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&#160;http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html.

For the present study, ethical clearance was obtained from The Ethics Committee of Dongzhimen Hospital, affiliated with Beijing University of Chinese Medicine (DZMEC-KY-2019.90). In this specific case, a methodical description of the research approach is provided, outlining a case involving a 65-year-old female patient with multiple pulmonary nodules. This patient provided informed consent for her diagnosis through digital modeling and authorized the use of her data for scientific research purposes. The model reconstruction function is derived from a commercially available software tool (see Table of Materials).
1. Data preparation and isovoxel transformation
<ol>
	<li>DICOM (Digital Imaging and Communications in Medicine) data preparation and data properties 
	NOTE: The variation in parameters remains relatively unaffected by the research methodology.
	<ol>
		<li>Copy the patient&#39;s DICOM data to a defined working directory.</li>
		<li>Using the file browser, examine each file directory to identify the image sequence with the highest number of scanning layers for analysis.</li>
		<li>Employ the Dicominfo function within MATLAB by providing DICOM files as input parameters. This will enable you to extract essential parameters, such as slice thickness and pixel spacing, directly within the MATLAB environment. 
		NOTE: These parameters hold significant importance in configuring the display rate for the 3D volume. In the case of the dataset utilized in this study, the slice thickness measured 1 mm, the pixel spacing equated to 0.7188 mm, and a total of 387 layers were scanned.</li>
	</ol>
	</li>
	<li>Correct sorting of scanned data 
	NOTE: The sequence of every image should be sorted for volume construction.
	<ol>
		<li>Retrieve the location data for each image using the Dicominfo function. Access the location information by referencing info.SliceLocation within the MATLAB workspace.</li>
		<li>Save the location data into a variable using the SliceLocation function and generate a plot for it (Figure 1).</li>
		<li>Enhance the plot by adding a data point to it using the Data Tips button situated in the upper-right corner of the GUI. This data point should mark the maximum location of the normal sequence, which corresponds to the topmost location in the patient&#39;s imaging (Figure 1).</li>
		<li>Organize all the images by sorting them and then extract the images ranging from the first location to the maximum location. Achieve this by invoking the VolumeResort function.</li>
		<li>Safeguard the volume data, which consists of 512 pixels by 512 pixels by 340 layers, from the valid images along with their sorted index. This information will be valuable for future reference, particularly in the context of identifying important nodules.</li>
	</ol>
	</li>
	<li>Isovoxel transformation 
	&#8203;NOTE: 3D Isovoxel transformation allows subsequent processing to maintain the same display scale in all dimensions.
	<ol>
		<li>Examine the three-dimensional scale of a 3D volume, which is 512 pixels x 512 pixels x 340 layers, using the size function in Matlab.</li>
		<li>To view the 3D-volume (Figure 2) using the Slice_View command function, record the sequence scan range containing the lungs from 60 to 340. Then, simply use the command V1=V0(:,:,60:340) to obtain a 3D-volume containing all the data of the entire lung. The size of V1 is 512 pixels x 512 pixels x 281 layers.</li>
		<li>Utilize the MATLAB command function dicominfo to obtain the slice thickness of the image sequence, which is 1 mm, and the pixel spacing is 0.7188. Calculate the number of z-axes for the isovoxel transformation with the command: round (281 x 1/0.7188). The number of layers for the isovoxel transformation should be 391.</li>
		<li>Use the Matlab command function imresize3 to perform isovoxel transformation on V1. Execute the script using the command V2=imresize3(V1, [512, 512, 391]). Then use the 3D_Slice_View function to view the isovoxel transformed 3D-Volume (Figure 3).</li>
	</ol>
	</li>
</ol>
2. Removal of noise interference caused by Computed Tomography (CT) equipment
NOTE: In Figure 2, the high-intensity signal representing the CT equipment&#39;s patient couch is visible, which can interfere with image segmentation. To eliminate this interference, a spatial filter design is required.
<ol>
	<li>Utilize the Data Tips button in Figure 2 to add continuous data points within the interactive interface. This will allow one to create a line connecting these points, effectively excluding the patient couch. Next, right-click on the Data Tips and select Export Cursor Data to Workspace to export the reference boundary for spatial filtering to the MATLAB workspace (Figure 3). The boundary scatter matrix in this case is named &#39;CI&#39;.</li>
	<li>Invoke the Noise_Clean function to apply spatial filtering to V2, using the input parameter &#39;CI&#39; from the workspace. This operation will yield a 3D volume that removes the interference signal from the CT equipment. Finally, use the Slice_View command function to visualize the resulting volume, as demonstrated in Figure 4.</li>
</ol>
3. Extraction of lung contour
<ol>
	<li>Begin by selecting a slice to serve as a template within the GUI displayed in Figure 4. For instance, choose the 232nd image for the image segmentation design and assign it to a variable &#39;I&#39; using the command I=V2(:,:,232). Then, open the MATLAB Image Segmenter GUI by executing the command imageSegmenter(I), as depicted in Figure 5.</li>
	<li>Figure 5 showcases an array of image segmentation tools. To start, select the Auto Cluster tool from the toolbar at the top and execute the command by clicking the left mouse button. The image will automatically be divided into two classes. Given the denoising process performed in step 2.2, image segmentation at this stage becomes relatively straightforward.</li>
	<li>Next, click on the Show Binary button in the upper right corner to display the image in black and white binary. At this point, the lung region will appear black. To make the lung region white, select the Invert Mask button from the top toolbar and execute the command by clicking the left mouse button.</li>
	<li>To eliminate the white color outside the lung area, select the Clear Borders button on the top toolbar and execute it by clicking with the left mouse button. After this step, only the white-colored lung area will remain. However, any black shadows remaining within the lung area at this point need to be filled. To achieve this, select the Fill Holes button in the toolbar, and the result after clicking the button is shown in Figure 6.</li>
	<li>All the steps involved in lung image segmentation are presented in the GUI of Figure 6 at the lower left corner. By clicking the Export button in the upper right corner, save these automated steps as a function for batch-processing lung region segmentation. In the pop-up Script Editor, click the Save button to save the function in the current working directory.</li>
</ol>
4. 3D reconstruction for the whole lung with multiple pulmonary nodules
NOTE: Taking the dot product of the lung segmentation image of each image with the original image is equivalent to performing 3D spatial filtering on the volume, effectively filtering out interference signals outside the lungs and obtaining the 3D structure of the lungs.
<ol>
	<li>Initiate the 3Dlung_Volume function within the MATLAB workspace. 
	NOTE: This function conducts image segmentation on each image using the output from step 3.5. It then executes a dot product operation between the binary lung mask and the original image to generate a new 3D-volume exclusively containing lung tissue. In the GUI (Figure 7) that appears after the function completes, one can visualize and perform Maximum Intensity Projection (MIP) operations on the entire 3D lung volume.</li>
	<li>Within the GUI, find the first drop-down menu in the top right corner. Select MIP Projection and then choose the jet colormap from the Built-in Colormaps options below. Next, in the drop-down menu located in the top right corner of the fourth view (3D Volume View), select Maximize. This action will yield a whole lung 3D volume (Figure 8) that can be observed from any angle, moved, and manipulated as needed. 
	NOTE: In the human-computer interaction section illustrated in Figure 8, one can adjust the viewing angle freely by holding down the left mouse button and moving it. Scrolling the middle mouse button allows one to zoom in or out.</li>
	<li>For advanced contrast and color enhancement operations, utilize the control panel on the right side of the GUI.</li>
</ol>
5. Focus on the examination of dominant pulmonary nodules
NOTE: In 3D space (Figure 8), the dominant lesion area among multiple pulmonary nodules becomes distinctly visible. The number, size, and concentration of these nodules are critical features of the dominant lesion, offering valuable insights into disease assessment.
<ol>
	<li>Once more, invoke the Slice_View function, but this time input the entire lung&#39;s 3D volume obtained in step 4.2. Within the resulting GUI (Figure 9), use the bottom scroll bar to navigate to the region where the dominant lung nodules are situated, spanning scans 48 to 70.</li>
	<li>Proceed by calling the 3Dlung_Horizon function to conduct 3D reconstruction of the Region of Interest (ROI) encompassing sections 48 to 70 from the whole lung&#39;s 3D volume. This action will generate a GUI interface tailored for visualizing pulmonary nodules, as illustrated in Figure 10. Within this GUI, one can explore the lesion&#39;s detailed features from various angles.</li>
</ol>

Enhancing Pulmonary Nodule Diagnosis Using Whole-Lung 3D Reconstruction

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The authors have no conflicts of interest to disclose. The software tool for pulmonary nodule model reconstruction, listed in the Table of Materials of this study, is a commercial software from Beijing Intelligent Entropy Science &amp; Technology Co Ltd. The intellectual property rights of this software tool belong to the company.

This research introduces a unique approach for creating a complete three-dimensional (3D) reconstruction of the entire lung, employing advanced medical image processing techniques to delineate the lung&#39;s 3D shape amidst the context of a full chest scan. This technique offers a more precise and thorough depiction of the spatial arrangement and radiological characteristics of early multiple nodules across the entire lung. This study makes a valuable contribution to enhancing the accuracy and efficacy of diagnostic and ...

Early multiple pulmonary nodules, which are small, round growths on the lung, can be benign or malignant1,2,3. Although solitary pulmonary nodules are easier to diagnose and treat, patients with early multiple pulmonary nodules face significant diagnostic and treatment challenges. To develop effective treatment plans, it is essential to accurately identify the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the whole lung4,5. Traditional diagnostic methods have limitations in accurately identifying early multiple pulmonary nodules.
Recent advancements in medical image processing technology and machine learning algorithms have the potential to improve the accuracy and efficiency of early pulmonary nodule detection and diagnosis. Various approaches have been proposed, such as pattern recognition methods based on machine vision and visualization methods based on maximum intensity projection (MIP)6,7,8,9,10. However, these methods suffer from limitations such as false positives, false negatives11,12,13,14,15, and lack of macroscopic and holistic descriptions of the distribution and spatial features of early multiple pulmonary nodules.
To address these limitations, this study proposes a whole-lung 3D reconstruction method that utilizes medical image processing technology to extract the 3D contour of the lung against the background of the whole chest scan. The method then performs 3D reconstruction of the lung, pulmonary artery, and early multiple pulmonary nodules in 3D space. This approach allows for a more comprehensive and accurate representation of the spatial distribution and radiological features of early multiple nodules throughout the whole lung.
The proposed method involves several key steps. Firstly, the medical images are imported into the 3D image processing software, and the lung region is extracted using a threshold-based segmentation technique. Subsequently, the extracted lung region is separated from the surrounding chest wall and the bony structures of the thoracic vertebrae. The early multiple pulmonary nodules and their relationship with surrounding blood vessels are then reconstructed in 3D space using maximum intensity projection (MIP) algorithms. Finally, the reconstructed 3D model of the lung, pulmonary artery, and nodules is displayed for further analysis.
This method has several advantages over existing methods. Unlike traditional methods that rely on 2D images, this method utilizes 3D volume to provide a more accurate and comprehensive representation of early multiple pulmonary nodules. The method also overcomes the limitations of false positives and false negatives associated with pattern recognition methods and MIP visualization methods. Furthermore, this method provides a macroscopic and holistic description of the distribution and spatial features of early multiple pulmonary nodules, which is essential for developing effective treatment plans.
The proposed method has several potential applications in the diagnosis and treatment of early multiple pulmonary nodules. The accurate identification of the spatial distribution and radiological features of early multiple nodules can aid in the early diagnosis and treatment of lung cancer. Furthermore, the method can be used to monitor the progression of the disease and evaluate the effectiveness of treatment plans.
Pattern recognition methods6,7,8 based on machine vision have shown promise in identifying pulmonary nodules, but suffer from limitations such as false positives and false negatives. MIP visualization methods, on the other hand, provide a more accurate representation of individual nodules, but lack a macroscopic and holistic description of the distribution and spatial features of early multiple nodules. The proposed whole-lung 3D reconstruction method overcomes these limitations and provides a more accurate and comprehensive representation of early multiple pulmonary nodules.
Isovoxel transformation16,17refers to the process of converting 3D images with different voxel sizes into 3D images with uniform voxel sizes. In the field of medical image processing, 3D volumes are often composed of voxels with varying sizes, which can lead to computational and visualization issues. The purpose of isovoxel transformation is to address these issues by resampling and interpolating the voxels in the original 3D volume, resulting in a new 3D image with consistent voxel sizes. This technique finds applications in various medical contexts, including image registration, segmentation, and visualization. Thus, this study proposed a whole-lung 3D reconstruction method that utilizes medical image processing technology to extract the 3D contour of the lung against the background of the whole chest scan. The method provides a more accurate and comprehensive representation of the spatial distribution and radiological features of early multiple nodules throughout the whole lung. This study contributes to the development of more accurate and effective diagnostic and treatment strategies for patients with early multiple pulmonary nodules.

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

three-dimensional reconstruction for the whole lung with early multiple pulmonary nodules

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules.
Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.

In the data preprocessing stage, DICOM data sorting should be the first step (Figure 1) to ensure the correct scan sequence for each layer during 3D reconstruction. Next, isotropic transformation is performed to ensure the correct aspect ratio of the 3D volume (Figure 2). Afterward, spatial filtering is applied to the original 3D volume (Figure 3) to eliminate interference signals from the patient couch of the CT equipment (<strong ...

Watch this Scientific Journal Video about Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer at JoVE.com

Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer 

This study introduces a three-dimensional (3D) reconstruction method for the entire lung in patients with early multiple pulmonary nodules. It offers a comprehensive visualization of nodule distribution and their interplay with lung tissue, simplifying the assessment of diagnosis and prognosis for these patients.

Three-Dimensional Reconstruction for the Whole Lung with Early Multiple Pulmonary Nodules

For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, ...

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1.3K Views.  Beijing University of Chinese Medicine. This study introduces a three-dimensional (3D) reconstruction method for the entire lung in patients with early multiple pulmonary nodules. It offers a comprehensive visualization of nodule distribution and their interplay with lung tissue, simplifying the assessment of diagnosis and prognosis for these patients.

Video: Author Spotlight: Advancing 3D Modeling for Enhanced Diagnosis and Treatment of Pulmonary Nodules in Early-Stage Lung Cancer 

Three-dimensional Co-culture Model for Tumor-stromal Interaction

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal cells. The tumor microenvironment is comprised of a variety of cell types, such as fibroblasts, immune cells, vascular endothelial cells, pericytes and bone-marrow-derived cells, embedded in the extracellular matrix (ECM). Cancer-associated fibroblasts (CAFs) have a pro-tumorigenic role through the secretion of soluble factors, angiogenesis and ECM remodeling. The experimental models for cancer cell survival, proliferation, migration, and invasion have mostly relied on two-dimensional monocellular and monolayer tissue cultures or Boyden chamber assays. However, these experiments do not precisely reflect the physiological or pathological conditions in a diseased organ. To gain a better understanding of tumor stromal or tumor matrix interactions, multicellular and three-dimensional cultures provide more powerful tools for investigating intercellular communication and ECM-dependent modulation of cancer cell behavior. As a platform for this type of study, we present an experimental model in which cancer cells are cultured on collagen gels embedded with primary cultures of CAFs.

Here we present a protocol to co-culture in three-dimensions, which is useful for investigating multicellular interactions and extracellular matrix-dependent modulation of cancer cell behavior. In this experimental model, cancer cells are cultured on collagen gels embedded with human cancer-associated fibroblasts.

Cancer progression (initiation, growth, invasion and metastasis) occurs through interactions between malignant cells and the surrounding tumor stromal ...

Three-Dimensional (3D) Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in particular (e.g., brain tumors such as glioblastoma multiforme and squamous cell carcinoma of the head and neck &#8211; SCCHN) it is a cause of severe morbidity and can be life-threatening even in the absence of distant metastases. In addition, cancers which have relapsed following treatment unfortunately often present with a more aggressive phenotype. Therefore, there is an opportunity to target the process of invasion to provide novel therapies that could be complementary to standard anti-proliferative agents. Until now, this strategy has been hampered by the lack of robust, reproducible assays suitable for a detailed analysis of invasion and for drug screening. Here we provide a simple micro-plate method (based on uniform, self-assembling 3D tumor spheroids) which has great potential for such studies. We exemplify the assay platform using a human glioblastoma cell line and also an SCCHN model where the development of resistance against targeted epidermal growth factor receptor (EGFR) inhibitors is associated with enhanced matrix-invasive potential. We also provide two alternative methods of semi-automated quantification: one using an imaging cytometer and a second which simply requires standard microscopy and image capture with digital image analysis.

Three-Dimensional 3D Tumor Spheroid Invasion Assay

Invasion of surrounding normal tissues is a defining characteristic of malignant tumors. We provide here a simple, semi-automated micro-plate assay of invasion into a natural 3D biomatrix that has been exemplified with a number of models of advanced human cancers.

Invasion of surrounding normal tissues is generally considered to be a key hallmark of malignant (as opposed to benign) tumors. For some cancers in ...

Use of Electromagnetic Navigational Transthoracic Needle Aspiration (E-TTNA) for Sampling of Lung Nodules

Lung nodule evaluation represents a clinical challenge especially in patients with intermediate risk for malignancy. Multiple technologies are presently available to sample nodules for pathological diagnosis. Those technologies can be divided into bronchoscopic and non-bronchoscopic interventions. Electromagnetic navigational bronchoscopy is being extensively used for the endobronchial approach to peripheral lung nodules but has been hindered by anatomic challenges resulting in a 70% diagnostic yield. Electromagnetic navigational guided transthoracic needle lung biopsy is novel non-bronchoscopic method that uses a percutaneous electromagnetic tip tracked needle to obtain core biopsy specimens. Electromagnetic navigational transthoracic needle aspiration complements bronchoscopic techniques potentially allowing the provider to maximize the diagnostic yield during one single procedure. This article describes a novel integrated diagnostic approach to pulmonary lung nodules. We propose the use of endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA) for mediastinal staging; radial EBUS, navigational bronchoscopy and E-TTNA during one single procedure to maximize diagnostic yield and minimize the number of invasive procedures needed to obtain a diagnosis. This manuscript describes in detail how the navigation transthoracic procedure is performed. Additional clinical studies are needed to determine the clinical utility of this novel technology.

Use of Electromagnetic Navigational Transthoracic Needle Aspiration E-TTNA for Sampling of Lung Nodules

We describe the novel use of electromagnetic navigational guided transthoracic needle aspiration for the pathologic assessment of human lung nodules.

Lung nodule evaluation represents a clinical challenge especially in patients with intermediate risk for malignancy. Multiple technologies are presently ...

Whole-Kidney Three-Dimensional Staining with CUBIC

Although conventional pathology provided numerous information about kidney microstructure, it was difficult to know the precise structure of blood vessels, proximal tubules, collecting ducts, glomeruli, and sympathetic nerves in the kidney due to the lack of three-dimensional (3D) information. Optical clearing is a good strategy to overcome this big hurdle. Multiple cells in a whole organ can be analyzed at single-cell resolution by combining tissue clearing and 3D imaging technique. However, cell labeling methods for whole-organ imaging remain underdeveloped. In particular, whole-mount organ staining is challenging because of the difficulty in antibody penetration. The present protocol developed a whole-mount mouse kidney staining for 3D imaging with the CUBIC (Clear, Unobstructed Brain/Body Imaging Cocktails and Computational analysis) tissue clearing method. The protocol has enabled visualizing renal sympathetic denervation after ischemia-reperfusion injury and glomerulomegaly in the early stage of diabetic kidney disease from a comprehensive viewpoint. Thus, this technique can lead to new discoveries in kidney research by providing a macroscopic perspective.

The present protocol describes a tissue clearing method and whole-mount immunofluorescent staining for three-dimensional (3D) kidney imaging. This technique can offer macroscopic perspectives in kidney pathology, leading to new biological discoveries.

Although conventional pathology provided numerous information about kidney microstructure, it was difficult to know the precise structure of blood ...

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating pulmonary nodules, and these approaches are progressively being acknowledged and adopted by physicians and patients. Nonetheless, constructing a relatively universal 3D digital model of pulmonary nodules for diagnosis and treatment is challenging due to device differences, shooting times, and nodule types. The objective of this study is to propose a new 3D digital model of pulmonary nodules that serves as a bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation. Many AI-driven pulmonary nodule detection and recognition methods employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve a good area under-the-curve (AUC) performance. However, false positives and false negatives remain a challenge for radiologists and clinicians. The interpretation and expression of features from the perspective of pulmonary nodule classification and examination are still unsatisfactory. In this study, a method of continuous 3D reconstruction of the whole lung in horizontal and coronal positions is proposed by combining existing medical image processing technologies. Compared with other applicable methods, this method allows users to rapidly locate pulmonary nodules and identify their fundamental properties while also observing pulmonary nodules from multiple perspectives, thereby providing a more effective clinical tool for diagnosing and treating pulmonary nodules.

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules  

The objective of this study is to develop a novel 3D digital model of pulmonary nodules that serves as a communication bridge between physicians and patients and is also a cutting-edge tool for pre-diagnosis and prognostic evaluation.

The three-dimensional (3D) reconstruction of pulmonary nodules using medical images has introduced new technical approaches for diagnosing and treating ...

ICE-Based 3D Remodeling for the Left Atrium and Pulmonary Vein

Three-Dimensional Modeling of the Left Atrium and Pulmonary Veins with a Precise Intracardiac Echocardiography Approach

Intracardiac echocardiography (ICE) is a novel tool for estimating cardiac anatomy during pulmonary vein isolation procedures, particularly the left atrium (LA) anatomy and pulmonary vein structures. ICE is widely used to establish a three-dimensional (3D) left atrial structural model during ablation procedures. However, it is unclear whether using ICE in a precise 3D modeling method can provide a more accurate left atrial 3D model and the transseptal approach. This study proposes a protocol to model the left atrium and pulmonary veins with ICE and fast anatomical mapping (FAM) catheter remodeling. It evaluates the accuracy of the models produced using the two methods through observer scoring. We included 50 patients who underwent ICE-based 3D remodeling and 45 who underwent FAM 3D remodeling based on pulmonary vein isolation procedures. The pulmonary vein antrum remodeling is estimated by comparing the antrum area acquired by remodeling and left atrial computed tomography angiography (CTA). The observer scores for the modeling in the ICE and FAM groups were 3.40 &#177; 0.81 and 3.02 &#177; 0.72 (P &#60; 0.05), respectively. The pulmonary vein antrum area obtained using the ICE- and FAM-based methods showed a correlation with the area acquired by left atrium CT. However, the 95% confidence interval bias was narrower in ICE-acquired models than in FAM-acquired models (-238 cm2 to 323 cm2 Vs. -363 cm2 to 386 cm2, respectively) using Bland-Altman analysis. Therefore, precise ICE possesses high accuracy in estimating the left atrial structure, becoming a promising approach for future cardiac structure estimation.

Author Spotlight: Advancements in Intracardiac Echocardiography for Atrial Anatomy Assessment 

Accurate intracardiac echocardiography (ICE) shows significant accuracy in estimating the left atrial structure, a prospective and promising cardiac structure estimation method. Here we propose a protocol for three-dimensional modeling of the left atrium and pulmonary veins with ICE and fast anatomical mapping (FAM) catheter remodeling.

Intracardiac echocardiography (ICE) is a novel tool for estimating cardiac anatomy during pulmonary vein isolation procedures, particularly the left ...

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 ...

Advancements in Pulmonary Lesion Biopsy Through Robotic Bronchoscopy

Robotic-assisted Bronchoscopy Combined with Multimodal Imaging for Targeted Lung Cryobiopsies

Robotic-assisted bronchoscopy (RAB) allows for targeted bronchoscopic biopsy in the lung. A robotic-assisted bronchoscope is navigated through the airways under direct vision after establishing a pathway to a target lesion based on mapping performed on a 3-dimensional (3D) lung and airway reconstruction obtained from a pre-procedure thin-slice computed tomography chest. RAB has maneuverability to distal airways throughout the lung, precise catheter tip articulation, and stability with the robotic arm. Adjunct imaging tools such as fluoroscopy, radial endobronchial ultrasound (r-EBUS), and cone beam computed tomography (CBCT) can be used with RAB. Studies using shape-sensing robotic-assisted bronchoscopy (ssRAB) have shown favorable diagnostic outcomes and safety profiles in both malignant and non-malignant processes for the biopsy of peripheral pulmonary lesions (PPLs). A 1.1 mm cryoprobe combined with ssRAB has been shown to be safe and effective for the diagnosis of PPLs compared to a traditional bronchoscopy with forceps biopsy. This technique can also be used for targeted lung sampling in benign processes. The aim of this article is to describe a stepwise approach to performing RAB combined with fluoroscopy, r-EBUS, and CBCT to obtain targeted transbronchial lung cryobiopsies (TBLC).

Author Spotlight: Expanding Interventional Pulmonology Research with Robotic-Assisted Bronchoscopy

This article aims to describe a stepwise approach to performing robotic-assisted bronchoscopy combined with fluoroscopy, radial endobronchial ultrasound, and cone beam computed tomography to obtain targeted transbronchial lung cryobiopsies.&#160;

Robotic-assisted bronchoscopy (RAB) allows for targeted bronchoscopic biopsy in the lung. A robotic-assisted bronchoscope is navigated through the airways ...

Advanced Lung Imaging for Disease Detection and Monitoring

Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time series of each voxel is analyzed with regard to the cardiac and breathing frequency components.
Using a standard 2D spoiled gradient-echo sequence with a temporal resolution of ~300 ms, an image-sorting algorithm was developed to produce phase-resolved functional lung imaging (PREFUL) with an increased temporal resolution. Thus, it is feasible to evaluate regional flow volume loops (FVL) during tidal volume breathing and depict the propagation of the pulse wave during the cardiac cycle. This method can be applied at 1.5T or 3T with standard MR hardware without the necessity for sequence programming, as the described protocol can be implemented with the default SPGRE sequence on most systems.
PREFUL ventilation MRI has been validated using 129Xe and 19F gas imaging with good regional agreement. Perfusion-weighted PREFUL MRI has been validated using SPECT as well as dynamic contrast enhanced (DCE) MRI. PREFUL has been tested in a dual center dual vendor setting and is currently applied in several ongoing multicenter trials. Furthermore, it is feasible across a range of field strengths (0.55T-3T) and different age groups, including newborns.
Quantitative V/Q PREFUL MRI has been used in patients with cystic fibrosis, chronic obstructive pulmonary disease, chronic thromboembolic pulmonary hypertension, and corona virus disease-2019 to quantify disease and monitor treatment change after therapy. Furthermore, PREFUL V/Q imaging has been shown to predict transplant loss due to chronic lung allograft dysfunction in patients after lung transplantation. In summary, PREFUL MRI is a validated technique for quantitative ventilation and pulmonary pulse wave/perfusion imaging for regional pulmonary disease detection, quantification, and treatment monitoring with potential added value to the current clinical routine.

Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis

Here, we describe the implementation of phase-resolved functional lung MRI as a contrast-agent-free proton MR technique for the assessment of pulmonary ventilation and perfusion dynamics. Validated and applicable across different field strengths and age groups, it could enhance clinical decision-making in the future by aiding in disease quantification and therapy monitoring.

Fourier decomposition is a contrast agent-free 1H MRI method for lung perfusion (Q) and ventilation (V) assessment. After image registration, the time ...

Protocol for High-Resolution Lung Imaging Using 3T UTE MRI with Self-Gating

Pulmonary Structural MRI using Free-Breathing, Self-Gated Ultra-short Echo Time Imaging

High quality MRI of the lungs is challenged by low tissue density, fast MRI signal relaxation, and respiratory and cardiac motion. For these reasons, structural imaging of the lungs is performed almost exclusively using Computed Tomography (CT). However, CT imaging delivers ionizing radiation, and thus is less well suited for certain vulnerable populations (e.g., pediatrics) or for research applications. As an alternative, MRI using ultra-short echo times (UTE) is attracting interest. This technique can be performed during free-breathing over the course of a ~5-10 min scan. Respiratory motion information is encoded alongside images; this information can be used to "self-gate" images. Self-gating thus removes the requirement of advanced MRI pulse sequence programming or the use of respiratory bellows, which simplifies image acquisition. In this protocol, simple, robust, and computationally efficient acquisition and reconstruction methods for acquiring high quality UTE MRI of the lungs are presented. This protocol was developed for use on a 3T MRI scanner, but the same principles can be implemented at lower magnetic field strength. The protocol includes recommended parameter settings for 3D radial UTE image acquisition as well as directions for self-gated image reconstruction to generate images at distinct respiratory phases. Through the implementation of this protocol, users can generate high-resolution UTE images of the lungs with minimal to minimal-to-no motion artifacts. These images can be used to evaluate pulmonary structure, which can be implemented for research use in a variety of pulmonary conditions.

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

A protocol is described for generating high-resolution structural images of the lungs using ultra-short-echo time (UTE) Magnetic Resonance Imaging (MRI). This protocol allows for images to be acquired using a simple MRI pulse sequence during free-breathing.

High quality MRI of the lungs is challenged by low tissue density, fast MRI signal relaxation, and respiratory and cardiac motion. For these reasons, ...