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 

폐결절의 진단 및 치료를 위한 3D 재구성

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.

저자 스포트라이트: 폐 결절의 진단 및 치료를 위한 3D 디지털 모델  

폐 결절의 진단 및 치료를 위한 3D 디지털 모델

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   

관상 3D 재구성의 디지털 모델 

폐 결절의 3차원 디지털 모델을 재구성하려면 MATLAB 작업 공간에서 3차원 폐 관상 함수를 구현하십시오. 그런 다음 3차원 관상 모델을 확인하는 기능으로 준비된 그래픽 사용자 인터페이스 또는 GUI를 엽니다. 마우스로 GUI의 스크롤 막대를 드래그하여 연속적인 3차원 폐 구조를 관찰하여 다양한 유형의 폐 결절과 폐 조직과의 상대적인 공간 관계를 명확하게 시각화할 수 있습니다.줌 기능을 사용하여 병변의 국부적 특징과 관련 3차원 구조 출력 사진을 관찰합니다. 결절의 크기를 측정하려면 픽셀 좌표 표시 버튼을 사용하여 두 점 사이의 거리를 계산합니다. 디폴트 색상 막대인 제트 컬러맵(Jet Colormap)은 파란색에서 빨간색 범위의 값을 낮음에서 높음으로 표시합니다.GUI를 재설정하려면 색상 막대를 마우스 오른쪽 버튼으로 클릭하고 표준 회색 컬러맵을 선택합니다. 필터 창이 만족스럽지 않으면 마우스 왼쪽 버튼을 사용하여 그림 가운데를 위아래로 드래그하여 창 수준을 조정합니다. 왼쪽과 오른쪽으로 드래그하여 창 너비를 조정하면 색상 막대에 정확한 필터링 범위가 표시됩니다.지속적인 3차원 재건은 폐 결절의 관상 보기와 결절과 주변 폐 조직 사이의 관계를 초래했습니다.

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 ...