Spectral diffusion: an algorithm for robust material decomposition of spectral CT data.

Published

Journal Article

Clinical successes with dual energy CT, aggressive development of energy discriminating x-ray detectors, and novel, target-specific, nanoparticle contrast agents promise to establish spectral CT as a powerful functional imaging modality. Common to all of these applications is the need for a material decomposition algorithm which is robust in the presence of noise. Here, we develop such an algorithm which uses spectrally joint, piecewise constant kernel regression and the split Bregman method to iteratively solve for a material decomposition which is gradient sparse, quantitatively accurate, and minimally biased. We call this algorithm spectral diffusion because it integrates structural information from multiple spectral channels and their corresponding material decompositions within the framework of diffusion-like denoising algorithms (e.g. anisotropic diffusion, total variation, bilateral filtration). Using a 3D, digital bar phantom and a material sensitivity matrix calibrated for use with a polychromatic x-ray source, we quantify the limits of detectability (CNR = 5) afforded by spectral diffusion in the triple-energy material decomposition of iodine (3.1 mg mL(-1)), gold (0.9 mg mL(-1)), and gadolinium (2.9 mg mL(-1)) concentrations. We then apply spectral diffusion to the in vivo separation of these three materials in the mouse kidneys, liver, and spleen.

Full Text

Duke Authors

Cited Authors

  • Clark, DP; Badea, CT

Published Date

  • November 2014

Published In

Volume / Issue

  • 59 / 21

Start / End Page

  • 6445 - 6466

PubMed ID

  • 25296173

Pubmed Central ID

  • 25296173

Electronic International Standard Serial Number (EISSN)

  • 1361-6560

International Standard Serial Number (ISSN)

  • 0031-9155

Digital Object Identifier (DOI)

  • 10.1088/0031-9155/59/21/6445

Language

  • eng