Name: 著者スポットライト:3D Dixon MRIを使用して肝臓の脂肪パターンを評価および区別するための非侵襲的ツール
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

この出版物は、国家中医薬総局が主催する第5回伝統中医薬の優れた臨床的才能の発掘のための国家プログラムの支援を受けました。公式ネットワークリンクは「http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html.

この研究は承認され、患者は中国の北京にある北京中医薬大学東直門病院の感染症科から募集されました。患者は、インフォームドコンセントを提供した後、定期的な腹部ディクソンMRIスキャンを受けました。この調査では、3D分布モデリングアプローチを採用して、医学的に診断された脂肪肝症の標準的な患者の肝脂肪分画(LFF)を再構築します。さらに、この研究では、患者の肝臓と健康な...

Dixon MRIを用いた網羅的3次元肝脂肪機能分布モデル

<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>

この研究は、Dixon MRI 9,10を使用して肝脂肪分率(LFF)の分布を分析するための革新的な3D定量化技術を提示します。同相画像と水相画像から生成されるLFFマップを3D肝臓の輪郭と統合することにより、正常肝臓と脂肪性肝臓のLFFパターンを区別する6。その結果、肝臓の脂肪含有量の正確な評価が容易になります。
...

非アルコール性脂肪性肝疾患(NAFLD)は、肝細胞におけるトリグリセリドの異常な蓄積(脂肪肝)から、非アルコール性脂肪性肝炎(NASH)として知られる肝細胞の炎症および損傷の発症に至るまで、さまざまな病理学的状態を網羅しています。場合によっては、NAFLDは線維症、肝硬変、末期肝疾患、さらには肝細胞がん(HCC)など、より重篤な病期に進行することがあります1。世界保健機関(WHO)と世界疾病負荷(Global Burden of Disease)が公表したデータによると、全世界で約12億3,570万人がすべての年齢層でNAFLDに罹患しています2。NAFLDは現在、世界的に肝臓関連疾患の最も顕著な原因の1つとしてランク付けされており、今後数十年で末期肝疾患の主要な原因になると予想されています3。
脂肪肝の程度を正確に評価することは、正確な診断、適切な治療法の選択、および効果的な疾患進行のモニタリングにとって非常に重要です。肝脂肪含有量を評価するためのゴールドスタンダードは、引き続き肝生検です。しかし、その侵襲性、痛み、出血、その他の術後合併症の可能性があるため、頻繁なフォローアップ検査の実用的な選択肢ではありません4,5,6。その結果、肝脂肪沈着を確実に定量化できる非侵襲的イメージング技術が急務となっています。磁気共鳴画像法(MRI)は、電離放射線がなく、化学シフト効果によって脂肪含有量を高感度に検出できるため、この分野で有望視されています7,8。
最近の研究では、Dixonイメージング9,10のような化学シフト勾配エコー法に基づいて、肝脂肪を定量化するためのMRI技術が概説されています。それにもかかわらず、これらの手法の大部分は、2次元の関心領域の分析に依存しています。肝脂肪分率(LFF)の3次元分布の包括的な評価は、依然として制限されています。本研究では、Dixon MRIと肝構造イメージングを組み合わせた独自の3D LFF定量化アプローチを紹介します。得られた3D LFFモデルにより、肝臓の全容積にわたる脂肪含有量の分布を正確に視覚化および測定できます。この技術は、脂肪肝の正確な診断にかなりの臨床的有用性を示しています。

<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

本研究では、Dixon MRI画像解析を用いた肝脂肪分率(LFF)分布の3D定量化手法を提示する。主な目的は、肝臓の脂肪含有量を評価するための非常に正確で非侵襲的な手段を提供することです。このプロセスには、ディクソンシーケンスからの同相および水相画像の取得が含まれます。LFFマップは、脂質相の画像を同相の画像で割ることにより、ボクセルごとに細心の注意を払って計算されます。同時に、3D肝臓の輪郭が同相画像から抽出されます。これらの重要なコンポーネントがシームレスに統合され、包括的な3D-LFF分散モデルが構築されます。この技術は健康な肝臓に限定されず、脂肪肝に苦しむ人々にも及びます。得られた結果は、肝脂肪含有量の視覚化と定量化の両方において、このアプローチの顕著な有効性を示しています。正常な肝臓と脂肪性の肝臓を区別するパターンを明確に識別します。Dixon MRIを用いて肝臓の3次元構造を抽出することで、臓器全体にわたる精密なLFF評価が可能で、脂肪肝の診断に期待が持てる大きな期待が寄せられています。

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.

著者スポットライト:3D Dixon MRIを使用して肝臓の脂肪パターンを評価および区別するための非侵襲的ツール 

Dixon磁気共鳴画像法を用いた肝脂肪分率分布解析のための3次元定量化技術

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.

この調査では、市販のMRIスキャナーを使用して取得した実際の患者データセットを利用して、3D肝脂肪分画定量方法を検証します(図1)。MRIプロトコルには、Dixonの4相画像9,10(同相、逆位相、水のみ、脂肪のみ)が含まれていました(図2)。各ボクセルの脂肪分率 (FF) は、Dixon MRI を使用して、同相から水のみ?...

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

この研究では、Dixon Magnetic Resonance Imaging (Dixon MRI) を使用した肝脂肪分率 (LFF) 分布の独自の 3D 定量化方法を紹介します。同相画像と水相画像から得られたLFFマップは、3D肝臓の輪郭と統合され、正常な肝臓と脂肪性の肝臓のLFFパターンを区別し、肝臓脂肪含有量の正確な評価を可能にします。

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

この研究では、肝脂肪分布を正確に定量化するための3DディクソンMRI技術を開発し、正常肝臓と脂肪肝の脂肪パターンを評価および区別するための正確で非侵襲的なツールとしての有効性を検証することを目的としています。MRI脂肪定量化の最近の進歩には、正常な3D分布モデリングアプローチがあり、Dixon Imagingと肝臓構造のセグメンテーションを統合して、肝臓脂肪分画パターンの正確な視覚化と環境を可能にします。AI駆動の医用画像、ディープラーニング、3Dビジュアライゼーション、マルチモーダルデータフュージョンを統合することで、肝臓全体のモデリングと脂肪質の評価が強化され、正確な臨床の意思決定が容易になります。現在の実験上の課題には、計算ワークフローの標準化、機器やプロトコルのばらつきへの対応、脂肪肝研究における疾患進行メカニズムの解明などがあります。私たちのプロトコルは、2D技術を凌駕する3D肝脂肪分率評価の利点を提供します。この知見は、治療の決定と予後評価に不可欠な脂肪肝疾患のさまざまな段階の包括的かつ正確な3D LFF評価を提供することにより、この分野の研究を前進させます。

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.

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.

3D肝脂肪分画の定量化を検証するための患者の肝臓MRIデータセットからのデータ収集

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.