Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study

Area of Science:

• Radiology
• Medical Imaging
• Oncologic Imaging

Background:

• Accurate tumor characterization in medical imaging is vital for diagnosis and treatment planning.
• Computed tomography (CT) parameters, including slice thickness and reconstruction algorithms, can influence image quality and quantitative analysis.
• Understanding these effects is essential for reliable tumor feature extraction.

Purpose of the Study:

• To investigate the impact of computed tomography (CT) slice thickness and reconstruction algorithm on the quantification of image features used for tumor characterization.
• To evaluate how different CT imaging settings affect the measurement of lesion size, shape, and texture in a chest phantom.

Main Methods:

• A chest phantom with 22 lesions of varying sizes, shapes, and densities was scanned using CT.
• Raw data were reconstructed with three slice thicknesses (1.25, 2.5, 5 mm) and two reconstruction kernels (lung, standard).
• Fourteen image features were calculated, and differences due to CT parameters were analyzed using linear regression.

Main Results:

• All 14 quantified image features showed significant differences between 1.25 mm and 5 mm slice thickness images.
• Thinner slices (1.25 and 2.5 mm) provided better quantification for volume, density mean, density standard deviation (SD), and texture features (GLCM energy, homogeneity).
• Lung reconstruction improved density mean, while standard reconstruction improved density SD, with no significant differences for some shape features.

Conclusions:

• CT slice thickness and reconstruction algorithm significantly alter the quantification of image features for tumor characterization.
• CT images acquired with different slice thicknesses (e.g., 1.25/2.5 mm vs. 5 mm) should not be used interchangeably for quantitative analysis.
• Reconstruction kernel choice impacts density-based features, highlighting the need for standardized protocols in quantitative CT imaging.