Name: Digital Model of a Coronal 3D Reconstruction
Uploaded: 2023-05-19T14:20:00
Description: Watch this Scientific Journal Video about Digital Model of a Coronal 3D Reconstruction at JoVE.com

The video demonstrates how to use MATLAB to create a three-dimensional digital model of pulmonary nodules. It guides users through a graphical interface to visualize lung structures, zoom in on lesions, and measure nodule sizes, while explaining color bar adjustments and improving filtering for better clarity.

digital model of a coronal 3d reconstruction 

3D-реконструкция для диагностики и лечения легочных узлов

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.

Автор в центре внимания: 3D-цифровая модель для диагностики и лечения легочных узлов  

Цифровая 3D-модель для диагностики и лечения легочных узлов

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 of a Coronal 3D Reconstruction at JoVE.com

Digital Model of a Coronal 3D Reconstruction   

Цифровая модель корональной 3D-реконструкции 

Чтобы реконструировать трехмерную цифровую модель легочных узелков, реализуйте трехмерную корональную функцию легких на рабочем месте MATLAB. Затем откройте графический пользовательский интерфейс или графический интерфейс, подготовленный функцией, чтобы проверить трехмерную корональную модель. Перетащите полосу прокрутки в графическом интерфейсе с помощью мыши, чтобы наблюдать непрерывную трехмерную структуру легких, что позволяет четко визуализировать различные типы легочных узелков и относительные пространственные отношения с легочной тканью.Используйте функцию масштабирования, чтобы наблюдать за локальными особенностями поражений и соответствующими трехмерными структурными выходными изображениями. Чтобы измерить размер узелков, используйте кнопку «Отметить координаты пикселей», чтобы вычислить расстояние между двумя точками. Цветовая шкала по умолчанию, Jet Colormap, отображает значения от низкого до высокого в диапазоне от синего до красного.Чтобы сбросить графический интерфейс, щелкните правой кнопкой мыши цветовую полосу и выберите стандартную серую цветовую карту. Если окно фильтра не устраивает, используйте левую кнопку мыши, чтобы отрегулировать уровень окна, перетаскивая вверх и вниз по середине рисунка. Перетащите влево и вправо, чтобы отрегулировать ширину окна и отобразить точный диапазон фильтрации на цветовой шкале.Непрерывная трехмерная реконструкция привела к корональному виду легочных узелков и взаимосвязи между узелками и окружающей легочной тканью.

Research

JoVE Journal

Medicine

Digital Model of a Coronal 3D Reconstruction 

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 ...

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

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

Сбор данных низкодозной компьютерной томографии (ЛДКТ) для выявления легочных узелков

Digital Model for Horizontal 3D Reconstruction of Pulmonary Nodules

Цифровая модель для горизонтальной 3D-реконструкции легочных узлов

Constructing a 3D Digital Model for any Specific Nodule

Построение цифровой 3D-модели для любого конкретного конкреция

Output 3D Video for Dominant Pulmonary Nodules 

Вывод 3D-видео для доминантных легочных узелков 