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

Clark, Darin P

Badea, Cristian T

England

2015-12-15T19:22:40Z

2014-11-07

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.

http://www.ncbi.nlm.nih.gov/pubmed/25296173

1361-6560

https://hdl.handle.net/10161/11180

eng

IOP Publishing

Phys Med Biol

10.1088/0031-9155/59/21/6445

Algorithms

Animals

Calibration

Contrast Media

Diffusion

Gadolinium

Gold

Humans

Image Processing, Computer-Assisted

Iodine

Kidney

Liver

Mice

Mice, Inbred C57BL

Phantoms, Imaging

Spleen

Tomography, X-Ray Computed

X-Rays

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

Journal article

Badea, Cristian T|0000-0002-1850-2522

http://www.ncbi.nlm.nih.gov/pubmed/25296173

6445

6466

21

Clinical Science Departments

Duke

Duke Cancer Institute

Institutes and Centers

Radiology

School of Medicine

Published

59