extraction of 3d region of the liver for hepatic fibrosis diagnosis

간 경직도 분포의 정량적 분석

Hepatic fibrosis refers to the formation of excessive scar tissue in the liver, usually as a result of liver damage or disease1,2,3,4. It frequently arises as a consequence of chronic liver injury and is commonly associated with liver diseases, such as chronic viral hepatitis, non-alcoholic fatty liver disease, and alcoholic liver disease. If left untreated, hepatic fibrosis can progress to cirrhosis, a potentially life-threatening condition associated with significant morbidity and mortality.
Active research in this area aims to elucidate the cellular and molecular mechanisms underlying the pathogenesis of hepatic fibrosis, as well as to develop novel diagnostic and therapeutic strategies to improve patient outcomes. Another objective is the noninvasive detection of the hepatic fibrosis stage, which is a critical aspect that directly correlates with disease diagnosis, treatment selection, and prognosis evaluation. Despite the importance of accurate diagnosis and the monitoring of hepatic fibrosis, traditional diagnostic methods, such as liver biopsy, are invasive and associated with significant risks. In contrast, magnetic resonance elastography5,6 (MRE) is a promising non-invasive imaging technique that has demonstrated potential in the diagnosis and monitoring of hepatic fibrosis by quantifying liver stiffness.
In recent years, there has been significant research focused on evaluating the accuracy and reliability of MRE in the diagnosis of hepatic fibrosis, as well as its potential advantages over traditional diagnostic methods. The liver stiffness metric of MRE has been granted approval by the United States Food and Drug Administration (FDA) for clinical diagnosis, and extensive comparative analysis with pathological results has been conducted in clinical practice. The results have shown that the stiffness maps generated by MRE exhibit a strong positive correlation with various stages of liver fibrosis7,8,9,10,11,12. Yet so far, the work of accurately evaluating and tracking the progression of liver fibrosis in patients through quantitative analysis of liver stiffness distribution (LSD) by matching liver structure images with MRE has not made much progress.
In this study, the medical imaging group analysis technique13,14,15 is introduced to achieve accurate alignment of the liver structure images with the stiffness map generated by MRE in 3D space, enabling the calculation of liver stiffness values for each voxel of the entire liver. Based on the 3D-digital model of LSD, the exact distribution of patient-specific liver fibrosis staging can be calculated and evaluated. This lays a solid foundation for the precise quantitative diagnosis of early-stage liver fibrosis.

저자 스포트라이트: 자기 공명 엘라스토그래피 및 AI를 사용한 간 섬유증 진단 발전 

자기공명탄성검사(Magnetic Resonance Elastography)를 기반으로 한 간섬유증 조기 진단을 위한 3차원 디지털 모델

The scope of the research is to develop a three dimensional digital model for the early diagnosis of hepatic fibrosis based on magnetic resonance elastography. The research aims to address the limitations of current methods and provide a noninvasive and quantitative approach for the accurate assessment and monitoring of hepatic fibrosis. Some of the current experimental challenges in the field of hepatic fibrosis research include improving the scanning accuracy and imaging techniques of non-invasive methods like magnetic resonance elastography, standardizing computational protocols for consistent results and accumulating more comprehensive, and that was data size for accurate classification and diagnosis.The liver MRI structural images were accurately aligned with MRE images, enabling the three dimensional distribution of liver stiffness, distribution, and achieving precise quantification in 3D space. It is possible to accurately establish a liver's stiffness distribution map for a healthy liver, and conveniently utilize it for the diagnosis and the treatment of patients with different stages of liver cirrhosis. In the future, we want to address AI-driven diagnosis, treatment, and prognosis of liver diseases, as well as the development of herbal medicines related to liver diseases.

Watch this Scientific Journal Video about Extraction of 3D Region of the Liver for Hepatic Fibrosis Diagnosis at JoVE.com

간 섬유증 진단을 위한 간의 3D 영역 추출

시작하려면 MATLAB을 여십시오. IDEAL 데이터의 가로 해상도가 256 x 256픽셀이고 픽셀 간격이 1.5625mm이고 슬라이스 두께가 10mm인지 확인합니다. 모든 DICOM 데이터를 사용자 지정 작업 디렉터리에 복사하고, MATLAB의 작업 디렉터리에 있는 데이터가 포함된 디렉터리로 이동합니다.그런 다음 설명 이름 함수를 실행하여 각 시퀀스의 폴더에 설명이 포함된 이름을 추가합니다. 각 이미지 시퀀스 폴더에 설명 이름을 추가합니다. IDEAL의 이미지를 확인하려면 다른 위상 폴더의 디렉토리를 변경하십시오.슬라이스 보기 기능을 실행하여 각 단계에 대한 충격 시퀀스를 확인합니다. 그런 다음 GUI 하단의 스크롤 막대를 사용하여 다양한 시퀀스를 빠르게 찾아봅니다. 간 조직 경계를 묘사하기 위해 MRI IDEAL outphase sequence를 선택합니다.Mimics 소프트웨어를 시작합니다. 새 프로젝트를 선택하고 다음 대화 상자에서 IDEAL outphase 이미지가 포함된 폴더로 이동합니다. 다음을 클릭한 다음 변환 버튼을 클릭하여 시퀀스 제목 상태로 들어갑니다.빈 마스크를 만들려면 마스크 대화 상자에서 새로 만들기 버튼을 클릭하고 최대 임계값을 선택합니다. 세그먼트 레이블 아래에 있는 마스크 편집 도구를 사용하여 모든 수평 보기에서 간 영역을 구분합니다. 간의 3D 공간 부분을 생성하려면 바깥쪽 간 마스크를 선택하고 마스크에 대한 부분 계산 버튼을 클릭합니다.그런 다음 파일을 클릭한 다음 내보내기를 클릭하고 DICOM 명령을 선택합니다. 팝업 대화 상자에서 간 마스크를 선택하고 파일 경로와 이름을 설정 한 다음 OK 버튼을 눌러 간의 3D 영역을 지정된 DICOM 파일로 내보내기를 완료합니다.

extraction-of-3d-region-of-the-liver-for-hepatic-fibrosis-diagnosis

Research

JoVE Journal

Medicine

Extraction of 3D Region of the Liver for Hepatic Fibrosis Diagnosis

84 조회수.  Capital Medical University. 시작하려면 MATLAB을 여십시오. IDEAL 데이터의 가로 해상도가 256 x 256픽셀이고 픽셀 간격이 1.5625mm이고 슬라이스 두께가 10mm인지 확인합니다. 모든 DICOM 데이터를 사용자 지정 작업 디렉터리에 복사하고, MATLAB의 작업 디렉터리에 있는 데이터가 포함된 디렉터리로 이동합니다.그런 다음 설명 이름 함수를 실행하여 각 시퀀스의 폴더에 설명이 포함된 이름을 추가합니다. 각 ?...

비디오: 간 섬유증 진단을 위한 간의 3D 영역 추출

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the disease. Despite the good progress made with the liver stiffness map (LSM) based on magnetic resonance elastography (MRE), there are still some limitations that need to be overcome, including manual focus determination, manual selection of regions of interest (ROIs), and discontinuous LSM data without structural information, which makes it impossible to evaluate the liver as a whole. In this study, we propose a novel three-dimensional (3D) digital model for the early diagnosis of hepatic fibrosis based on MRE.
MRE is a non-invasive imaging technique that employs magnetic resonance imaging (MRI) to measure the liver stiffness at the scanning site through human-computer interaction. Studies have indicated a significant positive correlation between the LSM obtained through MRE and the degree of hepatic fibrosis. However, for clinical purposes, a comprehensive and precise quantification of the degree of hepatic fibrosis is necessary. To address this, the concept of Liver Stiffness Distribution (LSD) was proposed in this study, which refers to the 3D stiffness volume of each liver voxel obtained by the alignment of 3D liver tissue images and MRE indicators. This provides a more effective clinical tool for the diagnosis and treatment of hepatic fibrosis.

The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the ...

연구

의학

To begin, open MATLAB software. Ensure that the IDEAL data have a horizontal resolution of 256 by 256 pixels with a pixel spacing of 1.5625 millimeters and a slice thickness of 10 millimeters. Copy all DICOM data to a customized working directory, and navigate to the directory containing the data in MATLAB's working directory.Then execute the description name function to add descriptive names to the folders for each sequence. Add a description name to each image sequence folder. To check images of IDEAL, change the directory of different phase folders.Execute the slice view function to view the impact sequences for each phase. Next, quickly browse the different sequences using the scroll bar at the bottom of the GUI. Select the MRI IDEAL outphase sequence to depict liver tissue boundaries.Initiate the Mimics software. Select new project, and in the ensuing dialogue box, navigate to the folder containing the IDEAL outphase images. Click next, followed by the convert button to enter into the sequence heading state.To create an empty mask, from the mask dialogue box, click on the new button and select the maximum threshold. Using the edit masks tool located beneath the segment label, delimit the area of the liver in all horizontal views. To generate the 3D spatial part of the liver, select the outlying liver mask and click on the calculate part for mask button.Next, click file, then export, and select the DICOM command. In the pop-up dialogue box, choose the liver mask, and set the file path and names, then click the OK button to complete the export of the 3D region of the liver to the specified DICOM files.

Calculating the Distribution Ratio of the Patient's Liver at Different Fibrosis Stages

Calculating the Distribution Ratio of the Patient&#039;s Liver at Different Fibrosis Stages  

Begin by changing the directory to the folder SE27_ST8K_, Pa, which contains the liver stiffness map sequence. To browse through each stiffness map, execute the MRE_show function, with the function's argument being the file name located in the specified path. Observe the liver stiffness map in an RGB true color image, where each pixel point has three values representing the three primary colors.Calculate the exact stiffness for each pixel using their respective correlations. Then, establish the spatial relationship between MRE and ideal sequences. To obtain the 3D volume of liver stiffness distribution, invoke the LSD_Slice function with the 3D liver region and the liver stiffness map as input parameters.Then, drag the scroll bar below the GUI to view the stiffness map of each layer of the liver. Next, execute the LSD_Volume function with the same input as LSD_Slice to obtain the spatial distribution of the 3D liver LSD. View the 3D volume of LSD from any perspective by holding down the left mouse button.After determining the numerical ranges of stiffness values from four different stages of hepatic fibrosis, using the Hepatic_Fibrosis function with the 3D volume of LSD as the input parameter, calculate the distribution of the patient's entire liver voxels across different fibrosis stages. Then, calculate and compare the results between a completely healthy liver and the liver of a typical fibrosis patient. Quantitative results of the patient's hepatic fibrosis indicate the proportion of the patient's liver in different stages of hepatic fibrosis.Comparison of LSD fully reflects the degree of hepatic fibrosis in the patient compared to a healthy liver.