The video demonstrates how to reconstruct and visualize a three-dimensional digital model of pulmonary nodules using MATLAB. It covers using a GUI for exploring lung structures, measuring nodule sizes with pixel coordinates, adjusting color maps, and observing nodules' spatial relationships with lung tissue through interactive features.

digital model for horizontal 3d reconstruction of pulmonary nodules

3D Reconstruction for the Diagnosis and Treatment of Pulmonary Nodules

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.

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.

Watch this Scientific Journal Video about Digital Model for Horizontal 3D Reconstruction of Pulmonary Nodules at JoVE.com

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.

digital-model-for-horizontal-3d-reconstruction-of-pulmonary-nodules

Research

JoVE Journal

Medicine

65 Views.  Beijing University of Chinese Medicine. 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.