• Journal of the Korean Society of Radiology
• /
• v.16 no.5
• /
• pp.537-543
• /
• 2022

The purpose of this simulation study was to evaluate the possibility of pancreas detection through effective atomic number information using dual-energy computed tomography(CT). The effective atomic number of 10 tissue-equivalent materials were estimated through stoichiometric calibration. For stoichiometric calibration, HU values at low-energy (80 kV) and high-energy (140 kV) for 10 tissue-equivalent materials were used. Based on this method, the effective atomic number image of the tissue-equivalent material was extracted through an iterative algorithm. According to the results, the attenuation ratio in accordance with the effective atomic number was estimated to have an R² value of 0.9999, and the effective atomic number of Pancreas, Water, Liver, Blood, Spongiosa, and Cortical bone was overall within 1% accuracy compared to the theoretical value. Conventional pancreatic cancer examination uses a contrast medium, so there is a possibility of potential side effects of the contrast medium. In order to solve this problem, it is thought that it will be possible to contribute to an accurate and safe examination by extracting the effective atomic number using dual-energy CT without contrast enhancement. Based on this study, future research will be conducted on the detection of pancreatic cancer using the HU value of pancreatic cancer based on clinical images.

• Journal of Veterinary Clinics
• /
• v.32 no.2
• /
• pp.200-204
• /
• 2015

A 12-year-old neutered male Shih Tzu presented with constipation and dyschezia. Abdominal radiographs showed distension of the descending colon and dorsal compression of the colon by a soft tissue mass. The mass was well-marginated with homogeneous soft tissue attenuation and showed no evidence of metastasis on computed tomography (CT). The dynamic CT showed a consistently mild contrast enhancement. The perfusion and capillary permeability were lower than those of the gluteal muscle. The tentative imaging diagnosis was a benign intrapelvic tumor, which rarely shows angiogenesis. The mass was excised, and a leiomyoma was confirmed by histopathologic examination.

• Korean Journal of Radiology
• /
• v.22 no.12
• /
• pp.1996-2005
• /
• 2021

Objective: To investigate the multidetector computed tomography (MDCT) features of fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) with germline or somatic mutations, and compare them with those of papillary type II RCC (pRCC type II). Materials and Methods: A total of 24 patients (mean ± standard deviation, 40.4 ± 14.7 years) with pathologically confirmed FH-deficient RCC (15 with germline and 9 with somatic mutations) and 54 patients (58.6 ± 12.6 years) with pRCC type II were enrolled. The MDCT features were retrospectively reviewed and compared between the two entities and mutation subgroups, and were correlated with the clinicopathological findings. Results: All the lesions were unilateral and single. Compared with pRCC type II, FH-deficient RCC was more prevalent among younger patients (40.4 ± 14.7 vs. 58.6 ± 12.6, p < 0.001) and tended to be larger (8.1 ± 4.1 vs. 5.4 ± 3.2, p = 0.002). Cystic solid patterns were more common in FH-deficient RCC (20/24 vs. 16/54, p < 0.001), with 16 of the 20 (80.0%) cystic solid tumors having showed typical polycystic and thin smooth walls and/or septa, with an eccentric solid component. Lymph node (16/24 vs. 16/54, p = 0.003) and distant (11/24 vs. 3/54, p < 0.001) metastases were more frequent in FH-deficient RCC. FH-deficient RCC and pRCC type II showed similar attenuation in the unenhanced phase. The attenuation in the corticomedullary phase (CMP) (76.3% ± 25.0% vs. 60.2 ± 23.6, p = 0.008) and nephrographic phase (NP) (87.7 ± 20.5, vs. 71.2 ± 23.9, p = 0.004), absolute enhancement in CMP (39.0 ± 24.8 vs. 27.1 ± 22.7, p = 0.001) and NP (50.5 ± 20.5 vs. 38.2 ± 21.9, p = 0.001), and relative enhancement ratio to the renal cortex in CMP (0.35 ± 0.26 vs. 0.24 ± 0.19, p = 0.001) and NP (0.43 ± 0.24 vs. 0.29 ± 0.19, p < 0.001) were significantly higher in FH-deficient RCC. No significant difference was found between the FH germline and somatic mutation subgroups in any of the parameters. Conclusion: The MDCT features of FH-deficient RCC were different from those of pRCC type II, whereas there was no statistical difference between the germline and somatic mutation subgroups. A kidney mass with a cystic solid pattern and metastatic tendency, especially in young patients, should be considered for FH-deficient RCC.

• Journal of the Korean Society of Radiology
• /
• v.82 no.2
• /
• pp.393-405
• /
• 2021

Purpose To determine the multidetector CT (MDCT) findings that differentiate adenomyoma of the ampulla of Vater (AOV) from localized adenocarcinoma of the AOV. Materials and Methods Sixteen and 30 patients with adenomyoma and localized adenocarcinoma of the AOV, respectively, were evaluated using MDCT. We analyzed the size and attenuation value and presence of uniform enhancement of the lesions, diameters of the extrahepatic bile duct (EHD) and main pancreatic duct, presence of regional lymph node enlargement, and laboratory findings. We determined the independent findings for differentiating adenomyoma from localized adenocarcinoma of the AOV using multivariate analysis. Results The size of the lesion and diameter of the EHD were significantly smaller for adenomyoma than those for localized adenocarcinoma of the AOV (all p < 0.001). In multivariate analyses, a lesion size of ≤ 1.3 cm, an EHD diameter of ≤ 1.3 cm, and an alanine transaminase level of ≤ 31 IU/L significantly differentiated adenomyoma from localized adenocarcinoma of the AOV. When all of these three findings were met, the specificity for adenomyoma of the AOV was 93.3%. Conclusion MDCT imaging may facilitate the differential diagnosis of adenomyoma and localized adenocarcinoma of the AOV based on the size of the lesion and diameter of the EHD.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.14 no.1
• /
• pp.111-114
• /
• 2010

Purpose: There has been recent interest in the radioactivity uptake and image acquisition of radioactivity concentration. The degree of uptake is strongly affected by many factors containing $^{18}F$-FDG injection volume, tumor size and the density of blood glucose. Therefore, we investigated how radioactivity uptake in target influences 2D or 3D image analysis and elucidate radioactivity concentration that mediate this effect. This study will show the relationship between the radioactivity uptake and 2D,3D image acquisition on radioactivity concentration. Materials and Methods: We got image with 2D and 3D using 1994 NEMA PET phantom and GE Discovery(GE, U.S.A) STe 16 PET/CT setting the ratio of background and hot sphere's radioactivity concentration as being a standard of 1:2, 1:4, 1:8, 1:10, 1:20, and 1:30 respectively. And we set 10 minutes for CT attenuation correction and acquisition time. For the reconstruction method, we applied iteration method with twice of the iterative and twenty times subset to both 2D and 3D respectively. For analyzing the images, We set the same ROI at the center of hot sphere and the background radioactivity. We measured the radioactivity count of each part of hot sphere and background, and it was comparative analyzed. Results: The ratio of hot sphere's radioactivity density and the background radioactivity with setting ROI was 1:1.93, 1:3.86, 1:7.79, 1:8.04, 1:18.72, and 1:26.90 in 2D, and 1:1.95, 1:3.71, 1:7.10, 1:7.49, 1:15.10, and 1:23.24 in 3D. The differences of percentage were 3.50%, 3.47%, 8.12%, 8.02%, 10.58%, and 11.06% in 2D, the minimum differentiation was 3.47%, and the maximum one was 11.06%. In 3D, the difference of percentage was 3.66%, 4.80%, 8.38%, 23.92%, 23.86%, and 22.69%. Conclusion: The difference of accumulated concentrations is significantly increased following enhancement of radioactivity concentration. The change of radioactivity density in 2D image is affected by less than 3D. For those reasons, when patient is examined as follow up scan with changing the acquisition mode, scan should be conducted considering those things may affect to the quantitative analysis result and take into account these differences at reading.

• Korean Journal of Radiology
• /
• v.22 no.1
• /
• pp.131-138
• /
• 2021

Objective: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). Materials and Methods: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. Results: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). Conclusion: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.