Name: 作者聚焦：一种使用 3D Dixon MRI 评估和区分肝脏脂肪模式的非侵入性工具
Uploaded: 2023-10-20T15:00:00
Description: Watch this Scientific Journal Video about A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI at JoVE.com

该出版物得到了国家中医药管理局组织的第五届国家中医药临床优秀人才认定计划的支持。官方网链接为'http：//www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html。

该研究获得批准，患者从中国北京中医药大学东直门医院感染科招募。患者在提供知情同意后接受了常规的腹部 Dixon MRI 扫描。在这项研究中，采用 3D 分布建模方法重建医学诊断为肝脂肪变性的标准患者的肝脏脂肪分数 （LFF）。此外，该研究还提供了将患者的肝脏与健康肝脏进行比较的定量评估。本研究中使用的软件工具列在 材料表中。
1. 准备和数据收集<...

使用 Dixon MRI 的综合 3D 肝脏脂肪功能分布模型

<ol>
	<li>Pouwels, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Non-alcoholic+fatty+liver+disease+(NAFLD):+a+review+of+pathophysiology,+clinical+management+and+effects+of+weight+loss.">Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss.</a> BMC Endocr Disord. 22 (1), 63 (2022).</li><li>Younossi, Z. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+global+burden+of+liver+disease.">The global burden of liver disease.</a> Clin Gastroenterol Hepatol. 21 (8), 1978-1991 (2023).</li><li>Younossi, Z., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+burden+of+NAFLD+and+NASH.+Trends,+predictions,+risk+factors+and+prevention.">Global burden of NAFLD and NASH. Trends, predictions, risk factors and prevention.</a> Nat Rev Gastroenterol Hepatol. 15 (1), 11-20 (2018).</li><li>Boyum, J. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Incidence+and+risk+factors+for+adverse+events+related+to+image-guided+liver+biopsy.">Incidence and risk factors for adverse events related to image-guided liver biopsy.</a> Mayo Clin Proc. 91 (3), 329-335 (2016).</li><li>Khalifa, A., Rockey, D. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+utility+of+liver+biopsy+in+2020.">The utility of liver biopsy in 2020.</a> Curr Opin Gastroenterol. 36 (3), 184-191 (2020).</li><li>Sumida, Y., Nakajima, A., Itoh, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Limitations+of+liver+biopsy+and+non-invasive+diagnostic+tests+for+the+diagnosis+of+nonalcoholic+fatty+liver+disease/nonalcoholic+steatohepatitis.">Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis.</a> World J Gastroenterol. 20 (2), 475-485 (2014).</li><li>Ajmera, V., Loomba, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Imaging+biomarkers+of+NAFLD,+NASH,+and+fibrosis.">Imaging biomarkers of NAFLD, NASH, and fibrosis.</a> Mol Metab. 50, 101167 (2021).</li><li>Castera, L., Friedrich-Rust, M., Loomba, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noninvasive+assessment+of+liver+disease+in+patients+with+nonalcoholic+fatty+liver+disease.">Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease.</a> Gastroenterology. 156 (5), 1264-1281 (2019).</li><li>Jiang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+multi-echo+Dixon+and+MRS+in+quantifying+hepatic+fat+content+and+staging+liver+fibrosis.">Application of multi-echo Dixon and MRS in quantifying hepatic fat content and staging liver fibrosis.</a> Sci Rep. 13 (1), 12555 (2023).</li><li>Yang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+accuracy+and+clinical+relevance+of+the+multi-echo+dixon+technique+for+evaluating+changes+to+hepatic+steatosis+in+patients+with+non-alcoholic+fatty+liver+disease+treated+with+formulated+food.">The accuracy and clinical relevance of the multi-echo dixon technique for evaluating changes to hepatic steatosis in patients with non-alcoholic fatty liver disease treated with formulated food.</a> Magn Reson Med Sci. 22 (2), 263-271 (2023).</li><li>Peng, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fat-water+separation+based+on+Transition+REgion+Extraction+(TREE).">Fat-water separation based on Transition REgion Extraction (TREE).</a> Magn Reason Med. 82 (1), 436-448 (2019).</li><li>Hines, C. D. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=T(1)+independent,+T(2)(*)+corrected+chemical+shift+based+fat-water+separation+with+multi-peak+fat+spectral+modeling+is+an+accurate+and+precise+measure+of+hepatic+steatosis.">T(1) independent, T(2)(*) corrected chemical shift based fat-water separation with multi-peak fat spectral modeling is an accurate and precise measure of hepatic steatosis.</a> Magn Reason Imaging. 33 (1), 873-881 (2011).</li><li>Tang, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nonalcoholic+fatty+liver+disease:+MR+imaging+of+liver+proton+density+fat+fraction+to+assess+hepatic+steatosis.">Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis.</a> Radiology. 267 (2), 422-431 (2013).</li><li>Caussy, C., Reeder, S. B., Sirlin, C. B., Loomba, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Noninvasive,+quantitative+assessment+of+liver+fat+by+MRI-PDFF+as+an+endpoint+in+NASH+Trials.">Noninvasive, quantitative assessment of liver fat by MRI-PDFF as an endpoint in NASH Trials.</a> Hepatology. 68 (2), 763-772 (2018).</li><li>Reeder, S. B., Cruite, I., Hamilton, G., Sirlin, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+assessment+of+liver+fat+with+magnetic+resonance+imaging+and+spectroscopy.">Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy.</a> Magn Reson Imaging. 34 (4), 729-749 (2011).</li></ol>

本研究提出了一种创新的 3D 量化技术，用于使用 Dixon MRI 9,10 分析肝脏脂肪分数 （LFF） 的分布。通过将从同相和水相图像生成的 LFF 图与 3D 肝脏轮廓相结合，该方法区分了正常肝脏和脂肪变性肝脏中的 LFF 模式6。因此，它有助于精确评估肝脏脂肪含量。
第 3 步代表了计算 FF 图以量化每个体素中脂肪含量的关键阶?...

非酒精性脂肪性肝病（NAFLD）包括一系列病理状况，从肝细胞中甘油三酯的异常积累（肝脂肪变性）到炎症的发展和肝细胞损伤，称为非酒精性脂肪性肝炎（NASH）。在某些情况下，NAFLD可以发展到更严重的阶段，包括纤维化、肝硬化、终末期肝病，甚至肝细胞癌（HCC）1。世界卫生组织和全球疾病负担的公布数据表明，全世界约有12.357亿人受到NAFLD所有年龄组的影响2。NAFLD目前是全球肝脏相关疾病最突出的原因之一，预计在未来几十年内将成为终末期肝病的主要原因3。
准确评估肝脂肪变性程度对于准确诊断、适当选择治疗方案和有效监测疾病进展具有重要意义。评估肝脏脂肪含量的金标准仍然是肝活检。然而，由于其侵入性，潜在的疼痛、出血和其他术后并发症，它不是频繁随访检查的实用选择4,5,6。因此，迫切需要能够可靠地量化肝脏脂肪沉积的无创成像技术。磁共振成像 （MRI） 在这一领域显示出前景，因为它缺乏电离辐射，并且能够通过化学位移效应灵敏地检测脂肪含量 7,8。
最近的研究概述了基于化学位移梯度回波方法（如Dixon成像9,10）的MRI技术，用于量化肝脏脂肪。尽管如此，这些技术中的大多数都依赖于对感兴趣的二维区域的分析。对肝脏脂肪分数（LFF）三维分布的综合评价仍然有限。在本研究中，引入了一种独特的 3D LFF 定量方法，将 Dixon MRI 与肝脏结构成像相结合。由此产生的 3D LFF 模型可以精确可视化和测量脂肪含量在整个肝脏体积中的分布。该技术在准确诊断肝脂肪变性方面显示出重要的临床实用性。

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MATLAB</td>
			<td>MathWorks&#160;</td>
			<td>2022B</td>
			<td>Computing and visualization&#160;</td>
		</tr>
		<tr>
			<td>Mimics</td>
			<td>Materialise</td>
			<td>Mimics Research V20</td>
			<td>Model format transformation</td>
		</tr>
		<tr>
			<td>Tools for 3D_LFF</td>
			<td>Intelligent Entropy</td>
			<td>HepaticSteatosis V1.0</td>
			<td>Beijing Intelligent Entropy Science &#38; Technology Co Ltd. 
			Modeling for CT/MRI fusion</td>
		</tr>
	</tbody>
</table>

a 3d quantification technique for liver fat fraction distribution analysis using dixon magnetic resonance imaging

本研究提出了一种通过利用 Dixon MRI 图像分析来测量肝脏脂肪分数 （LFF） 分布的 3D 定量方法。其核心目的是提供一种高度准确和非侵入性的方法来评估肝脏脂肪含量。该过程涉及从Dixon序列中获取同相和水相图像。然后，通过将脂质相图像除以同相图像，逐个细致地计算 LFF 图。同时，从相位图像中提取 3D 肝脏轮廓。这些关键组件无缝集成，以构建全面的 3D-LFF 分布模型。这种技术不仅限于健康的肝脏，还延伸到那些患有肝脂肪变性的人。获得的结果证明了这种方法在可视化和量化肝脏脂肪含量方面的显着有效性。它清楚地辨别区分正常肝脏和脂肪变性肝脏的模式。通过利用Dixon MRI提取肝脏的3D结构，该方法提供了跨越整个器官的精确LFF评估，从而为肝脂肪变性的诊断带来了巨大的希望，并具有显着的有效性。

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.

作者聚焦：一种使用 3D Dixon MRI 评估和区分肝脏脂肪模式的非侵入性工具 

使用Dixon磁共振成像分析肝脏脂肪分数分布的3D定量技术

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.

该研究利用使用市售 MRI 扫描仪获取的实际患者数据集来验证 3D 肝脏脂肪分数量化方法（图 1）。MRI 协议包括 Dixon 的四相成像 9,10：同相、异相、仅水和仅脂肪（图 2）。通过使用 Dixon MRI 将同相减去纯水体素信号除以同相体素信号来计算每个体素的脂肪分数 （FF）。该数值模型可以准确计算每个体素中的脂?...

Watch this Scientific Journal Video about A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI at JoVE.com

本研究引入了一种独特的 3D 定量方法，使用 Dixon 磁共振成像 （Dixon MRI） 对肝脏脂肪分数 （LFF） 分布进行定量。从同相和水相图像得出的 LFF 图与 3D 肝脏轮廓集成，以区分正常肝脏和脂肪变性肝脏之间的 LFF 模式，从而能够精确评估肝脏脂肪含量。

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

本研究开发了一种 3D Dixon MRI 技术来精确量化肝脏脂肪分布，旨在验证其作为评估和区分正常肝脏与脂肪变性肝脏脂肪模式的准确、非侵入性工具的功效。MRI 脂肪定量的最新进展包括正态 3D 分布建模方法，将 Dixon 成像与肝脏结构分割相结合，以实现肝脏脂肪分数模式的精确可视化和环境。集成人工智能驱动的医学成像、深度学习、3D 可视化和多模态数据融合，增强了全肝建模和脂肪变性评估，促进了精确的临床决策。目前的实验挑战包括标准化计算工作流程，解决设备和方案的变化，以及阐明肝脂肪变性研究中疾病进展的机制。我们的方案提供了 3D 肝脏脂肪分数评估的优势，超越了 2D 技术。这些发现将通过对脂肪肝疾病的不同阶段提供全面准确的3D LFF评估来推进我们领域的研究，这对于治疗决策和预后评估至关重要。

a-3d-quantification-technique-for-liver-fat-fraction-distribution

Research

JoVE Journal

Medicine

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.

科研

医学

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

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.

从患者肝脏 MRI 数据集收集数据以验证 3D 肝脏脂肪分数量化

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.