Constructing Surrogate Lung Ventilation Maps From 4-Dimensional Computed Tomography-Derived Subregional Respiratory...

Area of Science:

• Medical imaging
• Radiotherapy
• Pulmonary function analysis

Background:

• Accurate lung ventilation mapping is crucial for personalized radiation therapy.
• Existing methods often fail to capture the complex spatial and temporal heterogeneity of lung ventilation.
• Four-dimensional computed tomography (4DCT) offers a potential source for detailed respiratory dynamics.

Purpose of the Study:

• To develop and validate a novel 2-stage framework for extracting reliable lung ventilation surrogates from 4DCT.
• To create V_SRD maps integrating mechanical and physiological information for improved ventilation representation.
• To assess the imaging and dosimetric performance of V_SRD maps in comparison to existing methods for functional lung avoidance radiation therapy (FLA-RT).

Main Methods:

• Retrospective analysis of 3 subject cohorts from the Ventilation and Medical Pulmonary Image Registration Evaluation challenge.
• Partitioning lung subregions and tracking subregional respiratory dynamics (SRDs) across respiratory phases using 4DCT.
• Constructing voxel-level V_SRD maps from SRDs and evaluating their performance against reference ventilation imaging (RefVI) and biphasic Jacobian maps.

Main Results:

• V_SRD maps effectively captured temporally varying subregional volume and intensity changes, reflecting functional physiology and pathologies.
• V_SRD maps demonstrated superior imaging performance (median Spearman correlation: 0.561-0.600; median Dice similarity: 0.592-0.626) compared to biphasic Jacobian maps.
• V_SRD-guided FLA-RT plans achieved significantly better dose sparing of high-functioning lung regions than plans based on biphasic Jacobian maps.

Conclusions:

• V_SRD maps provide improved ventilation representations by capturing spatial and temporal heterogeneity, outperforming classical algorithms.
• The framework's ability to extract multidimensional ventilation data from 4DCT images shows promise for enhancing personalized FLA-RT.
• This approach offers a pathway to more precise and effective lung cancer radiotherapy.