• Korean Journal of Radiology
• /
• v.24 no.11
• /
• pp.1102-1113
• /
• 2023

Objective: To elucidate the use of radiological studies, including nuclear medicine, and biopsy for the diagnosis and staging of prostate cancer (PCA) in clinical practice and understand the current status of PCA in Asian countries via an international survey. Materials and Methods: The Asian Prostate Imaging Working Group designed a survey questionnaire with four domains focused on prostate magnetic resonance imaging (MRI), other prostate imaging, prostate biopsy, and PCA backgrounds. The questionnaire was sent to 111 members of professional affiliations in Korea, Japan, Singapore, and Taiwan who were representatives of their working hospitals, and their responses were analyzed. Results: This survey had a response rate of 97.3% (108/111). The rates of using 3T scanners, antispasmodic agents, laxative drugs, and prostate imaging-reporting and data system reporting for prostate MRI were 21.6%-78.9%, 22.2%-84.2%, 2.3%-26.3%, and 59.5%-100%, respectively. Respondents reported using the highest b-values of 800-2000 sec/mm² and fields of view of 9-30 cm. The prostate MRI examinations per month ranged from 1 to 600, and they were most commonly indicated for biopsy-naïve patients suspected of PCA in Japan and Singapore and staging of proven PCA in Korea and Taiwan. The most commonly used radiotracers for prostate positron emission tomography are prostate-specific membrane antigen in Singapore and fluorodeoxyglucose in three other countries. The most common timing for prostate MRI was before biopsy (29.9%). Prostate-targeted biopsies were performed in 63.8% of hospitals, usually by MRI-ultrasound fusion approach. The most common presentation was localized PCA in all four countries, and it was usually treated with radical prostatectomy. Conclusion: This survey showed the diverse technical details and the availability of imaging and biopsy in the evaluation of PCA. This suggests the need for an educational program for Asian radiologists to promote standardized evidence-based imaging approaches for the diagnosis and staging of PCA.

• Korean Journal of Radiology
• /
• v.23 no.4
• /
• pp.389-401
• /
• 2022

Objective: This study aimed to determine a factor for predicting suboptimal image quality of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in patients with extrahepatic bile duct (EHD) cancer before MRI examination. Materials and Methods: We retrospectively evaluated 259 patients (mean age ± standard deviation: 68.0 ± 8.3 years; 162 male and 97 female) with EHD cancer who underwent gadoxetic acid-enhanced MRI between 2011 and 2017. Patients were divided into a primary analysis set (n = 184) and a validation set (n = 75) based on the diagnosis date of January 2014. Two reviewers assigned the functional liver imaging score (FLIS) to reflect the HBP image quality. The FLIS consists of the sum of three HBP features, each scored on a 0-2 scale: liver parenchymal enhancement, biliary excretion, and signal intensity of the portal vein. Patients were classified into low-FLIS (0-3) or high-FLIS (4-6) groups. Multivariable analysis was performed to determine a predictor of low FLIS using serum biochemical and imaging parameters of cholestasis severity. The optimal cutoff value for predicting low FLIS was obtained using receiver operating characteristic analysis, and validation was performed. Results: Of the 259 patients, 140 (54.0%) and 119 (46.0%) were classified into the low-FLIS and high-FLIS groups, respectively. In the primary analysis set, total bilirubin was an independent factor associated with low FLIS (adjusted odds ratio per 1-mg/dL increase, 1.62; 95% confidence interval [CI], 1.32-1.98). The optimal cutoff value of total bilirubin for predicting low FLIS was 2.1 mg/dL with a sensitivity of 95.1% (95% CI: 88.9-98.4) and a specificity of 89.0% (95% CI: 80.2-94.9). In the validation set, the total bilirubin cutoff showed a sensitivity of 92.1% (95% CI: 78.6-98.3) and a specificity of 83.8% (95% CI: 68.0-93.8). Conclusion: Serum total bilirubin before acquisition of gadoxetic acid-enhanced MRI may help predict suboptimal HBP image quality in patients with EHD cancer.

• Korean Journal of Radiology
• /
• v.23 no.4
• /
• pp.446-454
• /
• 2022

Objective: To evaluate whether hyperoxia-induced ΔR₁ (hyperO₂ΔR₁) can accurately identify histological infarction in an acute cerebral stroke model. Materials and Methods: In 18 rats, MRI parameters, including hyperO₂ΔR₁, apparent diffusion coefficient (ADC), cerebral blood flow and volume, and ¹⁸F-fluorodeoxyglucose uptake on PET were measured 2.5, 4.5, and 6.5 hours after a 60-minutes occlusion of the right middle cerebral artery. Histological examination of the brain was performed immediately following the imaging studies. MRI and PET images were co-registered with digitized histological images. The ipsilateral hemisphere was divided into histological infarct (histological cell death), non-infarct ischemic (no cell death but ADC decrease), and nonischemic (no cell death or ADC decrease) areas for comparisons of imaging parameters. The levels of hyperO₂ΔR₁ and ADC were measured voxel-wise from the infarct core to the non-ischemic region. The correlation between areas of hyperO₂ΔR₁-derived infarction and histological cell death was evaluated. Results: HyperO₂ΔR₁ increased only in the infarct area (p ≤ 0.046) compared to the other areas. ADC decreased stepwise from non-ischemic to infarct areas (p = 0.002 at all time points). The other parameters did not show consistent differences among the three areas across the three time points. HyperO₂ΔR₁ sharply declined from the core to the border of the infarct areas, whereas there was no change within the non-infarct areas. A hyperO₂ΔR₁ value of 0.04 s^-1 was considered the criterion to identify histological infarction. ADC increased gradually from the infarct core to the periphery, without a pronounced difference at the border between the infarct and non-infarct areas. Areas of hyperO₂ΔR₁ higher than 0.04 s^-1 on MRI were strongly positively correlated with histological cell death (r = 0.862; p < 0.001). Conclusion: HyperO₂ΔR₁ may be used as an accurate and early (2.5 hours after onset) indicator of histological infarction in acute stroke.

• Korean Journal of Radiology
• /
• v.23 no.12
• /
• pp.1281-1289
• /
• 2022

Objective: Radiomic modeling using multiple regions of interest in MRI of the brain to diagnose juvenile myoclonic epilepsy (JME) has not yet been investigated. This study aimed to develop and validate radiomics prediction models to distinguish patients with JME from healthy controls (HCs), and to evaluate the feasibility of a radiomics approach using MRI for diagnosing JME. Materials and Methods: A total of 97 JME patients (25.6 ± 8.5 years; female, 45.5%) and 32 HCs (28.9 ± 11.4 years; female, 50.0%) were randomly split (7:3 ratio) into a training (n = 90) and a test set (n = 39) group. Radiomic features were extracted from 22 regions of interest in the brain using the T1-weighted MRI based on clinical evidence. Predictive models were trained using seven modeling methods, including a light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, with radiomics features in the training set. The performance of the models was validated and compared to the test set. The model with the highest area under the receiver operating curve (AUROC) was chosen, and important features in the model were identified. Results: The seven tested radiomics models, including light gradient boosting machine, support vector classifier, random forest, logistic regression, extreme gradient boosting, gradient boosting machine, and decision tree, showed AUROC values of 0.817, 0.807, 0.783, 0.779, 0.767, 0.762, and 0.672, respectively. The light gradient boosting machine with the highest AUROC, albeit without statistically significant differences from the other models in pairwise comparisons, had accuracy, precision, recall, and F1 scores of 0.795, 0.818, 0.931, and 0.871, respectively. Radiomic features, including the putamen and ventral diencephalon, were ranked as the most important for suggesting JME. Conclusion: Radiomic models using MRI were able to differentiate JME from HCs.

• Korean Journal of Radiology
• /
• v.22 no.11
• /
• pp.1886-1893
• /
• 2021

Objective: To assess the feasibility of quantitatively assessing pancreatic steatosis using magnetic resonance imaging (MRI) and its correlation with obesity and metabolic risk factors in pediatric patients. Materials and Methods: Pediatric patients (≤ 18 years) who underwent liver fat quantification MRI between January 2016 and June 2019 were retrospectively included and divided into the obesity and control groups. Pancreatic proton density fat fraction (P-PDFF) was measured as the average value for three circular regions of interest (ROIs) drawn in the pancreatic head, body, and tail. Age, weight, laboratory results, and mean liver MRI values including liver PDFF (L-PDFF), stiffness on MR elastography, and T2^* values were assessed for their correlation with P-PDFF using linear regression analysis. The associations between P-PDFF and metabolic risk factors, including obesity, hypertension, diabetes mellitus (DM), and dyslipidemia, were assessed using logistic regression analysis. Results: A total of 172 patients (male:female = 125:47; mean ± standard deviation [SD], 13.2 ± 3.1 years) were included. The mean P-PDFF was significantly higher in the obesity group than in the control group (mean ± SD, 4.2 ± 2.5% vs. 3.4 ± 2.4%; p = 0.037). L-PDFF and liver stiffness values showed no significant correlation with P-PDFF (p = 0.235 and p = 0.567, respectively). P-PDFF was significantly associated with obesity (odds ratio 1.146, 95% confidence interval 1.006-1.307, p = 0.041), but there was no significant association with hypertension, DM, and dyslipidemia. Conclusion: MRI can be used to quantitatively measure pancreatic steatosis in children. P-PDFF is significantly associated with obesity in pediatric patients.

• Korean Journal of Radiology
• /
• v.22 no.2
• /
• pp.233-242
• /
• 2021

Objective: To evaluate the association of MRI features with the major genomic profiles and prognosis of World Health Organization grade III (G3) gliomas compared with those of glioblastomas (GBMs). Materials and Methods: We enrolled 76 G3 glioma and 155 GBM patients with pathologically confirmed disease who had pretreatment brain MRI and major genetic information of tumors. Qualitative and quantitative imaging features, including volumetrics and histogram parameters, such as normalized cerebral blood volume (nCBV), cerebral blood flow (nCBF), and apparent diffusion coefficient (nADC) were evaluated. The G3 gliomas were divided into three groups for the analysis: with this isocitrate dehydrogenase (IDH)-mutation, IDH mutation and a chromosome arm 1p/19q-codeleted (IDHmut1p/19qdel), IDH mutation, 1p/19q-nondeleted (IDHmut1p/19qnondel), and IDH wildtype (IDHwt). A prediction model for the genetic profiles of G3 gliomas was developed and validated on a separate cohort. Both the quantitative and qualitative imaging parameters and progression-free survival (PFS) of G3 gliomas were compared and survival analysis was performed. Moreover, the imaging parameters and PFS between IDHwt G3 gliomas and GBMs were compared. Results: IDHmut G3 gliomas showed a larger volume (p = 0.017), lower nCBF (p = 0.048), and higher nADC (p = 0.007) than IDHwt. Between the IDHmut tumors, IDHmut1p/19qdel G3 gliomas had higher nCBV (p = 0.024) and lower nADC (p = 0.002) than IDHmut1p/19qnondel G3 gliomas. Moreover, IDHmut1p/19qdel tumors had the best prognosis and IDHwt tumors had the worst prognosis among G3 gliomas (p < 0.001). PFS was significantly associated with the 95th percentile values of nCBV and nCBF in G3 gliomas. There was no significant difference in neither PFS nor imaging features between IDHwt G3 gliomas and IDHwt GBMs. Conclusion: We found significant differences in MRI features, including volumetrics, CBV, and ADC, in G3 gliomas, according to IDH mutation and 1p/19q codeletion status, which can be utilized for the prediction of genomic profiles and the prognosis of G3 glioma patients. The MRI signatures and prognosis of IDHwt G3 gliomas tend to follow those of IDHwt GBMs.

• Investigative Magnetic Resonance Imaging
• /
• v.2 no.1
• /
• pp.67-72
• /
• 1998

The mapping of the spin-spin relaxation time T2 in pixel-by-pixel was suggested as a quantitative diagnostic tool in medicine. although the CPMG pulse sequence has been known to be the best pulse sequence for T2 measurement in physics NMR, the supplied pulse sequence by the manufacture of MRI system was able to obtain the maximum of 4 CPMG images. Eight or more images with different echo time TEs are required to construct a reliable T2 map, so that two or more acquisitions were required, which easily took more than 10 minutes. 4-echo CPMG imaging pulse sequence was modified to generate the maximum of 8 MR images with evenly spaced echo time TEs. In human MR imaging, since patients tend to move at least several pixels between the different acquisitions, 8-echo CPMG imaging sequence reduces the acquisition time and may remove any mis-regitration of each pixels signal for the fitting of T2. The resultant T2 maps using the theoretically simulated images and using the MR images of the human brain suggested that 8 echo CPMG sequence with short echo spacing such as 17-20 msec can give the reliable T2 map.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.46 no.12
• /
• pp.102-107
• /
• 2009

As technology gets developed, medical equipments are also modernized and leading-edge systems, such as PACS become popular. Many scientists noticed importance of medical image processing technology. Technique of region segmentation is the first step of digital medical image processing. Segmentation technique helps doctors to find out abnormal symptoms early, such as tumors, edema, and necrotic tissue, and helps to diagnoses correctly. Segmentation of white matter, gray matter and CSF of a brain image is very crucial part. However, the segmentation is not easy due to ambiguous boundaries and inhomogeneous physical characteristics. The rate of incorrect segmentation is high because of these difficulties. Fuzzy-based segmentation algorithms are robust to even ambiguous boundaries. In this paper a modified Fuzzy-based segmentation algorithm is proposed to handle the noise of MR scanners. A proposed algorithm requires minimal computations of mean and variance of neighbor pixels to adjust a new neighbor list. With the addition of minimal compuation, the modified FCM(mFCM) lowers the rate of incorrect clustering below 30% approximately compared the traditional FCM.

• Journal of Biomedical Engineering Research
• /
• v.25 no.1
• /
• pp.33-41
• /
• 2004

Accuracy of registration between images acquired from various medical image modalities is one of the critical issues in radiation treatment planing. In this study, a method of accuracy evaluation of image registration using a homemade brain phantom was investigated. Chamfer matching of CT-MR and CT-SPECT imaging was applied for the multimodal image registration. The accuracy of image correlation was evaluated by comparing the center points of the inserted targets of the phantom. The three dimensional root-mean-square translation deviations of the CT-MR and CT-SPECT registration were 2.1${\pm}$0.8 mm and 2.8${\pm}$1.4 mm, respectively. The rotational errors were ＜ 2$^{\circ}$ for the three orthogonal axes. These errors were within a reasonable margin compared with the previous phantom studies. A visual inspection of the superimposed CT-MR and CT- SPECT images also showed good matching results.

• The Transactions of the Korea Information Processing Society
• /
• v.7 no.2
• /
• pp.542-551
• /
• 2000

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.