3D Imaging Technique for Lungs Could Help Predict Surgery Outcomes Precisely

This could spell good news for people suffering from chronic obstructive pulmonary disease (COPD). In a new finding that could help offer better treatment, researchers have been successful in producing a 3D image that shows the flow of oxygen and carbon dioxide within the lungs. This is the first time that scientists have produced such detailed 3D images that present clear insights into the condition of the lungs.

According to Troels Johansen, PhD student at the Aarhus University’s Department of Clinical Medicine in Denmark, his team is the first to develop a 3D model that helps pulmonologists understand the precise nature of gas transfer occurring inside the lungs. This project saw researchers from the Aarhus University work in close collaboration with those from the Harvard Medical School.

How does this new method promise to make treatment for COPD and other respiratory conditions more effective? With every breath, we are taking in oxygen and expelling carbon dioxide. This gas transport is conducted between our blood and the air present inside our lungs. In order to stay alive, this exchange of gases is crucial. However, what also becomes crucial, as a result, is to understand how seamlessly this exchange is occurring within the lungs, especially in the case of people suffering from lung disease and COPD or those on a respirator.

The 3D mapping technique for lungs is based on a sophisticated mathematical model whereas the actual 3D images are developed using positron emission tomography (PET) scans. The researchers add that the 3D imaging model can be employed for different patient groups. In a practical scenario, for instance, if a patient has a tumor in the lung, the 3D map will make it easier for doctors to predict how the removal of the tumor may affect breathing.

In the case of patients suffering from COPD, too, using a 3D map will help doctors determine if a surgery would help improve the patient’s condition or not. The outcomes and risks associated with high-risk operations will be easier to predict.

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