Calculation of strain images of a breast-mimicking phantom from 3D CT image data

Purpose: Elastography is a medical imaging modality to visualize the elasticity of soft tissues. Ultrasound and MRI have been exclusively used for elastography of soft tissues since they can sensitize the tissues’ minute displacements of an order ofμm. It is known that ultrasound and MRI elastography show cancerous tissues with much higher contrast than conventional ultrasound and MRI. To evaluate possibility of combining elastography with x-ray imaging, we have calculated strain images of a breast-mimicking phantom from its 3D CT image data. Methods: We first simulated the x-ray elastography using a FEM model which incorporated both the elasticity and x-ray attenuation behaviors of breast tissues. After validating the x-ray elastography scheme by simulation, we made a breast-mimicking phantom that contained a hard inclusion against soft background. With a micro-CT, we took 3D images of the phantom twice, changing the compressing force to the phantom. From the two 3D phantom images taken with two different compression ratios, we calculated the displacement vector maps that represented the compression-induced pixel displacements. In calculating the displacement vectors, we tracked the movements of image feature patterns from the less-compressed-phantom images to the more-compressed-phantom images using the 3D image correlation technique. We obtained strain images of the phantom by differentiating the displacement vector maps. Results: The FEM simulation has shown that x-ray strain imaging is possible by tracking image feature patterns in the 3D CT images of the breast-mimicking phantom. The experimental displacement and strain images of a breast-mimicking phantom, obtained from the 3D micro-CT images taken with 0%–3% compression ratios, show behaviors similar to the FEM simulation results. The contrast and noise performance of the strain images improves as the phantom compression ratio increases. Conclusions: We have experimentally shown that we can improve x-ray strain image quality by applying 3D image correlation to the two sets of 3D CT images taken with different compression ratios. But, we need further investigations to evaluate the strain imaging performance considering the noise and decorrelation effects as well as the extra dose caused by two scans.