Name: Forfatter Spotlight: Et ikke-invasivt værktøj til at vurdere og differentiere fedtmønstre i leveren ved hjælp af 3D Dixon MR
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

Denne publikation modtog støtte fra det femte nationale program til identifikation af fremragende kliniske talenter inden for traditionel kinesisk medicin, organiseret af National Administration of Traditional Chinese Medicine. Det officielle netværkslink er'http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html.

Undersøgelsen blev godkendt, og patienten blev rekrutteret fra afdelingen for infektionssygdomme på Dongzhimen Hospital, Beijing University of Chinese Medicine, i Beijing, Kina. Patienten gennemgik en rutinemæssig abdominal Dixon MR-scanning efter at have givet informeret samtykke. I denne undersøgelse anvendes en 3D-fordelingsmodelleringsmetode til at rekonstruere leverfedtfraktionen (LFF) hos en standardpatient med medicinsk diagnosticeret hepatisk steatose. Desuden giver undersøgelsen en kvantitativ vurdering, de...

En omfattende 3D-leverfedtfunktionsfordelingsmodel ved hjælp af Dixon MR

<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>

Denne forskning præsenterer en innovativ 3D-kvantificeringsteknik til analyse af fordelingen af leverfedtfraktion (LFF) ved hjælp af Dixon MRI 9,10. Ved at integrere LFF-kort, der genereres fra billeder i fase og vandfase, med 3D-leverkonturer, skelner denne metode mellem LFF-mønstre i normale og steatotiske lever6. Derfor letter det en præcis evaluering af leverfedtindholdet.
Trin 3 repræsenterer et vigtig...

Ikke-alkoholisk fedtleversygdom (NAFLD) omfatter et spektrum af patologiske tilstande, lige fra unormal ophobning af triglycerider i leverceller (hepatisk steatose) til udvikling af betændelse og skade på leverceller, kendt som ikke-alkoholisk steatohepatitis (NASH). I nogle tilfælde kan NAFLD udvikle sig til mere alvorlige stadier, herunder fibrose, skrumpelever, leversygdom i slutstadiet eller endda hepatocellulært karcinom (HCC)1. Offentliggjorte data fra Verdenssundhedsorganisationen og Global Burden of Disease tyder på, at ca. 1.235.7 millioner individer over hele verden er ramt af NAFLD på tværs af alle aldersgrupper2. NAFLD rangerer i øjeblikket som en af de mest fremtrædende årsager til leverrelaterede sygdomme globalt og forventes at blive den førende årsag til leversygdom i slutstadiet i de kommende årtier3.
Den nøjagtige vurdering af omfanget af hepatisk steatose har stor betydning for præcis diagnose, passende behandlingsvalg og effektiv overvågning af sygdomsprogression. Guldstandarden til vurdering af leverfedtindhold er fortsat leverbiopsi. På grund af dets invasive karakter, potentialet for smerte, blødning og andre postoperative komplikationer er det imidlertid ikke en praktisk mulighed for hyppige opfølgningsundersøgelser 4,5,6. Derfor er der et presserende behov for ikke-invasive billeddannelsesteknikker, der pålideligt kan kvantificere leverfedtaflejring. Magnetisk resonansbilleddannelse (MRI) viser lovende på dette område på grund af dets mangel på ioniserende stråling og dets evne til følsomt at detektere fedtindhold gennem kemiske skifteffekter 7,8.
Nylige undersøgelser har skitseret MR-teknikker til kvantificering af leverfedt, baseret på kemiske skiftgradientekkometoder som Dixon-billeddannelse 9,10. Ikke desto mindre er størstedelen af disse teknikker afhængige af analysen af todimensionale regioner af interesse. Den omfattende evaluering af den tredimensionelle fordeling af leverfedtfraktion (LFF) har været begrænset. I denne undersøgelse introduceres en unik 3D LFF-kvantificeringsmetode, der kombinerer Dixon MR med leverstrukturel billeddannelse. Den resulterende 3D LFF-model giver mulighed for præcis visualisering og måling af fordelingen af fedtindhold gennem hele levervolumenet. Denne teknik demonstrerer betydelig klinisk anvendelighed til nøjagtig diagnose af hepatisk steatose.

<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

Denne undersøgelse præsenterer en 3D-kvantificeringsmetode til fordeling af leverfedtfraktion (LFF) gennem anvendelse af Dixon MR-billedanalyse. Det centrale mål er at tilbyde et meget præcist og ikke-invasivt middel til evaluering af leverfedtindhold. Processen involverer erhvervelse af In-fase og Water-fase billeder fra en Dixon sekvens. LFF-kort beregnes derefter omhyggeligt voxel for voxel ved at dividere lipidfasebillederne med billederne i fasen. Samtidig ekstraheres 3D-leverkonturer fra billederne i fasen. Disse vigtige komponenter integreres problemfrit for at konstruere en omfattende 3D-LFF-distributionsmodel. Denne teknik er ikke begrænset til sunde lever, men strækker sig til dem, der er ramt af hepatisk steatose. De opnåede resultater viser den bemærkelsesværdige effektivitet af denne tilgang til både visualisering og kvantificering af leverfedtindhold. Det skelner tydeligt mønstre, der skelner mellem normale og steatotiske lever. Ved at udnytte Dixon MR til at ekstrahere leverens 3D-struktur tilbyder denne metode præcise LFF-vurderinger, der spænder over hele organet, og giver derved et stort løfte om diagnosticering af hepatisk steatose med bemærkelsesværdig effektivitet.

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.

Forfatter Spotlight: Et ikke-invasivt værktøj til at vurdere og differentiere fedtmønstre i leveren ved hjælp af 3D Dixon MR 

En 3D-kvantificeringsteknik til analyse af leverfedtfraktionsfordeling ved hjælp af Dixon magnetisk resonansbilleddannelse

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.

Denne undersøgelse anvender faktiske patientdatasæt erhvervet ved hjælp af en kommercielt tilgængelig MR-scanner til at validere 3D-leverfedtfraktionskvantificeringsmetoden (figur 1). MR-protokollen omfattede Dixons firefasebilleddannelse 9,10: In-phase, Out-of-phase, Water-only og Fat-only (figur 2). Fedtfraktionen (FF) for hver voxel beregnes ved at dividere In-fase minus Water-only voxel-signalet ...

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

Denne undersøgelse introducerer en unik 3D-kvantificeringsmetode til fordeling af leverfedtfraktion (LFF) ved hjælp af Dixon Magnetic Resonance Imaging (Dixon MRI). LFF-kort, der stammer fra billeder i fase og vandfase, er integreret med 3D-leverkonturer for at skelne LFF-mønstre mellem normale og steatotiske lever, hvilket muliggør præcis vurdering af leverfedtindhold.

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

Denne undersøgelse udviklede en 3D Dixon MR-teknik til præcist at kvantificere leverfedtfordeling med det formål at validere dens effektivitet som et nøjagtigt, ikke-invasivt værktøj til vurdering og differentiering af fedtmønstre i normale versus steatotiske lever. Nylige fremskridt inden for MR-fedtkvantificering inkluderer normale 3D-fordelingsmodelleringsmetoder, der integrerer Dixon Imaging med leverstruktursegmentering for at muliggøre præcis visualisering og miljø af leverfedtfraktionsmønstre. Integration af AI-drevet medicinsk billeddannelse, dyb læring, 3D-visualisering og en multimodal datafusion forbedrer hellevermodellering og steatotisk vurdering, hvilket letter præcis klinisk beslutningstagning.Nuværende eksperimentelle udfordringer omfatter standardisering af beregningsmæssige arbejdsgange, adressering af variationer i udstyr og protokoller og belysning af mekanismerne for sygdomsprogression i hepatisk steatoseforskning. Vores protokol giver fordelen ved 3D leverfedtfraktionsvurdering, der overgår 2D-teknikkerne. Resultaterne vil fremme forskningen inden for vores felt ved at tilbyde en omfattende og nøjagtig 3D LFF-vurdering af forskellige stadier af fedtleversygdom, hvilket er afgørende for behandlingsbeslutninger og prognoseevaluering.

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.

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.

Dataindsamling fra patientlever-MR-datasæt til validering af kvantificering af 3D-leverfedtfraktion

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.