Improving material identification using multi-energy CT image data
Disclosed are methods for identification and quantification of a number of different materials within an object using one or more multi-energy CT imaging devices and the image data sets produced therefrom. Identification and quantification of different materials is achieved by using the following three properties: solve only for sparse solutions; separate the soft tissue problem from the dense material problem; and use a combinatorial approach to allow for simple application of different constraints to different combinations of materials. Also disclosed are one or more computer program products, computer systems or computer implemented methods for the identification of multiple materials within an object.
1. A computer-implemented method for identifying and/or quantifying, and presenting one or more materials represented by data produced by a multi-energy CT system using three or more energy bands using a computer program product, the method comprising:
a) receiving, directly or indirectly, an image data set from a multi-energy CT imaging apparatus using three or more energy bands and storing said data on a data storage medium;
b) calculating, for each of a plurality of reconstructed voxels contained within the data set, a sparse solution for a maximum number of material combinations using 0-norm minimisation using a processor;
c) storing said sparse solutions on a data storage medium;
d) applying rejection criteria to the sparse solutions of b) to identify one or more materials, wherein the step of applying rejection criteria to the sparse solutions includes the steps of identifying combination of material pre-determined not to be found in the same voxel and rejecting said combinations and/or setting acceptable solution ranges for each material, and rejecting any sub-problem with a solution outside these ranges; and
e) presenting the materials identified in d) via an interface.
2. The computer-implemented method of claim 1 , wherein, the step of calculating the sparse solutions further includes the steps of:
i) determining all combinations of the maximum number of materials present in the CT image data set;
ii) for each combination, constructing a reduced material matrix Mr;
iii) calculating a non-negative linear least squares solution xr for the sub problem Mrxr=one voxel;
iv) determining which solution from iii) has the smallest least square error out of all the tested combinations determined in i);
v) selecting the solution determined in iv) as the best sparse solution.
3. The method as claimed in claim 1 , wherein the method includes at least one of identifying and quantifying, and presenting data from five or more different materials.
4. The method of claim 1 , wherein the multi-energy CT imaging apparatus uses between three-eight energy bands.
5. The method of claim 1 , wherein the method includes decomposing data from six different materials using four energy bands.
6. The method of claim 1 , wherein the method includes the step of de-noising the data/images for each energy range prior to decomposition.
7. The method of claim 6 , wherein the step of de-noising includes applying a cylindrical median filter for reducing high frequency noise.
8. The method of claim 1 , wherein the method includes the further step of enforcing one or more constraints to the individual material combinations.
9. The method as claimed in claim 1 , further comprising:
a) scanning an animal, human or part thereof using the multi-energy CT system using three or more energy bands to produce the image data set or receiving a multi-energy CT image data set produced using three or more energy bands for the animal, human or part thereof;
b) quantifying or identifying the one or more materials based on the CT image data set.
10. The method of claim 9 , wherein the material to be identified or quantified is selected from plaque, cancer, cancerous tumors or markers and biological identifiers thereof, components associated with the inflammatory process or infection, bone or cartilage, pathogens, crystals, lipids or fats.
11. The method as claimed in claim 9 , wherein the method includes the step of administering a nanocontrast agent targeted to the material to be identified to the animal, human or part thereof prior to scanning.
12. The method of claim 11 , wherein the nanocontrast agents are selected from agents comprising nanoparticles of one or more metals.
13. The method of claim 11 , wherein the nanocontrast agent further includes a biologically active component.
14. A system for identifying and/or quantifying and presenting one or more materials represented by data produced by a multi-energy CT system using three or more energy bands, the system comprising:
a) one or more processor for directly or indirectly receiving an image data set from a multi-energy CT imaging apparatus using three or more energy bands;
b) one or more data storage medium for storing said data;
c) wherein, the one or more processor calculates, for each of a reconstructed voxel contained within the data, a sparse solution for a maximum number of material combinations using 0-norm minimisation;
d) further wherein, the one or more data storage medium stores said sparse solutions;
e) further wherein, the one or more processor applies rejection criteria to the sparse solutions of c) to identify one or more specific materials, wherein the step of applying rejection criteria to the sparse solutions includes the steps of identifying combinations of materials pre-determined not to be found in the same voxel and rejecting said combinations and/or setting acceptable solution ranges for each material, and rejecting any sub-problem with a solution outside these ranges; and
f) further wherein, an interface presents the materials identified in e).