Log In Sign Up

ADJUST: A Dictionary-Based Joint Reconstruction and Unmixing Method for Spectral Tomography

by   Mathé T. Zeegers, et al.

Advances in multi-spectral detectors are causing a paradigm shift in X-ray Computed Tomography (CT). Spectral information acquired from these detectors can be used to extract volumetric material composition maps of the object of interest. If the materials and their spectral responses are known a priori, the image reconstruction step is rather straightforward. If they are not known, however, the maps as well as the responses need to be estimated jointly. A conventional workflow in spectral CT involves performing volume reconstruction followed by material decomposition, or vice versa. However, these methods inherently suffer from the ill-posedness of the joint reconstruction problem. To resolve this issue, we propose `A Dictionary-based Joint reconstruction and Unmixing method for Spectral Tomography' (ADJUST). Our formulation relies on forming a dictionary of spectral signatures of materials common in CT and prior knowledge of the number of materials present in an object. In particular, we decompose the spectral volume linearly in terms of spatial material maps, a spectral dictionary, and the indicator of materials for the dictionary elements. We propose a memory-efficient accelerated alternating proximal gradient method to find an approximate solution to the resulting bi-convex problem. From numerical demonstrations on several synthetic phantoms, we observe that ADJUST performs exceedingly well when compared to other state-of-the-art methods. Additionally, we address the robustness of ADJUST against limited measurement patterns.


page 2

page 3

page 12

page 23

page 25

page 26

page 27


DLIMD: Dictionary Learning based Image-domain Material Decomposition for spectral CT

The potential huge advantage of spectral computed tomography (CT) is its...

Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition

Spectral Computed Tomography (CT) is an emerging technology that enables...

Improved Material Decomposition with a Two-step Regularization for spectral CT

One of the advantages of spectral computed tomography (CT) is it can ach...

Iterative Reconstruction of the Electron Density and Effective Atomic Number using a Non-Linear Forward Model

For material identification, characterization, and quantification, it is...

Target-based Hyperspectral Demixing via Generalized Robust PCA

Localizing targets of interest in a given hyperspectral (HS) image has a...

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Dual energy computed tomography (DECT) imaging plays an important role i...