This publication was supported by the fifth national traditional Chinese medicine clinical excellent talents research program 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. &#39;

This study utilized 3D-digital LSD modeling to reconstruct the liver of a typical patient with clinically confirmed hepatic fibrosis. The patient was recruited from a well-known liver disease treatment institution, &#34;You An Hospital&#34; in Beijing, China, and underwent routine upper abdominal magnetic resonance imaging (MRI) and MRE imaging after providing consent. The patient was chosen as the case study for this research method due to the confirmation of hepatic fibrosis staging through pathological examination and the absence of obvious clinical symptoms, which emphasizes the applicability and clinical value of this research in diagnosing early-stage hepatic fibrosis patients. This paper also provides a quantitative comparison between the liver of this patient and a healthy liver. The software tools used in this study are listed in the Table of Materials.
1. Data collection and preparation
NOTE: The parameter difference is not sensitive to the research method.
<ol>
	<li>MRI scanning strategies 
	NOTE: This study utilized actual DICOM data obtained from clinical imaging using a magnetic resonance imaging (MRI) device manufactured by GE. The content of the data includes IDEAL (Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation) water-fat separation imaging and magnetic resonance elastography (MRE) imaging.
	<ol>
		<li>Ensure that the IDEAL data have a horizontal resolution of 256 pixels by 256 pixels, with a pixel spacing of 1.5625 mm and a slice thickness of 10 mm. 
		NOTE: The scanning strategy could be further optimized, but the methodology employed in this study is applicable to higher-precision medical imaging.</li>
	</ol>
	</li>
	<li>Rename the folder of every sequence. 
	NOTE: As the DICOM data exported from the equipment does not explicitly provide sequence names, during the preprocessing stage, it is necessary to add explicit names for each sequence to facilitate subsequent analysis and processing.
	<ol>
		<li>Copy all DICOM data to a customized working directory.</li>
		<li>Navigate to the directory containing the data in MATLAB&#39;s working directory.</li>
		<li>Execute the Description_Name function to add descriptive names to the folders for each sequence.</li>
		<li>See Figure 1 for a comparison before and after renaming. Add a Description Name to each image sequence folder to facilitate the identification of the necessary image sequences for various analytical purposes.</li>
	</ol>
	</li>
	<li>Quickly check images of IDEAL.
	<ol>
		<li>Change the directory of different phases&#39; folders, including the in-phase, out-phase, water, and fat phases, which were stored in separate folders for imaging using IDEAL.</li>
		<li>Execute the Slice_View function to view the impact sequences for each phase.</li>
		<li>See Figure 2 for an image of the interactive graphic user interface (GUI) for the MRI-IDEAL sequence. Use the scroll bar at the bottom of the GUI to quickly browse through the different sequences.</li>
		<li>Use the MRI-IDEAL out-phase sequence as the type of MRI sequence for providing clearer descriptions of liver tissue boundaries. 
		&#8203;NOTE: In the following operations, the focus will be on using IDEAL&#39;s out-phase sequence to delineate the 3D region of the liver.</li>
	</ol>
	</li>
</ol>
2. Extract the 3D region of the liver
NOTE: The individual voxels in the 3D region of the liver serve as spatial carriers for LSD, with the stiffness value of each voxel being derived from MRE. Extracting the 3D region of the liver tissue is a necessary step before fusion. While deep learning can be used to accomplish this task more efficiently, it is not the focus of this study. Therefore, mature software tools (e.g., MIMICS) are still used here to extract the 3D region of the liver tissue.
<ol>
	<li>To initiate the MIMICS software, select New Project and in the ensuing dialog box, navigate to the folder containing the IDEAL out-phase images. Proceed by clicking on NEXT | the Convert button, thereby gaining entry into the sequence-editing state.</li>
	<li>To create an empty Mask, click on the New button in the MASK dialog box located on the right-hand side and select the maximum threshold.</li>
	<li>To delimit the area of the liver in all horizontal views, utilize the Edit Masks tool located beneath the Segment label.</li>
	<li>To generate the 3D spatial part of the liver, select the liver mask that has been delineated and click on the Calculate Part from Mask button. The extracted 3D region of the liver is shown in Figure 3.</li>
	<li>Click on File | Export | select the Dicom command. In the popup dialog box, choose the liver mask, set the file path and files&#39; names, and click the OK button to complete the export of the 3D region of the liver to the specified DICOM files.</li>
</ol>
3. The Liver stiffness map sequence
NOTE: The MRE stiffness range in patients with early fibrosis is typically below 8 kpa. To view this, the sequence image labeled &#39;SE27_ST8K_(Pa)&#39; should be selected.
<ol>
	<li>Change the directory to the folder of &#39;SE27_ST8K_(Pa)&#39;, which contains the liver stiffness map sequence.</li>
	<li>To browse through each stiffness map, execute the MRE_show function in Matlab&#39;s workspace, with the function&#39;s argument being the filename located in the specified path.</li>
	<li>The liver stiffness map shown in Figure 4 is an RGB true-color image, with a data structure of 512 pixels by 512 pixels by 3 matrix, where each pixel point has three values representing the three primary colors, RBG. Observe the color bar on the left that displays the corresponding stiffness values of different colored pixels. Calculate the exact stiffness of each pixel by using their respective correlations.</li>
	<li>The supplementary information in Figure 4 includes data such as sequence description, scan position, time, patient information, and image parameters. Use these data, particularly the image parameters, to establish the spatial relationship between MRE and IDEAL sequences.</li>
</ol>
4. 3D-Volume of liver stiffness distribution
NOTE: Each voxel in the 3D liver stiffness volume represents the stiffness value of a corresponding voxel in the 3D liver region, which is derived from the stiffness value of each pixel in Figure 4. By aligning the 3D liver region in Figure 3 with the stiffness map in Figure 4, the stiffness value of each voxel can be extracted, resulting in the generation of the 3D liver stiffness volume.
<ol>
	<li>Invoke the LSD_Slice function with the 3D liver region shown in Figure 3 and the Liver stiffness map in Figure 4 as input parameters to obtain the 3D-Volume of liver stiffness distribution, as shown in Figure 5.</li>
	<li>View the stiffness map of each layer of the liver by dragging the scroll bar below the GUI shown in Figure 5. 
	NOTE: However, unlike Figure 4, only liver tissue is accurately retained here.</li>
	<li>Observe the icons in the upper right corner of the GUI (Figure 5) such as zooming in, zooming out, returning to the global view, and marking the coordinates of the pixel selected. 
	NOTE: The default color bar is the colormap of &#34;jet&#34; which means that the corresponding values (Unit kpa) from blue to red are low to high.</li>
	<li>Execute the LSD_Volume function with the same input as LSD_Slice to obtain the spatial distribution of the 3D liver LSD, as shown in Figure 6. View the 3D-volume of LSD from any perspective by holding down the left mouse button and dragging the screen (Figure 6).</li>
</ol>
5. LSD quantitative analysis
NOTE: An important quantitative analysis focus of this study is to provide the proportion of different stages of LSD voxels in the patient&#39;s liver. Figure 6 shows that the distribution of liver fibrosis in patients is uneven in different spatial locations. The reason why clinical symptoms are not yet obvious is mainly due to a considerable proportion of liver tissue being in a normal stage. Therefore, it is necessary to quantify precisely the difference between patients and healthy individuals. This is an important quantitative concept of this study.
<ol>
	<li>Determine the numerical ranges of stiffness values for different stages of hepatic fibrosis, as shown in Figure 7.</li>
	<li>Calculate the distribution of the patient&#39;s entire liver voxels in different fibrosis stages (Figure 8) by invoking the Hepatic_Fibrosis function with the input parameter of the 3D-volume of LSD shown in Figure 6.</li>
	<li>Use the same steps to calculate and compare the results of a completely healthy liver with the typical liver fibrosis patient described above (Figure 9).</li>
</ol>

Quantitative Analysis of Liver Stiffness Distribution

<ol>
	<li>Henderson, N. C., Rieder, F., Wynn, T. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Fibrosis:+from+mechanisms+to+medicines.">Fibrosis: from mechanisms to medicines.</a> Nature. 587 (7835), 555-566 (2020).</li><li>Parola, M., Pinzani, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Liver+fibrosis:+Pathophysiology,+pathogenetic+targets+and+clinical+issues.">Liver fibrosis: Pathophysiology, pathogenetic targets and clinical issues.</a> Molecular Aspects of Medicine. 65, 37-55 (2019).</li><li>Ramachandran, P., 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=Resolving+the+fibrotic+niche+of+human+liver+cirrhosis+at+single-cell+level.">Resolving the fibrotic niche of human liver cirrhosis at single-cell level.</a> Nature. 575 (7783), 512-518 (2019).</li><li>Stefan, N., H&#228;ring, H. -. U., Cusi, K. <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:+causes,+diagnosis,+cardiometabolic+consequences,+and+treatment+strategies.">Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies.</a> The Lancet. Diabetes &amp; Endocrinology. 7 (4), 313-324 (2019).</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.e4-1281.e4 (2019).</li><li>Godoy-Matos, A. F., Silva J&#250;nior, W. S., Valerio, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NAFLD+as+a+continuum:+from+obesity+to+metabolic+syndrome+and+diabetes.">NAFLD as a continuum: from obesity to metabolic syndrome and diabetes.</a> Diabetology &amp; Metabolic Syndrome. 12 (1), 1-20 (2020).</li><li>Venkatesh, S. K., Xu, S., Tai, D., Yu, H., Wee, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Correlation+of+MR+elastography+with+morphometric+quantification+of+liver+fibrosis+(Fibro-C-Index)+in+chronic+hepatitis+B.">Correlation of MR elastography with morphometric quantification of liver fibrosis (Fibro-C-Index) in chronic hepatitis B.</a> Magnetic Resonance in Medicine. 72 (4), 1123-1129 (2014).</li><li>Yin, 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=Assessment+of+hepatic+fibrosis+with+magnetic+resonance+elastography.">Assessment of hepatic fibrosis with magnetic resonance elastography.</a> Clinical Gastroenterology and Hepatology. 5 (10), 1207-1213 (2007).</li><li>Venkatesh, S. K., Wang, G., Lim, S. 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The software tool for hepatic fibrosis quantification listed in the Table of Materials of this study, HepaticFibrosis V1.0, is a software tool from Beijing Intelligent Entropy Science &amp; Technology Co Ltd. The intellectual property rights of this software tool belong to the company.

In clinical practice, it is challenging to accurately quantify and track the condition of early-stage hepatic fibrosis patients. The comparison shown in Figure 9 fully reflects the degree of hepatic fibrosis in the patient compared to a healthy liver; of course, this figure can also be a comparison between two different periods for the patient, used for evaluation of treatment efficacy. This precise quantification method is the core critical step of this study. Furthermore, the calculation m...

Hepatic fibrosis refers to the formation of excessive scar tissue in the liver, usually as a result of liver damage or disease1,2,3,4. It frequently arises as a consequence of chronic liver injury and is commonly associated with liver diseases, such as chronic viral hepatitis, non-alcoholic fatty liver disease, and alcoholic liver disease. If left untreated, hepatic fibrosis can progress to cirrhosis, a potentially life-threatening condition associated with significant morbidity and mortality.
Active research in this area aims to elucidate the cellular and molecular mechanisms underlying the pathogenesis of hepatic fibrosis, as well as to develop novel diagnostic and therapeutic strategies to improve patient outcomes. Another objective is the noninvasive detection of the hepatic fibrosis stage, which is a critical aspect that directly correlates with disease diagnosis, treatment selection, and prognosis evaluation. Despite the importance of accurate diagnosis and the monitoring of hepatic fibrosis, traditional diagnostic methods, such as liver biopsy, are invasive and associated with significant risks. In contrast, magnetic resonance elastography5,6 (MRE) is a promising non-invasive imaging technique that has demonstrated potential in the diagnosis and monitoring of hepatic fibrosis by quantifying liver stiffness.
In recent years, there has been significant research focused on evaluating the accuracy and reliability of MRE in the diagnosis of hepatic fibrosis, as well as its potential advantages over traditional diagnostic methods. The liver stiffness metric of MRE has been granted approval by the United States Food and Drug Administration (FDA) for clinical diagnosis, and extensive comparative analysis with pathological results has been conducted in clinical practice. The results have shown that the stiffness maps generated by MRE exhibit a strong positive correlation with various stages of liver fibrosis7,8,9,10,11,12. Yet so far, the work of accurately evaluating and tracking the progression of liver fibrosis in patients through quantitative analysis of liver stiffness distribution (LSD) by matching liver structure images with MRE has not made much progress.
In this study, the medical imaging group analysis technique13,14,15 is introduced to achieve accurate alignment of the liver structure images with the stiffness map generated by MRE in 3D space, enabling the calculation of liver stiffness values for each voxel of the entire liver. Based on the 3D-digital model of LSD, the exact distribution of patient-specific liver fibrosis staging can be calculated and evaluated. This lays a solid foundation for the precise quantitative diagnosis of early-stage liver fibrosis.

<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_LSD</td>
			<td>Intelligent&#160;Entropy</td>
			<td>HepaticFibrosis V1.0</td>
			<td>Beijing Intelligent Entropy Science &#38; Technology Co Ltd. 
			Modeling for CT/MRI fusion</td>
		</tr>
	</tbody>
</table>

a three-dimensional digital model for early diagnosis of hepatic fibrosis based on magnetic resonance elastography

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the disease. Despite the good progress made with the liver stiffness map (LSM) based on magnetic resonance elastography (MRE), there are still some limitations that need to be overcome, including manual focus determination, manual selection of regions of interest (ROIs), and discontinuous LSM data without structural information, which makes it impossible to evaluate the liver as a whole. In this study, we propose a novel three-dimensional (3D) digital model for the early diagnosis of hepatic fibrosis based on MRE.
MRE is a non-invasive imaging technique that employs magnetic resonance imaging (MRI) to measure the liver stiffness at the scanning site through human-computer interaction. Studies have indicated a significant positive correlation between the LSM obtained through MRE and the degree of hepatic fibrosis. However, for clinical purposes, a comprehensive and precise quantification of the degree of hepatic fibrosis is necessary. To address this, the concept of Liver Stiffness Distribution (LSD) was proposed in this study, which refers to the 3D stiffness volume of each liver voxel obtained by the alignment of 3D liver tissue images and MRE indicators. This provides a more effective clinical tool for the diagnosis and treatment of hepatic fibrosis.

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI 

A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography

The scope of the research is to develop a three dimensional digital model for the early diagnosis of hepatic fibrosis based on magnetic resonance elastography. The research aims to address the limitations of current methods and provide a noninvasive and quantitative approach for the accurate assessment and monitoring of hepatic fibrosis. Some of the current experimental challenges in the field of hepatic fibrosis research include improving the scanning accuracy and imaging techniques of non-invasive methods like magnetic resonance elastography, standardizing computational protocols for consistent results and accumulating more comprehensive, and that was data size for accurate classification and diagnosis.The liver MRI structural images were accurately aligned with MRE images, enabling the three dimensional distribution of liver stiffness, distribution, and achieving precise quantification in 3D space. It is possible to accurately establish a liver's stiffness distribution map for a healthy liver, and conveniently utilize it for the diagnosis and the treatment of patients with different stages of liver cirrhosis. In the future, we want to address AI-driven diagnosis, treatment, and prognosis of liver diseases, as well as the development of herbal medicines related to liver diseases.

By utilizing the information in the Description_Name field of DICOM files, the original MRI folder can be renamed to facilitate the rapid localization of the required imaging sequence during the analysis process in the imaging group.&#160;The MRI-IDEAL out-phase sequence is the type of MRI sequence used for providing clearer descriptions of liver tissue boundaries. This is because the MRI-IDEAL out-of-phase sequence can better differentiate the magnetization strength and angle of different tissues throug...

Watch this Scientific Journal Video about Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI at JoVE.com

The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.

Hepatic fibrosis is an early stage of liver cirrhosis, and there are no better non-invasive and convenient methods for the detection and evaluation of the ...

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Extraction of 3D Region of the Liver for Hepatic Fibrosis Diagnosis

To begin, open MATLAB software. Ensure that the IDEAL data have a horizontal resolution of 256 by 256 pixels with a pixel spacing of 1.5625 millimeters and a slice thickness of 10 millimeters. Copy all DICOM data to a customized working directory, and navigate to the directory containing the data in MATLAB's working directory.Then execute the description name function to add descriptive names to the folders for each sequence. Add a description name to each image sequence folder. To check images of IDEAL, change the directory of different phase folders.Execute the slice view function to view the impact sequences for each phase. Next, quickly browse the different sequences using the scroll bar at the bottom of the GUI. Select the MRI IDEAL outphase sequence to depict liver tissue boundaries.Initiate the Mimics software. Select new project, and in the ensuing dialogue box, navigate to the folder containing the IDEAL outphase images. Click next, followed by the convert button to enter into the sequence heading state.To create an empty mask, from the mask dialogue box, click on the new button and select the maximum threshold. Using the edit masks tool located beneath the segment label, delimit the area of the liver in all horizontal views. To generate the 3D spatial part of the liver, select the outlying liver mask and click on the calculate part for mask button.Next, click file, then export, and select the DICOM command. In the pop-up dialogue box, choose the liver mask, and set the file path and names, then click the OK button to complete the export of the 3D region of the liver to the specified DICOM files.

Calculating the Distribution Ratio of the Patient's Liver at Different Fibrosis Stages

Calculating the Distribution Ratio of the Patient&#039;s Liver at Different Fibrosis Stages  

Begin by changing the directory to the folder SE27_ST8K_, Pa, which contains the liver stiffness map sequence. To browse through each stiffness map, execute the MRE_show function, with the function's argument being the file name located in the specified path. Observe the liver stiffness map in an RGB true color image, where each pixel point has three values representing the three primary colors.Calculate the exact stiffness for each pixel using their respective correlations. Then, establish the spatial relationship between MRE and ideal sequences. To obtain the 3D volume of liver stiffness distribution, invoke the LSD_Slice function with the 3D liver region and the liver stiffness map as input parameters.Then, drag the scroll bar below the GUI to view the stiffness map of each layer of the liver. Next, execute the LSD_Volume function with the same input as LSD_Slice to obtain the spatial distribution of the 3D liver LSD. View the 3D volume of LSD from any perspective by holding down the left mouse button.After determining the numerical ranges of stiffness values from four different stages of hepatic fibrosis, using the Hepatic_Fibrosis function with the 3D volume of LSD as the input parameter, calculate the distribution of the patient's entire liver voxels across different fibrosis stages. Then, calculate and compare the results between a completely healthy liver and the liver of a typical fibrosis patient. Quantitative results of the patient's hepatic fibrosis indicate the proportion of the patient's liver in different stages of hepatic fibrosis.Comparison of LSD fully reflects the degree of hepatic fibrosis in the patient compared to a healthy liver.

795 Views.  Capital Medical University.  The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.

Quantitative Analysis of Liver Stiffness Distribution

Summary