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Published on: June 21, 2024
Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia
Yabin Liu1, Yimin Wang1, Ya Shu1
1Clinical Medical College and The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan 610500, China.
Abstract:
This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis and pneumonia, in order to provide a certain reference basis for clinical identification. In this study, 30 pulmonary tuberculosis patients and 27 pneumonia patients who were hospitalized were selected as the research objects, and they were divided into a pulmonary tuberculosis group and a pneumonia group. MRI examination based on noise reduction algorithms was used to observe and compare the signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) of the images. In addition, the apparent diffusion coefficient (ADC) value for the diagnosis efficiency of lung parenchymal lesions was analyzed, and the best b value was selected. The results showed that the MRI image after denoising by the deep convolutional neural network (DCNN) algorithm was clearer, the edges of the lung tissue were regular, the inflammation signal was higher, and the SNR and CNR were better than before, which were 119.79 versus 83.43 and 12.59 versus 7.21, respectively. The accuracy of MRI based on a deep learning algorithm in the diagnosis of pulmonary tuberculosis and pneumonia was significantly improved (96.67% vs. 70%, 100% vs. 62.96%) (P < 0.05). With the increase in b value, the CNR and SNR of MRI images all showed a downward trend (P < 0.05). Therefore, it was found that the shadow of tuberculosis lesions under a specific sequence was higher than that of pneumonia in the process of identifying tuberculosis and pneumonia, which reflected the importance of deep learning MRI images in the differential diagnosis of tuberculosis and pneumonia, thereby providing reference basis for clinical follow-up diagnosis and treatment.
Insights
Deep learning enhances magnetic resonance imaging (MRI) for diagnosing pulmonary tuberculosis and pneumonia. This improved accuracy in identifying lung lesions offers a valuable tool for clinical differential diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Radiology
Background:
- Accurate differential diagnosis between pulmonary tuberculosis and pneumonia is crucial for effective treatment.
- Traditional imaging methods may have limitations in distinguishing between these conditions.
- Deep learning offers potential for improving image analysis and diagnostic accuracy.
Purpose of the Study:
- To evaluate the diagnostic value of deep learning-based magnetic resonance imaging (MRI) for differentiating pulmonary tuberculosis and pneumonia.
- To assess the impact of deep convolutional neural network (DCNN) denoising on MRI image quality and diagnostic performance.
- To analyze the influence of apparent diffusion coefficient (ADC) values and b-values on diagnostic efficiency.
Main Methods:
- Retrospective analysis of MRI scans from 30 pulmonary tuberculosis patients and 27 pneumonia patients.
- Application of deep convolutional neural network (DCNN) algorithms for noise reduction in MRI images.
- Comparison of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) before and after denoising.
- Analysis of apparent diffusion coefficient (ADC) values and selection of optimal b-values for lesion characterization.
Main Results:
- DCNN-based denoising significantly improved MRI image clarity, SNR (119.79 vs. 83.43), and CNR (12.59 vs. 7.21).
- Diagnostic accuracy for pulmonary tuberculosis and pneumonia improved substantially with deep learning-based MRI (96.67% vs. 70% and 100% vs. 62.96%, respectively).
- Increasing b-values led to a downward trend in CNR and SNR, indicating an optimal b-value range for diagnosis.
Conclusions:
- Deep learning significantly enhances MRI for the differential diagnosis of pulmonary tuberculosis and pneumonia.
- Improved image quality and diagnostic accuracy provided by DCNN-assisted MRI are valuable for clinical decision-making.
- The study highlights the importance of deep learning in improving the identification of lung lesions and guiding patient management.
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