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

用于肺结节诊断和治疗的 3D 重建

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.

作者聚焦：用于肺结节诊断和治疗的 3D 数字模型  

用于肺结节诊断和治疗的三维数字模型

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  

肺结节水平三维重建数字模型

要重建肺结节的三维数字模型，请在 MATLAB 工作区中实现三维肺水平函数。然后打开图形用户界面（GUI）以检查水平三维模型。用鼠标拖动GUI上的滚动条，观察连续的三维肺结构，可以清晰地看到各种类型的肺结节及其与肺组织的相对空间关系。使用缩放功能观察病变的局部特征和相关三维结构输出图片。要测量结节的大小，请使用标记像素坐标按钮计算两点之间的距离。默认颜色条（喷气式颜色图）在蓝色到红色范围内显示从低到高的值。要重置 GUI，请右键单击颜色条并选择标准灰色颜色图。如果滤镜窗口不理想，则使用鼠标左键通过在图中中间上下拖动来调整窗口级别。左右拖动以调整窗口宽度，以在颜色条上显示准确的过滤范围。连续的三维重建产生了肺结节的轴视图以及结节与周围肺组织之间的关系。

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Digital Model for Horizontal 3D Reconstruction of Pulmonary Nodules

68 次观看. 要重建肺结节的三维数字模型，请在 MATLAB 工作区中实现三维肺水平函数。然后打开图形用户界面（GUI）以检查水平三维模型。用鼠标拖动GUI上的滚动条，观察连续的三维肺结构，可以清晰地看到各种类型的肺结节及其与肺组织的相对空间关系。使用缩放功能观察病变的局部特征和相关三维结构输出图片。要测量结节的大小，请使用标记像素坐标按钮计算两点之间的距?...

视频: Digital Model for Horizontal 3D Reconstruction 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.

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 ...