• Korean Journal of Radiology
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• v.22 no.4
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• pp.547-558
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• 2021

The perihepatic space is frequently involved in a spectrum of diseases, including intrahepatic lesions extending to the liver capsule and disease conditions involving adjacent organs extending to the perihepatic space or spreading thanks to the communication from intraperitoneal or extraperitoneal sites through the hepatic ligaments. Lesions resulting from the dissemination of peritoneal processes may also affect the perihepatic space. Here we discuss how to assess the perihepatic origin of a lesion and describe the magnetic resonance imaging (MRI) features of normal structures and fluids that may be abnormally located in the perihepatic space. We then review and illustrate the MRI findings present in cases of perihepatic infectious, tumor-related, and miscellaneous conditions. Finally, we highlight the value of MRI over computed tomography.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2206-2212
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• 2013

We propose a method for the classification of fatty liver by ultrasound imaging using Fuzzy Contrast Enhancement Technique and FCM. ROI images are extracted after removal of information data except ultrasound image of the liver and the kidney then image contrast is improved by Fuzzy Contrast Enhancement Algorithm. The images applied Fuzzy Contrast Enhancement Technique is applied average binarization then ROI images of liver and kidney parenchyma are extracted using Blob algorithm. Representative brightness is extracted in the liver and kidney images using the most frequent brightness level after classification of 10 brightness levels. We applied this method to ultrasound images and a radiologist confirmed the accuracy of diagnosis for fatty liver. This method would be a model for automatic method in the diagnosis of fatty liver.

• Radiation Oncology Journal
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• v.34 no.1
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• pp.64-75
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• 2016

Purpose: In order to evaluate the relationship between the dose to the liver parenchyma and focal liver reaction (FLR) after stereotactic ablative body radiotherapy (SABR), we suggest a novel method using a three-dimensional dose distribution and change in signal intensity of gadoxetate disodium-gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) hepatobiliary phase images. Materials and Methods: In our method, change of the signal intensity between the pretreatment and follow-up hepatobiliary phase images of Gd-EOB-DTPA-enhanced MRI was calculated and then threshold dose (TD) for developing FLR was obtained from correlation of dose with the change of the signal intensity. For validation of the method, TDs for six patients, who had been treated for liver cancer with SABR with 45-60 Gy in 3 fractions, were calculated using the method, and we evaluated concordance between volume enclosed by isodose of TD by the method and volume identified as FLR by a physician. Results: The dose to normal liver was correlated with change in signal intensity between pretreatment and follow-up MRI with a median $R^2$ of 0.935 (range, 0.748 to 0.985). The median TD by the method was 23.5 Gy (range, 18.3 to 39.4 Gy). The median value of concordance was 84.5% (range, 44.7% to 95.9%). Conclusion: Our method is capable of providing a quantitative evaluation of the relationship between dose and intensity changes on follow-up MRI, as well as determining individual TD for developing FLR. We expect our method to provide better information about the individual relationship between dose and FLR in radiotherapy for liver cancer.

• Journal of the Korean Society of Radiology
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• v.82 no.4
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• pp.851-861
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• 2021

Liver injury is a common consequence of blunt abdominopelvic trauma. Contrast-enhanced CT allows for the rapid detection and evaluation of liver injury. The treatment strategy for blunt liver injury has shifted from surgical to nonoperative management, which has been widely complemented by interventional management to treat both liver injury and its complications. In this article, we review the major imaging features of liver injury and the role of interventional management for the treatment of liver injury.

• Investigative Magnetic Resonance Imaging
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• v.16 no.1
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• pp.6-15
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• 2012

Purpose : The objective of this study was to develop background gradient correction method using excitation pulse profile compensation for accurate fat and $T_2{^*}$ quantification in the liver. Materials and Methods: In liver imaging using gradient echo, signal decay induced by linear background gradient is weighted by an excitation pulse profile and therefore hinders accurate quantification of $T_2{^*}$and fat. To correct this, a linear background gradient in the slice-selection direction was estimated from a $B_0$ field map and signal decays were corrected using the excitation pulse profile. Improved estimation of fat fraction and $T_2{^*}$ from the corrected data were demonstrated by phantom and in vivo experiments at 3 Tesla magnetic field. Results: After correction, in the phantom experiments, the estimated $T_2{^*}$ and fat fractions were changed close to that of a well-shimmed condition while, for in vivo experiments, the background gradients were estimated to be up to approximately 120 ${\mu}T/m$ with increased homogeneity in $T_2{^*}$ and fat fractions obtained. Conclusion: The background gradient correction method using excitation pulse profile can reduce the effect of macroscopic field inhomogeneity in signal decay and can be applied for simultaneous fat and iron quantification in 2D gradient echo liver imaging.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.209-213
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• 2023

In recent years, the image processing mechanisms are used widely in several medical areas for improving earlier detection and treatment stages, in which the time factor is very important to discover the disease in the patient as possible as fast, especially in various cancer tumors such as the liver cancer. Liver cancer has been attracting the attention of medical and sciatic communities in the latest years because of its high prevalence allied with the difficult treatment. Statistics indicate that liver cancer, throughout world, is the one that attacks the greatest number of people. Over the time, study of MR images related to cancer detection in the liver or abdominal area has been difficult. Early detection of liver cancer is very important for successful treatment. There are few methods available to detect cancerous cells. In this paper, an automatic approach that integrates the intensity-based segmentation and k-means clustering approach for detection of cancer region in MRI scan images of liver.

• Korean Journal of Radiology
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• v.24 no.1
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• pp.10-14
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• 2023

• Korean Journal of Veterinary Research
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• v.57 no.2
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• pp.127-129
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• 2017

Intercostal abdominal hernia in the 11th intercostal space was identified in a leopard cat. Although mild leukopenia was found in laboratory examinations, no remarkable abnormality was revealed in medical imaging. To investigate abdominal organs, diagnostic laparoscopy was performed after hernia repair. In laparoscopic view, closure of the herniation site and a lesion with whitish discoloration in the liver (left medial lobe) were observed. Subsequently, laparoscopic liver biopsy was performed against the affected hepatic tissue. Histologically, the sample was diagnosed as mild hepatic lipidosis. Laparoscopy is considered useful for abdominal visceral examination and liver biopsy in a leopard cat patient.

• Investigative Magnetic Resonance Imaging
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• v.16 no.2
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• pp.173-176
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• 2012

We report the radiological findings of regional enhancement of the liver adjacent to the right adrenal pheochromocytoma. CT and MRI showed focal strong enhancement of adjacent liver tissue in the arterial phase. However during the delayed phase, the lesion showed iso-attenuation with normal hepatic parenchyma and not delineated. The lesion did not show abnormal signal intensity on neither T1 nor T2 weighted images and indistinguishable from normal parenchyma. The enhancing hepatic lesion was spontaneously regressed on postoperative follow up CT which was taken several months after the adrenalectomy.

• Smart Media Journal
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• v.9 no.3
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.