Name
Abstract | ||
|---|---|---|
The ability of dual-energy computed-tomographic (CT) systems to determine the concentration of constituent materials in a mixture, known as material decomposition, is the basis for many of dual-energy CT's clinical applications. However, the complex composition of tissues and organs in the human body poses a challenge for many material decomposition methods, which assume the presence of only two, or at most three, materials in the mixture. We developed a flexible, modelbased method that extends dual-energy CT's core material decomposition capability to handle more complex situations, in which it is necessary to disambiguate among and quantify the concentration of a larger number of materials. The proposed method, named multi-material decomposition (MMD), was used to develop two image analysis algorithms. The first was virtual un- enhancement (VUE), which digitally removes the effect of contrast agents from contrast-enhanced dual-energy CT exams. VUE has the ability to reduce patient dose and improve clinical workflow, and can be used in a number of clinical applications such as CT urography and CT angiography. The second algorithm developed was liver-fat quantification (LFQ), which accurately quantifies the fat concentration in the liver from dual-energy CT exams. LFQ can form the basis of a clinical application targeting the diagnosis and treatment of fatty liver disease. Using image data collected from both clinical from a cohort consisting of 50 patients and from phantoms, the application of MMD to VUE and LFQ yielded quantitatively accurate results when compared against gold standards. Furthermore, consistent results were obtained across all phases of imaging (contrast-free and contrast-enhanced). This is of particular importance since most clinical protocols for abdominal imaging with CT call for multiphase imaging. We conclude that MMD can successfully form the basis of a number of dual-energy CT image analysis algorithms, and has the potential to improve the clinical utility of dual-energy CT in disease management. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TMI.2013.2281719 | IEEE Trans. Med. Imaging |
Keywords | Field | DocType |
computerised tomography,decomposition,diseases,image enhancement,liver,medical image processing,phantoms,CT angiography,CT urography,abdominal imaging,dual-energy CT image analysis algorithms,dual-energy CT image enhancement,dual-energy computed-tomographic systems,fatty liver disease,human body,liver-fat quantification algorithms,model-based method,multimaterial decomposition methods,multiphase imaging,organ composition,patient dose reduction,phantoms,tissue composition,virtual unenhancement algorithms,Computed tomography (CT),dual-energy CT,liver-fat quantification,material decomposition,virtual un-enhancement | Computer vision,Artificial intelligence,Mathematics,Angiography,Disease management | Journal |
Volume | Issue | ISSN |
33 | 1 | 1558-254X |
Citations | PageRank | References |
5 | 0.77 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paulo R S Mendonca | 1 | 5 | 0.77 |
| Peter Lamb | 2 | 20 | 4.33 |
| Dushyant V Sahani | 3 | 23 | 2.81 |