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

Ricostruzione 3D per la diagnosi e il trattamento dei noduli polmonari

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.

Autore in primo piano: un modello digitale 3D per la diagnosi e il trattamento dei noduli polmonari  

Un modello digitale 3D per la diagnosi e il trattamento dei noduli polmonari

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  

Raccolta dati di tomografia computerizzata a basso dosaggio (LDCT) per la rilevazione di noduli polmonari

Per iniziare, ottenere il consenso scritto dal paziente per l'acquisizione dei dati DICOM. Dopo aver ottenuto le immagini, trasferire i dati nella directory di lavoro designata nel computer. Quindi, identificare la directory dei dati con il maggior numero di livelli di scansione e lo spessore dello strato più sottile per ottimizzare l'accuratezza in base alle informazioni sul file.Generalmente, più file di scansione DICOM, più sottile è lo spessore dello strato di scansione. Utilizzando la funzione DICOM info e i file DICOM come parametri di funzione nell'ambiente MATLAB, ottenere i parametri di spessore della sezione e spaziatura pixel, che sono essenziali per impostare la velocità di visualizzazione del volume 3D. Successivamente, utilizzando la funzione dicominfo, leggere i dati di posizione di ogni immagine fornendo informazioni.sliceLocation come input nell'area di lavoro MATLAB. Implementare la funzione SliceLocation per memorizzare la matrice di posizioni per una variabile e creare un grafico della matrice di posizioni. Utilizzare il pulsante dei suggerimenti dati in alto a destra dell'interfaccia utente grafica, o GUI, e aggiungere un suggerimento dati al grafico sul punto che rappresenta il valore di posizione massima della sequenza normale.Utilizzando la funzione VolumeResort, ordinare tutte le immagini e quindi estrarre le immagini da uno al valore di posizione massima. Memorizza i volumi delle immagini valide con l'indice ordinato, che aiuterà a risalire ai noduli importanti. Sposta il mirino sull'asse orizzontale per sfogliare tutte le immagini nell'asse coronale.Si noti che il mirino si trova nelle stesse coordinate spaziali e spostandolo su un asse cambierà la posizione delle immagini negli altri due assi. Utilizzare la funzione VolumeInspect per visualizzare le viste assiale, coronale e sagittale del volume costruito. Nella funzione VolumeInspect, utilizzare la finestra di intensità predefinita per il polmone nella GUI.Tenere premuto il pulsante sinistro del mouse per regolare le prestazioni del filtro e trascinarlo sull'asse. La preparazione diretta dei dati e il calcolo del volume producono immagini polmonari che appaiono nei piani assiale, coronale e sagittale.

Research

JoVE Journal

Medicine

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