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

NOTE: During the data preprocessing stage, the original DICOM data must be sorted and intercepted to ensure compatibility with various devices and consistent results. Adequate adjustable capacity must be reserved for intensity processing, and a continuous 3D perspective is essential for observation. In this protocol, a methodical description of the research approach is provided, detailing a case involving an 84-year-old female patient presenting with pulmonary nodules. This patient provided informed consent for her diagn...

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

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J., Lam, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+the+patient+with+a+pulmonary+nodule:+A+review.">Evaluating the patient with a pulmonary nodule: A review.</a> JAMA. 327 (3), 264-273 (2022).</li><li>MacMahon, 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=Guidelines+for+management+of+incidental+pulmonary+nodules+detected+on+CT+images:+From+the+Fleischner+Society+2017.">Guidelines for management of incidental pulmonary nodules detected on CT images: From the Fleischner Society 2017.</a> Radiology. 284 (1), 228-243 (2017).</li><li>Ather, S., Kadir, T., Gleeson, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+intelligence+and+radiomics+in+pulmonary+nodule+management:+Current+status+and+future+applications.">Artificial intelligence and radiomics in pulmonary nodule management: Current status and future applications.</a> Clinical Radiology. 75 (1), 13-19 (2020).</li><li>Bianconi, F., 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=Comparative+evaluation+of+conventional+and+deep+learning+methods+for+semi-automated+segmentation+of+pulmonary+nodules+on+CT.">Comparative evaluation of conventional and deep learning methods for semi-automated segmentation of pulmonary nodules on CT.</a> Quantitative Imaging in Medicine and Surgery. 11 (7), 3286-3305 (2021).</li><li>Christe, 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=Computer-aided+diagnosis+of+pulmonary+fibrosis+using+deep+learning+and+CT+images.">Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images.</a> Investigative Radiology. 54 (10), 627-632 (2019).</li><li>Kim, Y., Park, J. Y., Hwang, E. J., Lee, S. M., Park, 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=Applications+of+artificial+intelligence+in+the+thorax:+A+narrative+review+focusing+on+thoracic+radiology.">Applications of artificial intelligence in the thorax: A narrative review focusing on thoracic radiology.</a> Journal of Thoracic Disease. 13 (12), 6943-6962 (2021).</li><li>Schreuder, A., Scholten, E. T., van Ginneken, B., Jacobs, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Artificial+intelligence+for+detection+and+characterization+of+pulmonary+nodules+in+lung+cancer+CT+screening:+Ready+for+practice.">Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: Ready for practice.</a> Translational Lung Cancer Research. 10 (5), 2378-2388 (2021).</li><li>Gruden, J. F., Ouanounou, S., Tigges, S., Norris, S. D., Klausner, T. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Incremental+benefit+of+maximum-intensity-projection+images+on+observer+detection+of+small+pulmonary+nodules+revealed+by+multidetector+CT.">Incremental benefit of maximum-intensity-projection images on observer detection of small pulmonary nodules revealed by multidetector CT.</a> American Journal of Roentgenology. 179 (1), 149-157 (2002).</li><li>Guleryuz Kizil, P., Hekimoglu, K., Coskun, M., Akcay, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+importance+of+maximum+intensity+projection+technique+in+the+identification+of+small+pulmonary+nodules+with+computed+tomography.">Diagnostic importance of maximum intensity projection technique in the identification of small pulmonary nodules with computed tomography.</a> Tuberk Toraks. 68 (1), 35-42 (2020).</li><li>Valencia, R., 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=Value+of+axial+and+coronal+maximum+intensity+projection+(MIP)+images+in+the+detection+of+pulmonary+nodules+by+multislice+spiral+CT:+Comparison+with+axial+1-mm+and+5-mm+slices.">Value of axial and coronal maximum intensity projection (MIP) images in the detection of pulmonary nodules by multislice spiral CT: Comparison with axial 1-mm and 5-mm slices.</a> European Radiology. 16, 325-332 (2006).</li><li>Jabeen, N., Qureshi, R., Sattar, A., Baloch, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+accuracy+of+maximum+intensity+projection+in+diagnosis+of+malignant+pulmonary+nodules.">Diagnostic accuracy of maximum intensity projection in diagnosis of malignant pulmonary nodules.</a> Cureus. 11 (11), e6120 (2019).</li><li>Naeem, 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=Comparison+of+maximum+intensity+projection+and+volume+rendering+in+detecting+pulmonary+nodules+on+multidetector+computed+tomography.">Comparison of maximum intensity projection and volume rendering in detecting pulmonary nodules on multidetector computed tomography.</a> Cureus. 13 (3), e14025 (2021).</li><li>Armato, S. 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=The+lung+image+database+consortium+(LIDC)+and+image+database+resource+initiative+(IDRI):+A+completed+reference+database+of+lung+nodules+on+CT+scans.">The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans.</a> Medical Physics. 38 (2), 915-931 (2011).</li><li>Xie, 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=Knowledge-based+collaborative+deep+learning+for+benign-malignant+lung+nodule+classification+on+chest+CT.">Knowledge-based collaborative deep learning for benign-malignant lung nodule classification on chest CT.</a> IEEE Transactions on Medical Imaging. 38 (4), 991-1004 (2018).</li><li>Zheng, 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=Automatic+pulmonary+nodule+detection+in+CT+scans+using+convolutional+neural+networks+based+on+maximum+intensity+projection.">Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection.</a> IEEE Transactions on Medical Imaging. 39 (3), 797-805 (2019).</li></ol>

Different LDCT devices have significant differences in the DICOM image sequences they output, especially in terms of the file system management. Therefore, to reconstruct the key 3D digital model of a pulmonary nodule in the later stages of the protocol, the data preprocessing step is particularly important. In the data preparation and preprocessing stage (step 1.2.2), the sequence z-axis coordinate can be correctly sorted by using the sequence shown in Figure 1, which can also be used to pr...

The global incidence of pulmonary nodules is variable, but it is generally estimated that about 30% of adults have at least one pulmonary nodule visible on chest radiographs1. The incidence of pulmonary nodules is higher in specific populations, such as heavy smokers and those with a history of lung cancer or other lung diseases. It is important to note that not all pulmonary nodules are malignant, but a thorough evaluation is necessary to rule out malignancy2. The early detection and diagnosis of lung cancer are crucial for improving survival rates, and regular screening with low-dose computed tomography (LDCT) is recommended for high-risk individuals. Many AI-driven pulmonary nodule detection and recognition methods3,4,5,6,7 employ deep learning techniques to capture the radiological features of pulmonary nodules, and these methods can achieve 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. At the same time, the 3D reconstruction of pulmonary nodules based on LDCT has gained increasing attention as a digital model for various types of nodules.
The 3D reconstruction of pulmonary nodules is a process that generates a 3D representation of a small growth or lump in the lung. This process typically involves the application of medical image analysis techniques that leverage both medical expertise and data intelligence approaches. The resulting 3D digital model offers a more detailed and accurate depiction of the nodule, enabling the improved visualization and analysis of its size, shape, and spatial relationship with the surrounding lung tissues8,9,10,11,12. Such information can aid in the diagnosis and monitoring of pulmonary nodules, particularly those suspected of being cancerous. By facilitating more precise analysis, the 3D reconstruction of pulmonary nodules has the potential to enhance the accuracy of diagnosis and inform treatment decisions.
Maximum intensity projection (MIP) is a popular technique in the field of 3D reconstruction of pulmonary nodules and is used to create a 2D projection of a 3D image8,9,10,11,12It is particularly useful in the visualization of volumetric data extracted from digital imaging and communications in medicine (DICOM) files scanned by CT. The MIP technique works by selecting the voxels (the smallest units of 3D volume data) with the highest intensity along the viewing direction and projecting them onto a 2D plane. This results in a 2D image that emphasizes the structures with the highest intensity and suppresses those with lower intensity, which makes it easier to identify and analyze relevant features9,10,11,12. However, MIP is not without limitations. For example, the projection process can result in a loss of information, and the resulting 2D image may not accurately represent the 3D structure of the underlying object. Nevertheless, MIP remains a valuable tool for medical imaging and visualization, and its use continues to evolve with advances in technology and computing power11.
In this study, a successive MIP model to visualize pulmonary nodules is developed that is easy to use, user-friendly for radiologists, physicians, and patients, and allows the identification and estimation of the properties of pulmonary nodules. The primary advantages of this processing approach include the following aspects: (1) eliminating false positives and false negatives arising from pattern recognition, which enables a focus on assisting physicians to obtain more comprehensive information on the location, shape, and 3D size of pulmonary nodules, as well as their relationship to the surrounding vasculature; (2) enabling specialist physicians to attain professional knowledge of the characteristics of pulmonary nodules even without the assistance of radiologists; and (3) enhancing both communication efficiency between physicians and patients and prognosis evaluation.

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	<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>&#160;Intelligent 
			&#160;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>

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  

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

This study aims to create a continuous 3D reconstruction model for the better characterizing the pulmonary nodules for clinical diagnosis and prognosis evaluation. Deep integration of AI-driven imaging technology and pulmonary nodule disease diagnosis and treatment are some of the recent developments in this research field. The combination of medical imaging, natural language processing, digitization, class of field design, and clinical diagnosis and treatment scenario risks, are recent advances in this field.Gradually, establishing three-dimensional imaging, differentiating features of pulmonary nodules and conducting long-term tracking of the efficacy of traditional Chinese medicine, helps to optimize the treatment plan for the prevention and the treatment of disease. The images produced with this digital model, are accurate and intuitive, avoiding false positives and false negatives, and are not sensitive to scanning equipment. The outcome of this study helps in the clinical evidence-based classification of pulmonary nodules, prognosis, evaluation, and optimization of treatment plans, based on three-dimensional special features.Our research focus includes combining digitization, artificial intelligence, imaging, and the evaluation and optimization of clinical treatment plans of traditional Chinese medicine, to create cost effective and better treatment plans.

To make the method applicable to a wider range of devices, the stacking order of each scan needs to be reorganized based on the internal coordinates of the DICOM file system (Figure 1) to generate the correct 3D volume (Figure 2). Based on the accurate volume data, we utilized algorithmic continuous reconstruction of the patient&#39;s lung horizontal and coronal MIPs (Figure 4 and Figure 5) for the prec...

Watch this Scientific Journal Video about A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules at JoVE.com

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 ...

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﻿Low-Dose Computed Tomography (LDCT) Data Collection for Detecting Pulmonary Nodules

Low-Dose Computed Tomography (LDCT) Data Collection for Detecting Pulmonary Nodules  

To begin, obtain written consent from the patient for acquiring the DICOM data. After obtaining the images, transfer the data to the designated working directory in the computer. Then, identify the data directory with the highest number of scanning layers and the thinnest layer thickness to optimize the accuracy based on the file information.Generally, the more DICOM scan files, the thinner is the scan layer thickness. Using the DICOM info function and DICOM files as the function parameters in the MATLAB environment, obtain the slice thickness and pixel-spacing parameters, which are essential for setting the 3D volume display rate. Next, using the function dicominfo, read the location data of each image by giving info.sliceLocation as input into the MATLAB workspace. Implement the SliceLocation function to store the location array for a variable and create a plot of the location array. Use the data tips button at the top right of the graphical user interface, or GUI, and add a data tip to the plot on the point representing the maximum location value of the normal sequence.Using the VolumeResort function, sort all the images and then extract the images from one to the maximum location value. Store the volumes of the valid images with the sorted index, which will help to trace back the important nodules. Move the crosshair to the horizontal axis to browse all the images in the coronal axis.Note that the crosshair is in the same spatial coordinates and moving it on one axis will change the location of the images in the other two axes. Use the VolumeInspect function to display the axial, coronal, and sagittal views of the constructed volume. In the VolumeInspect function, use the default intensity window for the lung in the GUI.Hold the left mouse button to adjust the filter performance and drag it on the axis. Direct data preparation and volume calculation results in lung images appearing in the axial, coronal, and sagittal planes.

Digital Model for Horizontal 3D Reconstruction of Pulmonary Nodules

To reconstruct the three-dimensional digital model of the pulmonary nodules, implement the Three-Dimensional Lung Horizon Function in the MATLAB workspace. Then open the Graphical User Interface, or GUI, to check the horizontal three-dimensional model. Drag the scroll bar on the GUI with the mouse to observe the continuous three-dimensional lung structure, allowing for the clear visualization of various types of pulmonary nodules and their relative spatial relationships with the lung tissue.Use the Zoom function to observe the local features of the lesions and relevant three-dimensional structural output pictures. To measure the size of the nodules, use the Mark Pixel Coordinates button to calculate the distance between two points. The default color bar, jet colormap, displays values from low to high in a blue to red range.To reset the GUI, right click the color bar and select the standard gray colormap. If the filter window is not satisfactory, use the left mouse button to adjust the window level by dragging up and down in the middle of the figure. Drag left and right to adjust the window width to display the accurate filtering range on the color bar.The continuous three-dimensional reconstruction resulted in the axial view of the pulmonary nodules and the relationship between the nodules and the surrounding lung tissue.

Constructing a 3D Digital Model for any Specific Nodule

Use the three dimensional nodules function with two parameters, including the slice number and the thoracic volume to reconstruct a three dimensional digital model for specific nodules. If the three dimensional nodules function correctly executes, press and hold the left mouse button, and drag it in any direction to alter the perspective of the pulmonary nodules. It is important to note that the observation angle should account for anatomical considerations and attempt to display both the medical characteristics of the pulmonary nodules and the relationship between the nodules and the surrounding tissues.Use the zoom and move icons in the upper right corner. Alternatively, use the middle mouse button to zoom in on the model or out of the view. The GUI displays the coordinate indication in the lower left corner with the positive direction on the Z axis being the scanning direction in the horizontal position.Utilize the screenshot tool for the operating system to save the necessary three dimensional projection of the nodules. With this three dimensional construction, the GUI allows the user to observe the pulmonary nodules of interest from any perspective.

Digital Model of a Coronal 3D Reconstruction 

To reconstruct the three-dimensional digital model of pulmonary nodules, implement the three-dimensional lung coronal function in the MATLAB workplace. Then open the graphical user interface or GUI prepared by the function to check the three-dimensional coronal model. Drag the scroll bar on the GUI with the mouse to observe the continuous three-dimensional lung structure, allowing for the clear visualization of various types of pulmonary nodules and the relative spatial relationships with the lung tissue.Use the zoom function to observe the local features of the lesions and relevant three-dimensional structural output pictures. To measure the size of the nodules, use the Mark Pixel Coordinates button to calculate the distance between two points. The default color bar, Jet Colormap, displays values from low to high in a blue to red range.To reset the GUI, right click the color bar and select the standard gray colormap. If the filter window is not satisfactory, use the left mouse button to adjust the window level by dragging up and down in the middle of the figure. Drag left and right to adjust the window width to display the accurate filtering range on the color bar.The continuous three-dimensional reconstruction resulted in the coronal view of the pulmonary nodules and the relationship between the nodules and the surrounding lung tissue.

Output 3D Video for Dominant Pulmonary Nodules 

Once the three-dimensional digital model is prepared, open Adobe Captivate 2019, and create a new screen recording project. Turn the camera off, and wait for a red screen recording range box to appear for recording only the screen operation. Fit the frame into the model, and click the recording button to operate the Graphical User Interface, or GUI, and generate a digital video file of the screen recording.After recording the dynamic display of the pulmonary nodules, click the icon in the task bar to return to the operating environment of the software. Next, click on File, Distribute, and configure the file storage path to save the recorded dynamic video of the three-dimensional digital model of the pulmonary nodules. Then name the file and save the desired digital video file.

1.4K Views.  Beijing University of Chinese Medicine.  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.

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

Summary