This publication received support from the fifth national program for the identification of outstanding clinical talents in traditional Chinese medicine, organized by the National Administration of Traditional Chinese Medicine. The official network link is&#39;http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html.

The study was approved, and the patient was recruited from the Department of Infectious Diseases at Dongzhimen Hospital, Beijing University of Chinese Medicine, in Beijing, China. The patient underwent a routine abdominal Dixon MRI scan after providing informed consent. In this investigation, a 3D distribution modeling approach is employed to reconstruct the liver fat fraction (LFF) in a standard patient with medically diagnosed hepatic steatosis. Furthermore, the study provides a quantitative assessment comparing the patient&#39;s liver with a healthy liver. The software tools utilized in this research are listed in the Table of Materials.
1. Preparation and data collection
NOTE: The variance in parameters remains unaffected by the research approach. In this investigation, genuine DICOM data were obtained from clinical imaging. The data were acquired using a MRI apparatus with a field strength of 1.5 Tesla. The dataset comprises four distinct phases derived from the Dixon sequence, specifically In-phase, Out-of-phase, Water, and Fat.
<ol>
	<li>Check the Dixon sequence for the upper abdomen.
	<ol>
		<li>Copy all DICOM data to a customized working directory. 
		NOTE: The working directory is the same in both the operating system and MATLAB.</li>
		<li>Go to the directory housing the data within MATLAB&#39;s current working directory (as shown in Figure 1).</li>
		<li>Confirm that the DIXON data displays a horizontal resolution of 520 pixels by 704 pixels, with a pixel spacing of 0.5682 mm and a slice thickness of 5 mm.</li>
	</ol>
	</li>
	<li>Quickly check images of Dixon.
	<ol>
		<li>Change the directory to access folders for different phases, which include In-phase, Out-phase, Water, and Fat phases, each stored in distinct folders for Dixon imaging.</li>
		<li>Utilize the Slice_View function to visualize the images for each phase.</li>
		<li>See Figure 2 to view an image of the MRI-Dixon sequence&#39;s interactive graphical user interface (GUI). Employ the scroll bar situated at the bottom of the GUI to efficiently navigate through the various sequences.</li>
		<li>Employ the MRI-Dixon In-phase sequence to enhance descriptions of liver tissue boundaries. 
		NOTE: In Figure 2, one can find icons located in the upper right corner of the GUI. These icons offer functions such as zooming out, zooming in, returning to the global view, and marking the coordinates of the selected pixel. Make use of the zoom feature to closely examine local lesion features.</li>
		<li>Use the Mark pixel coordinates feature to calculate the distance between two points, aiding in the measurement of nodule sizes.</li>
		<li>The initial color bar uses the jet colormap, which signifies a color shift from blue to red, signifying low to high values respectively. To switch to the standard gray colormap and reset the entire GUI, right-click on the Color Bar and select the option from the pop-up menu.</li>
		<li>If the default filter window is not suitable, adjust it by dragging up and down in the middle of the figure using the left mouse button to modify the window level. Dragging left and right adjusts the window width, and the precise filtering range will be shown on the color bar. 
		&#8203;NOTE: Subsequent operations will focus on utilizing the DIXON In-phase sequence to represent the 3D region of the liver.</li>
	</ol>
	</li>
</ol>
2. Extracting the 3D region of the liver
NOTE: To compute the Liver Fat Fraction (LFF), each voxel within the 3D liver region acts as a spatial carrier, with its fat fraction value obtained from MRI-Dixon data. Before calculating LFF, it&#39;s crucial to extract the 3D liver region. Although deep learning methods could achieve this more efficiently, the focus here is on using mature software tools like MIMICS for liver region extraction.
<ol>
	<li>Open the MIMICS software and choose New Project. In the subsequent dialog box, locate the folder housing the DIXON Out-phase images. Continue by clicking on NEXT, and then hit Convert to enter the sequence-editing mode.</li>
	<li>Generate an empty Mask by clicking on New within the MASK dialog box situated on the right-hand side, and opt for the maximum threshold.</li>
	<li>Use the Edit Masks tool located under the &#34;Segment&#34; label to delineate the liver area in all horizontal views.</li>
	<li>Create a 3D spatial representation of the liver by choosing the liver mask depicted earlier and clicking on Calculate Part from Mask. The resulting 3D liver region will be presented (refer to Figure 3).</li>
	<li>Navigate to File, then select Export, and choose the DICOM option. In the pop-up dialog 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.</li>
</ol>
3. Generating Fat Fraction Map (FF-Map)
NOTE: The fat fraction map (FF-Map) has a value range of 0-1. In this study, the FF of each voxel is calculated by dividing the voxel value of In-phase minus Water-only by that of In-phase using Dixon MRI.
<ol>
	<li>Change the directory to the folder of In-phase images and select the Volume_In function to generate the in-phase Volume.</li>
	<li>Change the directory to the folder of Water-only images and select the Volume_Water function to generate the water-only Volume.</li>
	<li>Select the FF_Volume function using the two Volumes generated in step 3.1 and step 3.2 as inputs to obtain the FF-Volume of the abdominal MRI, visualized as shown in Figure 4.</li>
	<li>The Fat Fraction (FF) map shown in Figure 4 is presented as an RGB true-color image with a data structure of 520 by 704 by 44 matrix. In this representation, each pixel is composed of three values, representing the red, green, and blue channels. Take note of the color bar on the left side, which provides a visual reference for the fat fraction values associated with different colored pixels. 
	&#8203;NOTE: The GUI shown in Figure 4 allows closer observation of the patient&#39;s liver through the zooming tool in the top right corner and obtaining the exact FF values using the probing tool. By dragging the scroll bar at the bottom, users can observe the FF-maps of all scanning positions in the horizontal plane.</li>
</ol>
4. 3D-volume of liver fat fraction distribution
NOTE: Figure 4 shows the LFF map calculated based on the Dixon MRI images of the upper abdomen. Combined with the 3D liver region in Figure 3, the 3D-LFF volume of the entire liver can be computed separately.
<ol>
	<li>Utilize the LFF_Volume function, providing it with the 3D liver region (as depicted in Figure 3) and the Liver stiffness map (shown in Figure 4) as input parameters. This will result in the creation of the 3D-Volume representation of the liver fat fraction, as illustrated in Figure 5.</li>
	<li>To examine the stiffness map for each liver layer, simply use the scroll bar located beneath the GUI displayed in Figure 5. 
	NOTE: In this view, only liver tissue is accurately depicted.</li>
	<li>Pay attention to the icons located in the upper right corner of the GUI (as shown in Figure 5). These icons provide functionalities for zooming out, zooming in, returning to the global view, and marking the coordinates of the selected pixel. 
	NOTE: The default color bar utilizes the &#34;jet&#34; colormap, where colors transition from blue to red to represent varying values (measured in units of kPa) from low to high.</li>
	<li>Run the LFF_Distribution function using the identical input parameters as &#34;LFF_Volume&#34; to produce the spatial distribution of the 3D Liver Fat Fraction (3D-LFF), as depicted in Figure 6.</li>
</ol>
5. 3D-LFF quantitative analysis
NOTE: Normal liver voxels: LFF &#60; 5%; Mild fatty liver voxels: 5%-10%; Moderate fatty liver voxels: 10%-20%; Severe fatty liver voxels: LFF &#8805; 20%11,12,13,14,15. A key quantitative focus of this study is determining the proportion of voxels at different LFF stages in the patient&#39;s liver. Figure 6 demonstrates the uneven spatial distribution of liver fat fraction in the patient. The lack of distinct clinical symptoms is primarily attributed to a substantial proportion of normal liver tissue. Therefore, precise quantification of differences between patients and healthy individuals is imperative. This represents a vital quantitative concept herein.
<ol>
	<li>Calculate the 3D-LFF distribution for a healthy liver by repeating steps 1-4. 
	NOTE: The built-in MATLAB bar function enables the comparison of the 3D-LFF distributions for healthy and fatty livers (Figure 7).</li>
</ol>

A Comprehensive 3D-Liver Fat Function Distribution Model Using Dixon MRI

<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>

The software tool for hepatic steatosis quantification, listed in the Table of Materials of this study as HepaticSteatosis V1.0, is a product of Beijing Intelligent Entropy Science &amp; Technology Co., Ltd. The intellectual property rights of this software tool belong to the company.

This research presents an innovative 3D quantification technique for analyzing the distribution of liver fat fraction (LFF) using Dixon MRI9,10. By integrating LFF maps, which are generated from in-phase and water-phase images, with 3D liver contours, this method distinguishes between LFF patterns in normal and steatotic livers6. Consequently, it facilitates a precise evaluation of liver fat content.
Step 3 repr...

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.

<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

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.

Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI 

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

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.

This investigation utilizes actual patient datasets acquired using a commercially available MRI scanner to validate the 3D liver fat fraction quantification methodology (Figure 1). The MRI protocol included Dixon&#39;s four-phase imaging9,10: In-phase, Out-of-phase, Water-only, and Fat-only (Figure 2). The fat fraction (FF) of each voxel is computed by dividing the In-phase minus Water-only voxel signal ...

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

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. ...

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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.

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.

847 Views.  Beijing University of Chinese Medicine.  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.

A Comprehensive 3D-Liver Fat Function Distribution Model Using Dixon MRI

Summary