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Related Concept Videos

Pulmonary Tuberculosis I01:29

Pulmonary Tuberculosis I

Tuberculosis, often called TB, is a contagious illness primarily caused by Mycobacterium tuberculosis. It mainly affects the lung parenchyma but can also impact other body parts.
Causative Organism
The primary infectious agent causing tuberculosis is Mycobacterium tuberculosis, a slow-growing, acid-fast, aerobic rod that exhibits sensitivity to heat and ultraviolet light. Instances of Mycobacterium bovis and Mycobacterium avium contributing to the development of TB infection are rare.
Mode of...
Pulmonary Tuberculosis II01:28

Pulmonary Tuberculosis II

Tuberculosis, or TB, is a bacterial infectious disease caused by Mycobacterium tuberculosis. While its primary impact is on the lungs, leading to pulmonary tuberculosis, it can also affect various other organs, a condition referred to as extrapulmonary tuberculosis.
Here is a detailed explanation of its pathophysiology:
Transmission: The process begins when a person inhales droplet nuclei containing M. tuberculosis. These are typically released into the air when an individual with pulmonary or...
Pulmonary Tuberculosis III01:31

Pulmonary Tuberculosis III

Tuberculosis (TB) is a contagious infection primarily affecting the lung parenchyma but which can also affect other body parts. TB can be classified based on disease development, presentation, and the affected anatomical site.
The first classification is based on the development of the disease, and it includes the following categories:
Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
Pulmonary Tuberculosis V01:28

Pulmonary Tuberculosis V

Medical management of tuberculosis (TB) patients involves a comprehensive approach that includes diagnosis, treatment, and monitoring. The specific strategies can vary depending on the type of tuberculosis (latent or active), the patient's overall health status, and other considerations.
Latent tuberculosis infection occurs when TB bacteria are present in a person's body, but are not causing illness or symptoms. It is not contagious, and preventive treatment is crucial to avoid the progression...

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Related Experiment Video

Updated: May 23, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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BPGAN: Bidirectional CT-to-MRI prediction using multi-generative multi-adversarial nets with spectral normalization

Liming Xu1, Xianhua Zeng1, He Zhang1

  • 1Chongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 23, 2020
PubMed
Summary

This study introduces BPGAN, a novel bidirectional prediction model for generating Magnetic Resonance Imaging (MRI) from Computed Tomography (CT) images and vice-versa. BPGAN enhances medical image synthesis for radiotherapy planning without radiation exposure.

Keywords:
Bidirectional predictionCross modalityGenerative adversarial netsPathological invarianceSpectral normalization

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiotherapy Planning

Background:

  • Medical imaging modalities like MRI and CT are crucial for clinical diagnosis and research.
  • CT involves ionizing radiation, while MRI is safer but more costly and time-consuming.
  • Accurate image synthesis between MRI and CT is vital for applications like radiotherapy planning.

Purpose of the Study:

  • To develop a bidirectional prediction model for synthesizing CT images from MRI and vice-versa.
  • To address the lack of existing bidirectional models for cross-modality image translation.
  • To improve image quality and anatomical structure preservation in synthesized medical images.

Main Methods:

  • Proposed a multi-generative, multi-adversarial network (BPGAN) for bidirectional image prediction.
  • Employed cycle consistency to learn nonlinear mappings between MRI and CT domains.
  • Incorporated pathological prior information and an edge retention metric for enhanced feature generation and structural integrity.
  • Utilized spectral normalization and localization techniques to optimize network performance and reduce generalization error.

Main Results:

  • BPGAN demonstrated superior prediction performance compared to state-of-the-art methods.
  • Achieved significant average increments in Mean Absolute Error (MAE) and Structural Similarity Index (SSIM) on benchmark datasets.
  • Successfully generated high-fidelity images preserving anatomical structures and pathological details.

Conclusions:

  • BPGAN offers an effective solution for bidirectional medical image synthesis between MRI and CT.
  • The model holds promise for improving radiotherapy planning and reducing patient exposure to radiation.
  • The proposed techniques advance the field of cross-modality medical image translation.