data collection from patient liver-mri datasets to validate the 3d liver fat fraction quantification

Dixon MRI를 사용한 포괄적인 3D-간 지방 기능 분포 모델

Non-Alcoholic Fatty Liver Disease (NAFLD) encompasses a spectrum of pathological conditions, ranging from the abnormal accumulation of triglycerides in liver cells (hepatic steatosis) to the development of inflammation and damage to liver cells, known as non-alcoholic Steatohepatitis (NASH). In some cases, NAFLD can progress to more severe stages, including fibrosis, cirrhosis, end-stage liver disease, or even Hepatocellular carcinoma (HCC)1. Published data from the World Health Organization and the Global Burden of Disease suggest that approximately 1,235.7 million individuals worldwide are affected by NAFLD across all age groups2. NAFLD currently ranks as one of the most prominent causes of liver-related diseases globally and is expected to become the leading cause of end-stage liver disease in the coming decades3.
The accurate assessment of hepatic steatosis&#39;s extent holds substantial importance for precise diagnosis, appropriate treatment selection, and effective disease progression monitoring. The gold standard for assessing liver fat content continues to be liver biopsy. However, due to its invasive nature, the potential for pain, bleeding, and other postoperative complications, it is not a practical option for frequent follow-up examinations4,5,6. Consequently, there is a pressing need for noninvasive imaging techniques that can reliably quantify hepatic fat deposition. Magnetic resonance imaging (MRI) shows promise in this area due to its lack of ionizing radiation and its ability to sensitively detect fat content through chemical shift effects7,8.
Recent studies have outlined MRI techniques for quantifying hepatic fat, based on chemical shift gradient echo methods like Dixon imaging9,10. Nonetheless, the majority of these techniques rely on the analysis of two-dimensional regions of interest. The comprehensive evaluation of the three-dimensional distribution of liver fat fraction (LFF) has remained limited. In the present study, a unique 3D LFF quantification approach is introduced, combining Dixon MRI with liver structural imaging. The resulting 3D LFF model allows for precise visualization and measurement of the distribution of fat content throughout the entire volume of the liver. This technique demonstrates substantial clinical utility for the accurate diagnosis of hepatic steatosis.

저자 스포트라이트: 3D Dixon MRI를 사용하여 간의 지방 패턴을 평가하고 구별하기 위한 비침습적 도구 

Dixon 자기 공명 영상을 사용한 간 지방 분획 분포 분석을 위한 3D 정량화 기법

This study developed a 3D Dixon MRI technique to precisely quantify liver fat distribution, aiming to validate its efficacy as an accurate, non-invasive tool for assessing and differentiating fat patterns in normal versus steatotic livers. Recent advances in MRI fat quantification include normal 3D distribution modeling approaches, integrating Dixon Imaging with liver structure segmentation to enable precise visualization and environment of liver fat fraction patterns. Integrating AI-driven medical imaging, deep learning, 3D visualization, and a multimodal data fusion enhances whole liver modeling and steatotic assessment, facilitating precise clinic decision-making.Current experimental challenges include standardizing computational workflows, addressing variations in equipment and protocols, and elucidating the mechanisms of disease progression in hepatic steatosis research. Our protocol offers the advantage of 3D liver fat fraction assessment, surpassing the 2D techniques. The findings will advance research in our field by offering a comprehensive and accurate 3D LFF assessment of different stages of fatty liver disease, which is crucial for treatment decisions and prognosis evaluation.

Watch this Scientific Journal Video about Data Collection from Patient Liver-MRI Datasets to Validate the 3D Liver Fat Fraction Quantification at JoVE.com

Data Collection from Patient Liver-MRI Datasets to Validate the 3D Liver Fat Fraction Quantification  

3D 간 지방 분획 정량화를 검증하기 위한 환자 간-MRI 데이터 세트의 데이터 수집

상복부에 대한 딕슨 시퀀스 확인을 시작하려면 사용자 지정 작업 디렉터리를 만들고 모든 DICOM 데이터를 생성된 디렉터리에 복사합니다. 그런 다음 MATLAB의 현재 작업 디렉터리 내에 있는 데이터가 들어 있는 디렉터리로 이동합니다. 디렉토리를 변경하여 동상, 위상 외, 물 및 지방 단계와 같은 다양한 단계에 대한 폴더에 액세스합니다.Slice_View 기능을 활용하여 각 단계에 대한 이미지를 봅니다. MRI Dixon 동위상 염기서열을 사용하여 간 조직 경계에 대한 설명을 향상시킵니다. 색상 막대를 마우스 오른쪽 버튼으로 클릭하고 팝업 메뉴에서 옵션을 선택하여 표준 회색 색상 맵으로 전환하고 전체 GUI를 재설정합니다.이미지를 적절하게 확대/축소한 후 픽셀 좌표 표시 기능을 사용하여 두 점 사이의 거리를 계산하여 결절 크기를 측정하는 데 도움이 됩니다. 그래픽 사용자 인터페이스 또는 GUI의 맨 아래에 있는 스크롤 막대를 사용하여 다양한 시퀀스를 효율적으로 탐색할 수 있습니다. 디폴트 필터 창이 적합하지 않은 경우, Figure 가운데에서 위아래로 드래그하고 마우스 왼쪽 버튼을 사용하여 창 레벨을 수정하여 조정합니다.

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Research

JoVE Journal

Medicine

Data Collection from Patient Liver-MRI Datasets to Validate the 3D Liver Fat Fraction Quantification

123 조회수.  Beijing University of Chinese Medicine. 상복부에 대한 딕슨 시퀀스 확인을 시작하려면 사용자 지정 작업 디렉터리를 만들고 모든 DICOM 데이터를 생성된 디렉터리에 복사합니다. 그런 다음 MATLAB의 현재 작업 디렉터리 내에 있는 데이터가 들어 있는 디렉터리로 이동합니다. 디렉토리를 변경하여 동상, 위상 외, 물 및 지방 단계와 같은 다양한 단계에 대한 폴더에 액세스합니다.Slice_View 기능을 활용하여 각 단계?...

비디오: Data Collection from Patient Liver-MRI Datasets to Validate the 3D Liver Fat Fraction Quantification  

A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. The central aim is to offer a highly accurate and non-invasive means of evaluating liver fat content. The process involves the acquisition of In-phase and Water-phase images from a Dixon sequence. LFF maps are then meticulously computed voxel by voxel by dividing the Lipid Phase images by the In-phase images. Simultaneously, 3D liver contours are extracted from the In-phase images. These crucial components are seamlessly integrated to construct a comprehensive 3D-LFF distribution model. This technique is not limited to healthy livers but extends to those afflicted by hepatic steatosis. The results obtained demonstrate the remarkable effectiveness of this approach in both visualizing and quantifying liver fat content. It distinctly discerns patterns that differentiate between normal and steatotic livers. By harnessing Dixon MRI to extract the 3D structure of the liver, this method offers precise LFF assessments spanning the entirety of the organ, thereby holding great promise for the diagnosis of hepatic steatosis with remarkable effectiveness.

This study introduces a unique 3D quantification method for liver fat fraction (LFF) distribution using Dixon Magnetic Resonance Imaging (Dixon MRI). LFF maps, derived from in-phase and water-phase images, are integrated with 3D liver contours to differentiate LFF patterns between normal and steatotic livers, enabling precise assessment of liver fat content.

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. ...

연구

의학

To begin checking the Dixon sequence for the upper abdomen, create a customized working directory and copy all DICOM data to the created directory. Then go to the directory housing the data within MATLAB's current working directory. Change the directory to access folders for different phases, like in-phase, out-phase, water and fat phases.Utilize the Slice_View function to view the image for each phase. Use the MRI Dixon in-phase sequence to enhance descriptions of liver tissue boundaries. Right-click on the color bar and select the option from the pop-up menu to switch to the standard gray color map and reset the entire GUI.After zooming the image appropriately, Use the Mark Pixel Coordinates feature to calculate the distance between two points, aiding in the measurement of nodule sizes. Employ the scroll bar situated at the bottom of the graphic user interface, or GUI, to navigate through the various sequences efficiently. If the default filter window is not suitable, adjust it by dragging it up and down in the middle of the figure, using the left mouse button to modify the window level.

Extracting the 3D Region of the Liver and Computing Liver Fat Fraction Using MRI-Dixon Data

To begin extracting the 3D region of the liver from the Dixon MRI data, open the Mimics software and choose new project. In the subsequent dialogue box, locate the folder housing the Dixon out phase images. Click on next, then click on continue, and then hit convert to enter the sequence editing mode.Click on new within the mask dialogue box situated on the right hand side to generate an empty mask and opt for the maximum threshold. Use the edit masks tool located under the segment label to delineate the liver area in all horizontal views. Choose the liver mask depicted earlier and click on calculate part from the mask to create a 3D spatial representation of the liver.Navigate to file, then select export, and choose the DICOM option. In the pop-up dialogue box, select the liver mask, specify the file path and names, and then click on OK to export the 3D liver region to the designated DICOM files. Change the directory to the folder of in phase images and select the Volume_In function to generate the in phase volume.Change the directory to the folder of water only images and select the Volume_Water function to generate the water only volume. Select the FF volume function and use the two volumes previously generated as inputs to obtain the FF volume of the abdominal MRI. Utilize the LFF function, providing it with the 3D liver region and the liver stiffness map as input parameters.Run the LFF distribution function using the identical input parameters as LFF volume to produce the spatial distribution of the 3D liver fat fraction. Fusing the 3D liver contour with the 2D FF map generated an integrated 3D FF distribution model. This revealed fat fraction values at different liver positions, enabling precise measurement of the proportion of the liver at various steatosis levels.Comparison between a normal and fatty liver validated the technique's ability to discern different 3D LFF distribution patterns.