Image Quality Enhancement for Digital Breast Tomosynthesis: High-Density Object Artifact Reduction

Area of Science:

• Medical Imaging
• Radiology
• Computer-Aided Diagnosis

Background:

• Breast cancer remains a leading cause of mortality in women, necessitating effective early detection methods.
• Digital breast tomosynthesis (DBT) is a key imaging modality for early breast cancer diagnosis.
• High-density object artifacts in DBT images can hinder accurate diagnosis.

Purpose of the Study:

• To analyze the causes of high-density object artifacts in DBT images.
• To propose and evaluate novel reconstruction methods for mitigating these artifacts.
• To enhance the diagnostic quality of DBT images.

Main Methods:

• Developed a reprojection-based segmentation algorithm for high-density object projection data.
• Implemented an iterative reconstruction algorithm utilizing volume expansion.
• Validated the methods using simulation data and human breast data with artificial surgical needles.

Main Results:

• The proposed algorithm effectively reduces the distortion of shape, size, and position of high-density objects.
• Significant reduction in the influence of artifacts was observed.
• Overall improvement in the quality of reconstructed DBT images was achieved.

Conclusions:

• The developed reconstruction methods effectively address high-density object artifacts in DBT.
• Improved image quality can enhance the diagnostic accuracy and utility of DBT.
• This work aims to promote wider adoption and effectiveness of DBT in clinical practice.