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><tbody><tr><td>MATLAB</td><td>MathWorks&amp;nbsp;</td><td>2022B</td><td>Computing and visualization&amp;nbsp;</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 &amp;amp; Technology Co Ltd.&lt;br/&gt; 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.

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

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

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 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. ...

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1.2K Views.  Beijing University of Chinese Medicine. Deze studie ontwikkelde een 3D Dixon MRI-techniek om de verdeling van levervet nauwkeurig te kwantificeren, met als doel de werkzaamheid ervan te valideren als een nauwkeurig, niet-invasief hulpmiddel voor het beoordelen en differentiëren van vetpatronen in normale versus steatotische levers. Recente ontwikkelingen op het gebied van kwantificering van MRI-vet omvatten normale 3D-distributiemodelleringsbenaderingen, waarbij Dixon Imaging wordt geïntegreerd met segmentatie van de leverstructu...

Video: Auteur in de schijnwerpers: een niet-invasief hulpmiddel om vetpatronen in de lever te beoordelen en te differentiëren met behulp van 3D Dixon MRI 

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking 

Purpose: An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF. Current technologies for CMR are time consuming and difficult to implement in clinical practice. Feature tracking is a technology that can lead to more automization and robustness of quantitative analysis of medical images with less time consumption than comparable methods.
Methods: An automatic or manual input in a single phase serves as an initialization from which the system starts to track the displacement of individual patterns representing anatomical structures over time. The specialty of this method is that the images do not need to be manipulated in any way beforehand like e.g. tagging of CMR images.
Results: The method is very well suited for tracking muscular tissue and with this allowing quantitative elaboration of myocardium and also blood flow.
Conclusions: This new method offers a robust and time saving procedure to quantify myocardial tissue and blood with displacement, velocity and deformation parameters on regular sequences of CMR imaging. It therefore can be implemented in clinical practice.

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that strain is a more sensitive and earlier marker for contractile dysfunction than the frequently used parameter EF.

Purpose: An accurate and practical method to measure parameters like strain in myocardial tissue is of great clinical value, since it has been shown, that ...

Magnetic Resonance Imaging Quantification of Pulmonary Perfusion using Calibrated Arterial Spin Labeling

This demonstrates a MR imaging method to measure the spatial distribution of pulmonary blood flow in healthy subjects 
during normoxia (inspired O2, fraction (FIO2) = 0.21) hypoxia (FIO2 = 0.125), and hyperoxia 
(FIO2 = 1.00). In addition, the physiological responses of the subject are monitored in the MR scan environment. MR images 
were obtained on a 1.5 T GE MRI scanner during a breath hold from a sagittal slice in the right lung at functional residual capacity. An arterial 
spin labeling sequence (ASL-FAIRER) was used to measure the spatial distribution of pulmonary blood flow 1,2 and a multi-echo fast 
gradient echo (mGRE) sequence 3 was used to quantify the regional proton (i.e. H2O) density, allowing the quantification 
of density-normalized perfusion for each voxel (milliliters blood per minute per gram lung tissue). 
With a pneumatic switching valve and facemask equipped with a 2-way non-rebreathing valve, different oxygen concentrations 
were introduced to the subject in the MR scanner through the inspired gas tubing. A metabolic cart collected expiratory gas via expiratory tubing. Mixed expiratory O2 and CO2 concentrations, oxygen consumption, carbon dioxide production, respiratory exchange ratio, 
respiratory frequency and tidal volume were measured. Heart rate and oxygen saturation were monitored using pulse-oximetry. 
Data obtained from a normal subject showed that, as expected, heart rate was higher in hypoxia (60 bpm) than during normoxia (51) or hyperoxia (50) and the arterial oxygen saturation (SpO2) was reduced during hypoxia to 86%. Mean ventilation was 8.31 L/min BTPS during hypoxia, 7.04 L/min during normoxia, and 6.64 L/min during hyperoxia. Tidal volume was 0.76 L during hypoxia, 0.69 L during normoxia, and 0.67 L during hyperoxia. 
Representative quantified ASL data showed that the mean density normalized perfusion was 8.86 ml/min/g during hypoxia, 8.26 ml/min/g during normoxia and 8.46 ml/min/g during hyperoxia, respectively. In this subject, the relative dispersion4, an index of global heterogeneity, was increased in hypoxia (1.07 during hypoxia, 0.85 during normoxia, and 0.87 during hyperoxia) while the fractal dimension (Ds), another index of heterogeneity reflecting vascular branching structure, was unchanged (1.24 during hypoxia, 1.26 during normoxia, and 1.26 during hyperoxia). 
Overview. This protocol will demonstrate the acquisition of data to measure the distribution of pulmonary perfusion noninvasively under conditions of normoxia, hypoxia, and hyperoxia using a magnetic resonance imaging technique known as arterial spin labeling (ASL). 
Rationale: Measurement of pulmonary blood flow and lung proton density using MR technique offers high spatial resolution images which can be quantified and the ability to perform repeated measurements under several different physiological conditions. In human studies, PET, SPECT, and CT are commonly used as the alternative techniques. However, these techniques involve exposure to ionizing radiation, and thus are not suitable for repeated measurements in human subjects.

A MR imaging method to study the distribution of pulmonary blood flow under a variety of physiological conditions, in this case exposure to three different inspired oxygen concentrations: hypoxia, normoxia, and hyperoxia, is described. This technique utilizes human pulmonary physiology research techniques in an MR scanning environment.

This demonstrates a MR imaging method to measure the spatial distribution of pulmonary blood flow in healthy subjects 
during normoxia (inspired O2, ...

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. 	
DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.	
In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.

Diffusion tensor imaging (DTI) basically serves as an MRI-based tool to identify in vivo the microstructure of the brain and pathological processes due to neurological disorders within the cerebral white matter. DTI-based analyses allow for application to brain diseases both at the group level and in single subject data.

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present ...

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative magnetic resonance imaging (qMRI) describes the development and use of MRI to quantify physical, chemical, and/or biological properties of living systems. Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using qMRI methods. The MRI acquisition protocol begins with localizer images (used to locate the position of the body and tissue of interest within the MRI system), quality control measurements of relevant magnetic field distributions, and structural imaging for general anatomical characterization. The qMRI portion of the protocol includes measurements of the longitudinal and transverse relaxation time constants (T1 and T2, respectively). Also acquired are diffusion-tensor MRI data, in which water diffusivity is measured and used to infer pathological processes such as edema. Quantitative magnetization transfer imaging is used to characterize the relative tissue content of macromolecular and free water protons. Lastly, fat-water MRI methods are used to characterize fibro-adipose tissue replacement of muscle. In addition to describing the data acquisition and analysis procedures, this paper also discusses the potential problems associated with these methods, the analysis and interpretation of the data, MRI safety, and strategies for artifact reduction and protocol optimization.

Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using non-invasive magnetic resonance imaging methods.

Quantitative magnetic resonance imaging (qMRI) describes the development and use of MRI to quantify physical, chemical, and/or biological properties of ...

Assessing Hepatic Steatosis with 3D Fluorescence Lipid Droplet Reconstruction

Analysis of Fluorescent-Stained Lipid Droplets with 3D Reconstruction for Hepatic Steatosis Assessment

Lipid droplets (LDs) are specialized organelles that mediate lipid storage and play a very important role in suppressing lipotoxicity and preventing dysfunction caused by free fatty acids (FAs). The liver, given its critical role in the body's fat metabolism, is persistently threatened by the intracellular accumulation of LDs in the form of both microvesicular and macrovesicular hepatic steatosis. The histologic characterization of LDs is typically based on lipid-soluble diazo dyes, such as Oil Red O (ORO) staining, but a number of disadvantages consistently hamper the use of this analysis with liver specimens. More recently, lipophilic fluorophores 493/503 have become popular for visualizing and locating LDs due to their rapid uptake and accumulation into the neutral lipid droplet core. Even though most applications are well-described in cell cultures, there is less evidence demonstrating the reliable use of lipophilic fluorophore probes as an LD imaging tool in tissue samples. Herein, we propose an optimized boron dipyrromethene (BODIPY) 493/503-based protocol for the evaluation of LDs in liver specimens from an animal model of high-fat diet (HFD)-induced hepatic steatosis. This protocol covers liver sample preparation, tissue sectioning, BODIPY 493/503 staining, image acquisition, and data analysis. We demonstrate an increased number, intensity, area ratio, and diameter of hepatic LDs upon HFD feeding. Using orthogonal projections and 3D reconstructions, it was possible to observe the full content of neutral lipids in the LD core, which appeared as nearly spherical droplets. Moreover, with the fluorophore BODIPY 493/503, we were able to distinguish microvesicles (1 &#181;m &lt; d &#8804; 3 &#181;m), intermediate vesicles (3 &#181;m &lt; d &#8804; 9 &#181;m), and macrovesicles (d &gt; 9 &#181;m), allowing the successful discrimination of microvesicular and macrovesicular steatosis. Overall, this BODIPY 493/503 fluorescence-based protocol is a reliable and simple tool for hepatic LD characterization and may represent a complementary approach to the classical histological protocols.

Author Spotlight: Analysis of Fluorescent-Stained Lipid Droplets with 3D Reconstruction for Hepatic Steatosis Assessment  

Herein, we demonstrate an optimized BODIPY 493/503 fluorescence-based protocol for lipid droplet characterization in liver tissue. Through the use of orthogonal projections and 3D reconstructions, the fluorophore allows for successful discrimination between microvesicular and macrovesicular steatosis and may represent a complementary approach to the classical histological protocols for hepatic steatosis assessment.

Lipid droplets (LDs) are specialized organelles that mediate lipid storage and play a very important role in suppressing lipotoxicity and preventing ...

Biochemistry

Using Multi-fluorinated Bile Acids and In Vivo Magnetic Resonance Imaging to Measure Bile Acid Transport

Along with their traditional role as detergents that facilitate fat absorption, emerging literature indicates that bile acids are potent signaling molecules that affect multiple organs; they modulate gut motility and hormone production, and alter vascular tone, glucose metabolism, lipid metabolism, and energy utilization. Changes in fecal bile acids may alter the gut microbiome and promote colon pathology including cholerrheic diarrhea and colon cancer. Key regulators of fecal bile acid composition are the small intestinal Apical Sodium-dependent Bile Acid Transporter (ASBT) and fibroblast growth factor-19 (FGF19). Reduced expression and function of ASBT decreases intestinal bile acid up-take. Moreover, in vitro data suggest that some FDA-approved drugs inhibit ASBT function. Deficient FGF19 release increases hepatic bile acid synthesis and release into the intestines to levels that overwhelm ASBT. Either ASBT dysfunction or FGF19 deficiency increases fecal bile acids and may cause chronic diarrhea and promote colon neoplasia. Regrettably, tools to measure bile acid malabsorption and the actions of drugs on bile acid transport in vivo are limited. To understand the complex actions of bile acids, techniques are required that permit simultaneous monitoring of bile acids in the gut and metabolic tissues. This led us to conceive an innovative method to measure bile acid transport in live animals using a combination of proton (1H) and fluorine (19F) magnetic resonance imaging (MRI). Novel tracers for fluorine (19F)-based live animal MRI were created and tested, both in vitro and in vivo. Strengths of this approach include the lack of exposure to ionizing radiation and translational potential for clinical research and practice.

Using Multi-fluorinated Bile Acids and In Vivo Magnetic Resonance Imaging to Measure Bile Acid Transport

Tools to diagnose bile acid malabsorption and measure bile acid transport in vivo are limited. An innovative approach in live animals is described that utilizes combined proton (1H) plus fluorine (19F) magnetic resonance imaging; this novel methodology has translational potential to screen for bile acid malabsorption in clinical practice.

Along with their traditional role as detergents that facilitate fat absorption, emerging literature indicates that bile acids are potent signaling ...

Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol

As new techniques are developed to image adipose tissue, methods to validate such protocols are becoming increasingly important. Phantoms, experimental replicas of a tissue or organ of interest, provide a low cost, flexible solution. However, without access to expensive and specialized equipment, constructing stable phantoms with high fat fractions (e.g., &#62;50% fat fraction levels such as those seen in brown adipose tissue) can be difficult due to the hydrophobic nature of lipids. This work presents a detailed, low cost protocol for creating 5x 100 mL&#160;phantoms with fat fractions of 0%, 25%, 50%, 75%, and 100% using basic lab supplies (hotplate, beakers, etc.) and easily accessible components (distilled water, agar, water-soluble surfactant, sodium benzoate, gadolinium-diethylenetriaminepentacetate (DTPA) contrast agent, peanut oil, and oil-soluble surfactant). The protocol was designed to be flexible; it can be used to create phantoms with different fat fractions and a wide range of volumes. Phantoms created with this technique were evaluated in the feasibility study that compared the fat fraction values from fat-water magnetic resonance imaging to the target values in the constructed phantoms. This study yielded a concordance correlation coefficient of 0.998 (95% confidence interval: 0.972-1.00). In summary, these studies demonstrate the utility of&#160;fat phantoms for validating adipose tissue imaging techniques across a range of clinically relevant tissues and organs.

The purpose of this work is to describe a protocol for creating a practical fat-water phantom that can be customized to produce phantoms with varying fat percentages and volumes.

As new techniques are developed to image adipose tissue, methods to validate such protocols are becoming increasingly important. Phantoms, experimental ...

DUCT: Double Resin Casting followed by Micro-Computed Tomography for 3D Liver Analysis

The liver is the biggest internal organ in humans and mice, and high auto-fluorescence presents a significant challenge for assessing the three-dimensional (3D) architecture of the organ at the whole-organ level. Liver architecture is characterized by multiple branching lumenized structures, which can be filled with resin, including vascular and biliary trees, establishing a highly stereotyped pattern in the otherwise hepatocyte-rich parenchyma. This protocol describes the pipeline for performing double resin casting micro-computed tomography, or &#34;DUCT&#34;. DUCT entails injecting the portal vein and common bile duct with two different radiopaque synthetic resins, followed by tissue fixation. Quality control by clearing one lobe, or the entire liver, with an optical clearing agent, allows for pre-screening of suitably injected samples. In the second part of the DUCT pipeline, a lobe or the whole liver can be used for micro-computed tomography (microCT) scanning, (semi-)automated segmentation, and 3D rendering of the portal venous and biliary networks. MicroCT results in 3D coordinate data for the two resins allowing for qualitative as well as quantitative analysis of the two systems and their spatial relationship. DUCT can be applied to postnatal and adult mouse liver and can be further extended to other tubular networks, for example, vascular networks and airways in the lungs.

Double resin casting micro-computed tomography, or DUCT, enables visualization, digitalization, and segmentation of two tubular systems simultaneously to facilitate 3D analysis of organ architecture. DUCT combines ex vivo injection of two radiopaque resins followed by micro-computed tomography scanning and segmentation of the tomographic data.

The liver is the biggest internal organ in humans and mice, and high auto-fluorescence presents a significant challenge for assessing the ...

Quantitative Analysis of Liver Stiffness Distribution

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 

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 ...

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a growing global health issue, and the impact of NAFLD is compounded by the current lack of effective treatments. Considerable limiting factors hindering the timely and accurate diagnosis (including grading) and monitoring of NAFLD, as well as the development of potential therapies, are the current inadequacies in the characterization of the hepatic microenvironment structure and the scoring of the disease stage in a spatiotemporal and non-invasive manner. Using a diet-induced NAFLD mouse model, we investigated the use of in vivo micro-computed tomography (CT) imaging techniques as a non-invasive method to assess the progression stages of NAFLD, focusing predominantly on the hepatic vascular network due to its significant involvement in NAFLD-related hepatic dysregulation. This imaging methodology allows for longitudinal analysis of liver steatosis and functional tissue uptake, as well as the evaluation of the relative blood volume, portal vein diameter, and density of the vascular network. Understanding the adaptations of the hepatic vascular network during NAFLD progression and correlating this with other ways of characterizing the disease progression (steatosis, inflammation, fibrosis) using the proposed method can pave the way toward the establishment of new, more efficient, and reproducible approaches for NAFLD research in mice. This protocol is also expected to upgrade the value of preclinical animal models for investigating&#160;the development of novel therapies against disease progression.

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Using a diet-induced non-alcoholic fatty liver disease (NAFLD) mouse model, we describe the use of novel in vivo micro-computed tomography imaging techniques as a non-invasive method to assess the progression stages of NAFLD, focusing predominantly on the hepatic vascular network due to its significant involvement in NAFLD-related hepatic dysregulation.

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

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