Deformation analysis to detect and quantify active lesions in three-dimensional medical image sequences

Area of Science:

• Medical imaging analysis
• Quantitative medical research
• Biomedical engineering

Background:

• Precise evaluation of temporal lesion volume variations is critical for pharmaceutical trials, treatment decisions, and patient monitoring.
• Existing methods often require detailed lesion segmentation, which can be challenging and time-consuming.

Purpose of the Study:

• To develop and validate a novel volumetric analysis technique for precise quantification of lesion volume changes.
• To enable automated detection and measurement of evolving lesions with minimal user input.
• To differentiate between tissue transformation and mass changes within lesions.

Main Methods:

• Combines rigid registration of 3-D medical images, nonrigid deformation computation, and flow-field analysis.
• Utilizes an approximate region of interest (ROI) designation, eliminating the need for precise lesion segmentation.
• Distinguishes tissue transformation (intensity changes) from expansion/contraction (mass changes).

Main Results:

• The technique successfully detects evolving lesions and quantitatively measures volume variations.
• Demonstrated effectiveness on synthesized volumetric image sequences.
• Applied to a real patient case of multiple sclerosis (MS) to quantify in vivo mass effect.

Conclusions:

• The developed technique offers a robust and efficient method for analyzing temporal lesion volume dynamics.
• Its ability to work with approximate ROIs makes it highly applicable in clinical settings and research.
• This approach advances the quantitative assessment of disease progression and treatment response in conditions like multiple sclerosis.