The video outlines a process for managing DICOM data for medical imaging. It details obtaining patient consent, transferring data, optimizing image quality using MATLAB, extracting slice locations, and visualizing lung images in three planes (axial, coronal, sagittal) through specific functions to analyze scan volumes effectively.

﻿low-dose computed tomography (ldct) data collection for detecting 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 ﻿Low-Dose Computed Tomography LDCT Data Collection for Detecting Pulmonary Nodules at JoVE.com

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.

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Research

JoVE Journal

Medicine

70 Görüntüleme.  Beijing University of Chinese Medicine. Başlamak için, DICOM verilerini elde etmek için hastadan yazılı onay alın. Görüntüleri elde ettikten sonra, verileri bilgisayarda belirlenen çalışma dizinine aktarın. Ardından, dosya bilgilerine göre doğruluğu optimize etmek için en fazla sayıda tarama katmanına ve en ince katman kalınlığına sahip veri dizinini belirleyin.Genel olarak, DICOM tarama dosyaları ne kadar fazla olursa, tarama katmanı kalınlığı o kadar ince olur. DICOM bilgi işlevini ve DICOM d...