For assessment of variations in dose of tissue around materials with high density, the volume of a cylindrical shape was designed at 3 places in images acquired from Rando phantom by Eclipse. With 6 MV, 7-fields, $15{\time}15cm2$ and 100 cGy per fraction, treatment planning was created and the mean dose were compared with a plan using original image. Results : In the test with the Gammex phantom, CT numbers had a few difference at established points and especially 3 points affected by artifacts had most of the same figures. In the case of O-MAR image, the more reduction in SD appeared at all of 8 points than non O-MAR image. In the test using the Rando Phantom, the variations in dose of tissue around high density materials had a few difference between original CT image and CT image with O-MAR. Conclusion : The CT images using O-MAR were acquired clearly at the boundary of tissue around high density materials and applying O-MAR was useful for correcting CT numbers.

• Nuclear Medicine and Molecular Imaging
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• v.40 no.1
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• pp.40-47
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• 2006

Purpose: The aim of this study was to examine the effects of attenuation correction (AC) and scatter correction (SC) on the quantification of PET count rates. Materials and Methods: To assess the effects of AC and SC $^{18}F$-FDG PET images of phantom and cat brain were acquired using microPET R4 scanner. Thirty-minute transmission images using $^{68}Ge$ source and emission images after injection of FDG were acquired. PET images were reconstructed using 2D OSEM. AC and SC were applied. Regional count rates were measured using ROIs drawn on cerebral cortex including frontal, parietal, and latral temporal lobes and deep gray matter including head of caudate nucleus, putamen and thalamus for pre- and post-AC and SC images. The count rates were then normalized with the injected dose per body weight. To assess the effects of AC, count ratio of "deep gray matter/cerebral cortex" was calculated. To assess the effects of SC, ROIs were also drawn on the gray matter (GM) and white matter (WM), and contrast between them ((GM-WM)/GM was measured. Results: After the AC, count ratio of "deep gray matter/cerebral cortex" was increased by $17{\pm}7%$. After the SC, contrast was also increased by $12{\pm}3%$. Conclusion: Relative count of deep gray matter and contrast between gray and white matters were increased after AC and SC, suggesting that the AC would be critical for the quantitative analysis of cat brain PET data.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.37-43
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• 2013

Purpose: PET/CT scan using the SUV (Standardized Uptake Value) of radiopharmaceutical uptake in organs and tissues as an objective indicator makes it possible to analyze physiological and chemical reactions of human organs. This study analyzes the change of the SUV uptake in accordance with the way how PET/CT patients take a rest after the injection of $^{18}F-FDG$ (Fluororo-deoxyglucose). And also subjective satisfaction is assessed listening to music while taking a rest. Materials and Methods: From April 2011 until February 2013, Among the Primary cancer patients who admitted to the Catholic Medical Center (Seoul & Bucheon St. Mary's Hospital) and scanned $^{18}F-FDG$ PET/CT and also received care through the tracking test (mean age $55.61{\pm}12.41$ years, 108 people, 48 men and 60 women) were selected. The patients were divided into two groups. The first group (A: basal study) is requested to take a rest in bed quietly after the injection. However the second one (B: follow up study) is requested to listen to the music while taking a rest. And then SUV analysis was performed respectively. At the end of the scan, ROI (Region Of Interest) were set from the center of the liver (right lobe) and 3 spots of the brain (frontal, temporal, and occipital lobes). And the SUV was calculated. To identify the correlation among those ROIs, paired t-test was performed using SPSS software (Version 12.0K for windows, P>0.05). Also, after the PET/CT scan the satisfaction study was conducted of all the patients. 1:1 questionnaire survey was performed, and that questionnaire was made using the Likert 5-point scale. By utilizing those questionnaires, the analysis about simple frequency, percentage, average, and standard deviation was performed. Results: The SUV change of the 4 designated ROIs in accordance with listening to music was not statistically significant. (Frontal lobe P-value=0.611, Occipital lobe P-value=0.499, Temporal lobe P-value=0.717, Liver P-value=0.334: P-value>0.05) And the satisfaction study indicated that group B was appear to be 0.42 points (5 basis points) higher than group A. It showed that patients are more satisfied in group B than group A. Conclusion: when performing PET/CT scan using $^{18}F-FDG$, listening to music after the injection of the radiopharmaceuticals does not affect the SUV but given the state of the psychological comfort that may increase the patient's satisfaction.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.122-126
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• 2010

Purpose: F-18-FPCIT that shows strong familiarity with DAT located at a neural terminal site offers diagnostic information about DAT density state in the region of the striatum especially Parkinson's disease. In this study, we altered the iteration and subset and measured SUV${\pm}$SD and Contrasts from phantom images which set up to specific iteration and subset. So, we are going to suggest the appropriate range of the iteration and subset. Materials and Methods: This study has been performed with 10 normal volunteers who don't have any history of Parkinson's disease or cerebral disease and Flangeless Esser PET Phantom from Data Spectrum Corporation. $5.3{\pm}0.2$ mCi of F-18-FPCIT was injected to the normal group and PET Phantom was assembled by ACR PET Phantom Instructions and it's actual ratio between hot spheres and background was 2.35 to 1. Brain and Phantom images were acquired after 3 hours from the time of the injection and images were acquired for ten minutes. Basically, SIEMENS Bio graph 40 True-point was used and True-X method was applied for image reconstruction method. The iteration and Subset were set to 2 iterations, 8 subsets, 3 iterations, 16 subsets, 6 iterations, 16 subsets, 8 iterations, 16 subsets and 8 iterations, 21 subsets respectively. To measure SUVs on the brain images, ROIs were drawn on the right Putamen. Also, Coefficient of variance (CV) was calculated to indicate the uniformity at each iteration and subset combinations. On the phantom study, we measured the actual ratio between hot spheres and back ground at each combinations. Same size's ROIs were drawn on the same slide and location. Results: Mean SUVs were 10.60, 12.83, 13.87, 13.98 and 13.5 at each combination. The range of fluctuation by sets were 22.36%, 10.34%, 1.1%, and 4.8% respectively. The range of fluctuation of mean SUV was lowest between 6 iterations 16 subsets and 8 iterations 16 subsets. CV showed 9.07%, 11.46%, 13.56%, 14.91% and 19.47% respectively. This means that the numerical value of the iteration and subset gets higher the image's uniformity gets worse. The range of fluctuation of CV by sets were 2.39, 2.1, 1.35, and 4.56. The range of fluctuation of uniformity was lowest between 6 iterations, 16 subsets and 8 iterations, 16 subsets. In the contrast test, it showed 1.92:1, 2.12:1, 2.10:1, 2.13:1 and 2.11:1 at each iteration and subset combinations. A Setting of 8 iterations and 16 subsets reappeared most close ratio between hot spheres and background. Conclusion: Findings on this study, SUVs and uniformity might be calculated differently caused by variable reconstruction parameters like filter or FWHM. Mean SUV and uniformity showed the lowest range of fluctuation at 6 iterations 16 subsets and 8 iterations 16 subsets. Also, 8 iterations 16 subsets showed the nearest hot sphere to background ratio compared with others. But it can not be concluded that only 6 iterations 16 subsets and 8 iterations 16 subsets can make right images for the clinical diagnosis. There might be more factors that can make better images. For more exact clinical diagnosis through the quantitative analysis of DAT density in the region of striatum we need to secure healthy people's quantitative values.

• Tuberculosis and Respiratory Diseases
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• v.66 no.1
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• pp.20-26
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• 2009

Background: $^{18}F$-Fluorodeoxyglucose positron emission tomography (FDG-PET) is widely used for the diagnosis and staging of non-small cell lung cancer (NSCLC). The aim of this study is to determine whether the bone marrow hypermetabolism seen on FDG-PET predicts a response to chemotherapy in patients with NSCLC. Methods: We evaluated the patients with advanced NSCLC and who were treated with combination chemotherapy. For determination of the standardized uptake value (SUV) of the bone marrow (BM SUV) on FDG-PET, regions of interest (ROIs) were manually drawn over the lumbar vertebrae (L1, 2, 3). ROIs were also drawn on a homogenous transaxial slice of the liver to obtain the bone marrow/ liver SUV ratio (BM/L SUV ratio). The response to chemotherapy was evaluated according to the Response Evaluation Criteria in Solid Tumor (RECIST) criteria after three cycles of chemotherapy. Results: Fifty-nine NSCLC patients were included in the study. Multivariate analysis was performed using a logistic regression model. The BM SUV and the BM/L SUV ratio on FDG-PET were not associated with a response to chemotherapy in NSCLC patients (p=0.142 and 0.978, respectively). Conclusion: The bone marrow hypermetabolism seen on FDG-PET can not predict a response to chemotherapy in NSCLC patients.