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.

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

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

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.

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 ...

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1.0K 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.

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

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Cardiac Magnetic Resonance Imaging at 7 Tesla

CMR at an ultra-high field (magnetic field strength B0&#160;&#8805; 7 Tesla) benefits from the signal-to-noise ratio (SNR) advantage inherent at higher magnetic field strengths and potentially provides improved signal contrast and spatial resolution. While promising results have been achieved, ultra-high field CMR is challenging due to energy deposition constraints and physical phenomena such as transmission field non-uniformities and magnetic field inhomogeneities. In addition, the magneto-hydrodynamic effect renders the synchronization of the data acquisition with the cardiac motion difficult. The challenges are currently addressed by explorations into novel magnetic resonance technology. If all impediments can be overcome, ultra-high field CMR may generate new opportunities for functional CMR, myocardial tissue characterization, microstructure imaging or metabolic imaging. Recognizing this potential, we show that multi-channel radio frequency (RF) coil technology tailored for CMR at 7 Tesla together with higher order B0 shimming and a backup signal for cardiac triggering facilitates high fidelity functional CMR. With the proposed setup, cardiac chamber quantification can be accomplished in examination times similar to those achieved at lower field strengths. To share this experience and to support the dissemination of this expertise, this work describes our setup and protocol tailored for functional CMR at 7 Tesla.

The sensitivity gain inherent to ultrahigh field magnetic resonance holds promise for high spatial resolution imaging of the heart. Here, we describe a protocol customized for functional cardiovascular magnetic resonance (CMR) at 7 Tesla using an advanced multi-channel radio-frequency coil, magnetic field shimming and a triggering concept.

CMR at an ultra-high field (magnetic field strength B0&#160;&#8805; 7 Tesla) benefits from the signal-to-noise ratio (SNR) advantage inherent at higher ...

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Cardiovascular diseases (CVDs) are the leading cause of death throughout the world. The total risk of developing CVD is determined by the combined effect of different cardiovascular risk factors (e.g., diabetes, raised blood pressure, unhealthy diet, tobacco use, stress, etc.) that commonly coexist and act multiplicatively. Most CVDs can be prevented by an early identification of the highest risk factors and an appropriate treatment. The stratification of cardiovascular risk factors involves a wide range of parameters and tests that specialists use in their clinical practice. In addition to cardiovascular (CV) risk stratification, ambulatory blood pressure monitoring (ABPM) also provides relevant information for diagnostic and treatment purposes. This work presents a list of protocols based on the Hydra platform, a web-based system for clinical decision support which incorporates a set of functionalities and services that are required for complete cardiovascular analysis, risk assessment, early diagnosis, treatment and monitoring of patients over time. The program includes tools for inputting and managing comprehensive patient data, organized into different checkups to track the evolution over time. It also has a risk stratification tool to compute a CV risk factor based upon several risk stratification tables of reference. Additionally, the program includes a tool that incorporates ABPM analysis and allows the extraction of valuable information by monitoring blood pressure over a specific period of time. Finally, the reporting service summarizes the most relevant information in a set of reports that aid clinicians in their clinical decision-making process.

This article presents a protocol based on Hydra &#8212; a web-based system for clinical decision support that integrates a full and detailed set of functionalities and services required by physicians for complete cardiovascular analysis, risk assessment, early diagnosis, treatment, and monitoring over time.

Cardiovascular diseases (CVDs) are the leading cause of death throughout the world. The total risk of developing CVD is determined by the combined effect ...

Murine Precision-Cut Liver Slices as an Ex Vivo Model of Liver Biology

Understanding the mechanisms of liver injury, hepatic fibrosis, and cirrhosis that underlie chronic liver diseases (i.e., viral hepatitis, non-alcoholic fatty liver disease, metabolic liver disease, and liver cancer) requires experimental manipulation of animal models and in vitro cell cultures. Both techniques have limitations, such as the requirement of large numbers of animals for in vivo manipulation. However, in vitro cell cultures do not reproduce the structure and function of the multicellular hepatic environment. The use of precision-cut liver slices is a technique in which uniform slices of viable mouse liver are maintained in laboratory tissue culture for experimental manipulation. This technique occupies an experimental niche that exists between animal studies and in vitro cell culture methods. The presented protocol describes a straightforward and reliable method to isolate and culture precision-cut liver slices from mice. As an application of this technique, ex vivo liver slices are treated with bile acids to simulate cholestatic liver injury and ultimately assess the mechanisms of hepatic fibrogenesis.

This protocol provides a simple and reliable method for the production of viable precision-cut liver slices from mice. The ex vivo tissue samples can be maintained under laboratory tissue culture conditions for multiple days, providing a flexible model to examine liver pathobiology.

Understanding the mechanisms of liver injury, hepatic fibrosis, and cirrhosis that underlie chronic liver diseases (i.e., viral hepatitis, non-alcoholic ...

Sample Preparation for Computed Tomography-based Three-dimensional Visualization of Murine Hind-limb Vessels

Blood vessels are complex networks with tree-like structures, and vascular networks are essential for maintaining both circulation and maintaining organ function. Clarifying the mechanism of blood vessel formation is therefore extremely useful for elucidating developmental processes and pathological mechanisms. Murine hind-limb vessels are often used as a model for physiological and pathological angiogenesis. Evaluation is mainly performed via a two-dimensional method using tissue sections. However, methods for evaluating three-dimensional (3D) vascular morphology are particularly limited. This paper introduces a method for visualizing murine hind-limbs using computed tomography (CT). Radiation-opaque resin is injected through the descending aorta, and whole vessels are filled with dye. By adjusting the time of dye injection, arterial-specific filling is also possible, and samples can be obtained with any micro-X-ray CT device. This contrast method provides a basic technique for the 3D evaluation of murine blood vessels in the lower extremities. Furthermore, this method can be used to visualize all blood vessels below the diaphragm and evaluate blood vessels in the abdominal organs.

Here, we describe a visualization and quantification method for murine hind-limb vessels using micro-X-ray computed tomography.

Blood vessels are complex networks with tree-like structures, and vascular networks are essential for maintaining both circulation and maintaining organ ...