This study introduces a three-dimensional (3D) reconstruction method for the entire lung in patients with early multiple pulmonary nodules. It offers a comprehensive visualization of nodule distribution and their interplay with lung tissue, simplifying the assessment of diagnosis and prognosis for these patients.
For patients with early multiple pulmonary nodules, it is essential, from a diagnostic perspective, to determine the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the entire lung. This is crucial for identifying the primary lesion and developing more scientifically grounded treatment plans for doctors. However, pattern recognition methods based on machine vision are susceptible to false positives and false negatives and, therefore, cannot fully meet clinical demands in this regard. Visualization methods based on maximum intensity projection (MIP) can better illustrate local and individual pulmonary nodules but lack a macroscopic and holistic description of the distribution and spatial features of multiple pulmonary nodules.
Therefore, this study proposes a whole-lung 3D reconstruction method. It extracts the 3D contour of the lung using medical image processing technology against the background of the entire lung and performs 3D reconstruction of the lung, pulmonary artery, and multiple pulmonary nodules in 3D space. This method can comprehensively depict the spatial distribution and radiological features of multiple nodules throughout the entire lung, providing a simple and convenient means of evaluating the diagnosis and prognosis of multiple pulmonary nodules.
Early multiple pulmonary nodules, which are small, round growths on the lung, can be benign or malignant1,2,3. Although solitary pulmonary nodules are easier to diagnose and treat, patients with early multiple pulmonary nodules face significant diagnostic and treatment challenges. To develop effective treatment plans, it is essential to accurately identify the spatial distribution, size, location, and relationship with surrounding lung tissue of these nodules throughout the whole lung4,5. Traditional diagnostic methods have limitations in accurately identifying early multiple pulmonary nodules.
Recent advancements in medical image processing technology and machine learning algorithms have the potential to improve the accuracy and efficiency of early pulmonary nodule detection and diagnosis. Various approaches have been proposed, such as pattern recognition methods based on machine vision and visualization methods based on maximum intensity projection (MIP)6,7,8,9,10. However, these methods suffer from limitations such as false positives, false negatives11,12,13,14,15, and lack of macroscopic and holistic descriptions of the distribution and spatial features of early multiple pulmonary nodules.
To address these limitations, this study proposes a whole-lung 3D reconstruction method that utilizes medical image processing technology to extract the 3D contour of the lung against the background of the whole chest scan. The method then performs 3D reconstruction of the lung, pulmonary artery, and early multiple pulmonary nodules in 3D space. This approach allows for a more comprehensive and accurate representation of the spatial distribution and radiological features of early multiple nodules throughout the whole lung.
The proposed method involves several key steps. Firstly, the medical images are imported into the 3D image processing software, and the lung region is extracted using a threshold-based segmentation technique. Subsequently, the extracted lung region is separated from the surrounding chest wall and the bony structures of the thoracic vertebrae. The early multiple pulmonary nodules and their relationship with surrounding blood vessels are then reconstructed in 3D space using maximum intensity projection (MIP) algorithms. Finally, the reconstructed 3D model of the lung, pulmonary artery, and nodules is displayed for further analysis.
This method has several advantages over existing methods. Unlike traditional methods that rely on 2D images, this method utilizes 3D volume to provide a more accurate and comprehensive representation of early multiple pulmonary nodules. The method also overcomes the limitations of false positives and false negatives associated with pattern recognition methods and MIP visualization methods. Furthermore, this method provides a macroscopic and holistic description of the distribution and spatial features of early multiple pulmonary nodules, which is essential for developing effective treatment plans.
The proposed method has several potential applications in the diagnosis and treatment of early multiple pulmonary nodules. The accurate identification of the spatial distribution and radiological features of early multiple nodules can aid in the early diagnosis and treatment of lung cancer. Furthermore, the method can be used to monitor the progression of the disease and evaluate the effectiveness of treatment plans.
Pattern recognition methods6,7,8 based on machine vision have shown promise in identifying pulmonary nodules, but suffer from limitations such as false positives and false negatives. MIP visualization methods, on the other hand, provide a more accurate representation of individual nodules, but lack a macroscopic and holistic description of the distribution and spatial features of early multiple nodules. The proposed whole-lung 3D reconstruction method overcomes these limitations and provides a more accurate and comprehensive representation of early multiple pulmonary nodules.
Isovoxel transformation16,17refers to the process of converting 3D images with different voxel sizes into 3D images with uniform voxel sizes. In the field of medical image processing, 3D volumes are often composed of voxels with varying sizes, which can lead to computational and visualization issues. The purpose of isovoxel transformation is to address these issues by resampling and interpolating the voxels in the original 3D volume, resulting in a new 3D image with consistent voxel sizes. This technique finds applications in various medical contexts, including image registration, segmentation, and visualization. Thus, this study proposed a whole-lung 3D reconstruction method that utilizes medical image processing technology to extract the 3D contour of the lung against the background of the whole chest scan. The method provides a more accurate and comprehensive representation of the spatial distribution and radiological features of early multiple nodules throughout the whole lung. This study contributes to the development of more accurate and effective diagnostic and treatment strategies for patients with early multiple pulmonary nodules.
For the present study, ethical clearance was obtained from The Ethics Committee of Dongzhimen Hospital, affiliated with Beijing University of Chinese Medicine (DZMEC-KY-2019.90). In this specific case, a methodical description of the research approach is provided, outlining a case involving a 65-year-old female patient with multiple pulmonary nodules. This patient provided informed consent for her diagnosis through digital modeling and authorized the use of her data for scientific research purposes. The model reconstruction function is derived from a commercially available software tool (see Table of Materials).
1. Data preparation and isovoxel transformation
2. Removal of noise interference caused by Computed Tomography (CT) equipment
NOTE: In Figure 2, the high-intensity signal representing the CT equipment's patient couch is visible, which can interfere with image segmentation. To eliminate this interference, a spatial filter design is required.
3. Extraction of lung contour
4. 3D reconstruction for the whole lung with multiple pulmonary nodules
NOTE: Taking the dot product of the lung segmentation image of each image with the original image is equivalent to performing 3D spatial filtering on the volume, effectively filtering out interference signals outside the lungs and obtaining the 3D structure of the lungs.
5. Focus on the examination of dominant pulmonary nodules
NOTE: In 3D space (Figure 8), the dominant lesion area among multiple pulmonary nodules becomes distinctly visible. The number, size, and concentration of these nodules are critical features of the dominant lesion, offering valuable insights into disease assessment.
In the data preprocessing stage, DICOM data sorting should be the first step (Figure 1) to ensure the correct scan sequence for each layer during 3D reconstruction. Next, isotropic transformation is performed to ensure the correct aspect ratio of the 3D volume (Figure 2). Afterward, spatial filtering is applied to the original 3D volume (Figure 3) to eliminate interference signals from the patient couch of the CT equipment (Figure 4). To obtain the 3D contour of the entire lung, image segmentation is performed on each scan (Figure 5) to create a binary lung image (Figure 6). Based on the 3D contour of the lung, the entire lung 3D volume is reconstructed (Figure 7) and visualized in 3D (Figure 8). For the dominant lesion area (Figure 9), a separate 3D visualization (Figure 10) can be performed to carefully identify the detailed features of the lesion.
Isovoxel transformation ensures that the same scale is maintained in all dimensions during subsequent processing. Figure 2 displays the slice view after isovoxel transformation. In this graphical user interface (GUI), one can view the complete raw 3D volume data.
Figure 3 and Figure 4 demonstrate the spatial filtering process used to remove bed signal interference from the CT equipment. Without this, images with noisy signals cannot complete the segmentation of lung structures in subsequent steps.
Figure 5 and Figure 6 illustrate the lung contour extraction function, which can automatically extract lung contours, providing the basic conditions for the subsequent 3D reconstruction of lung structures.
Figure 7 and Figure 8 show the 3D reconstruction of the entire lung, revealing the spatial distribution of lung tissues and multiple lung nodules. By eliminating signal interference from tissues outside the lungs, the spatial location, size, and concentration of multiple pulmonary nodules can be accurately depicted.
Figure 9 and Figure 10 display the 3D visualization of the dominant lung nodules of interest. Due to the exclusion of signal interference from outside the lungs, the contrast of the images is improved. The ability to observe the 3D structure from any angle allows physicians to make more accurate judgments about the lesion features of the dominant pulmonary nodules.
Figure 1: Location plot of images. The plot displays the location of images based on their file name sequence. Please click here to view a larger version of this figure.
Figure 2: GUI for 3D-volume slice view. Graphical User Interface (GUI) for viewing slices of the 3D volume after Isovoxel transformation. Please click here to view a larger version of this figure.
Figure 3: Reference boundary scatter matrix. The matrix representing reference boundary scatter for spatial filtering. Please click here to view a larger version of this figure.
Figure 4: 3D volume slice view after spatial filtering. View of slices from the 3D volume after applying spatial filtering. Please click here to view a larger version of this figure.
Figure 5: Image segmenter GUI. Graphical User Interface (GUI) of the Image Segmenter tool. Please click here to view a larger version of this figure.
Figure 6: Result of lung area shadow filling. The resultant image after filling black shadows in the lung area using the "Fill Holes" button. Please click here to view a larger version of this figure.
Figure 7: 3D lung reconstruction with multiple pulmonary nodules. 3D reconstruction of the entire lung showing early multiple pulmonary nodules. Please click here to view a larger version of this figure.
Figure 8: Interactive GUI for 3D lung volume viewing. Interactive Graphical User Interface (GUI) for viewing and manipulating the entire 3D lung volume. Please click here to view a larger version of this figure.
Figure 9: Slice view for navigating dominant pulmonary nodule area. Slice view for navigating the region containing dominant pulmonary nodules within the entire 3D lung volume. Please click here to view a larger version of this figure.
Figure 10: 3D visualization of dominant pulmonary nodule. Three-dimensional visualization of the dominant pulmonary nodule within the lung volume. Please click here to view a larger version of this figure.
This research introduces a unique approach for creating a complete three-dimensional (3D) reconstruction of the entire lung, employing advanced medical image processing techniques to delineate the lung's 3D shape amidst the context of a full chest scan. This technique offers a more precise and thorough depiction of the spatial arrangement and radiological characteristics of early multiple nodules across the entire lung. This study makes a valuable contribution to enhancing the accuracy and efficacy of diagnostic and treatment strategies for individuals with early multiple pulmonary nodules.
Critical steps
In this study, several critical steps were identified as essential for the success of the protocol: (1) Sorting and arranging DICOM scan sequence coordinates to generate an accurate 3D volume of the lung scan (step 1.2.2); (2) Isotropic transformation to ensure the correct aspect ratio of the 3D volume, which is crucial for subsequent 3D reconstruction (step 1.3.4); (3) Reconstruction of the entire lung using an early multiple pulmonary nodules model, enabling the identification of the dominant pulmonary nodule area (step 4.1); (4) Detailed visualization and examination of the local area containing the dominant lesion (step 5.2).
Modifications and troubleshooting
The segmentation of lung tissue structures may be affected by the grayscale threshold offset in the scanning sequence, potentially resulting in inaccurate image segmentation in some scans. In cases of inaccurate segmentation, a separate filter (repeating step 3) can be designed to obtain precise lung tissue contours. Maintaining the highest precision in isovoxel transformation16,17is crucial to ensure the accurate utilization of data. These steps are expected to become more intelligent and automated in the future. With the advancement of large-scale medical imaging models, precise contour identification through computer vision is also an important direction for future development11.
Limitations
Simplified implementation of lung contour extraction may lead to errors at the boundary of the lung's 3D contour, potentially affecting the visualization of small nodules near the lung's edge. However, the impact of this limitation is minimal when visualizing the dominant lesion area in cases of multiple pulmonary nodules.
Significance with respect to existing methods
Compared to computer vision approaches, this method offers a comprehensive representation of lung tissue structure, including the relationships between multiple pulmonary nodules and lung tissue, while avoiding the issues of false positives and false negatives. Additionally, it effectively filters out signal interference from other tissue structures, leading to more precise and accurate diagnoses with enhanced contrast and clarity.
Future applications
This 3D visualization method holds substantial potential for various clinical applications, such as facilitating doctor-patient communication, enabling precise diagnosis, supporting data-driven evidence-based classification, aiding in treatment planning, and evaluating prognosis. It can assist in preoperative planning, provide intraoperative navigation for the surgical removal of multiple lung nodules, and monitor changes in nodule size and shape over time to assess treatment effectiveness. Overall, it has the capacity to enhance clinical decision-making in the diagnosis and treatment of multiple pulmonary nodules.
The authors have nothing to disclose.
This publication was supported by the fifth national traditional Chinese medicine clinical excellent talents research program organized by the National Administration of Traditional Chinese Medicine. The official network link is http://www.natcm.gov.cn/renjiaosi/zhengcewenjian/2021-11-04/23082.html.
MATLAB | MathWorks | 2022B | Computing and visualization |
Tools for Modeling | Intelligent Entropy | PulmonaryNodule V1.0 | Beijing Intelligent Entropy Science & Technology Co Ltd. Modeling for CT/MRI fusion |