4DCT Ventilation Map Construction Using Biomechanics-base Image Registration and Enhanced Air Segmentation

Area of Science:

• Medical Physics
• Radiotherapy
• Pulmonary Imaging

Background:

• Lung radiation therapy (RT) planning often assumes uniform lung function, which is inaccurate for non-small cell lung cancer (NSCLC) patients with co-existing pulmonary dysfunctions.
• Regional variations in lung perfusion and ventilation, especially in smokers, lead to inhomogeneous lung function, increasing risks of pulmonary toxicity.
• Adaptive RT strategies avoiding highly functional lung areas can mitigate treatment-related morbidity.

Purpose of the Study:

• To develop a novel, accurate method for calculating 4D computed tomography (4DCT) ventilation maps.
• To address limitations in current 4DCT ventilation estimation methods, specifically intensity-based and Jacobian models.
• To enable adaptive RT planning by providing reliable lung function data.

Main Methods:

• Proposed a novel method combining biomechanical model-based registration with an accurate air segmentation algorithm.
• Calculated 4DCT ventilation maps using the developed approach.
• Evaluated the accuracy of air volume distribution and image registration compared to existing methods.

Main Results:

• Successfully developed accurate 4DCT ventilation maps.
• The proposed method demonstrated improved estimations of air volume distribution.
• Biomechanical registration proved more reliable than intensity-based algorithms for ventilation imaging.

Conclusions:

• The novel method provides a more accurate way to calculate 4DCT ventilation maps.
• This advancement supports the development of adaptive radiation therapy for NSCLC, potentially reducing pulmonary toxicity.
• Accurate lung ventilation mapping is crucial for personalized cancer treatment planning.