A Flexible Method for Multi-Material Decomposition of Dual-Energy CT Images

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, model-based 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 unenhancement (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 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 multi-phase 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.

Quantification of the Fat Fraction in the Liver Using Dual-Energy Computed Tomography and Multimaterial Decomposition

“…Hepatic fat fraction calculation was possible with MMD, as the LFQ algorithm uses fat, liver tissue, and blood in the material basis and also considers iodinated contrast medium by applying the virtual unenhancement image in the case of postcontrast DECT images. [37][38][39] On the other hand, HFF quantification by MD of postcontrast DECT scan, which considers only 2 material bases of fat and soft tissue, was not possible because HFF value would be negative. This was mainly because iodinated contrast medium confounds measurements by an increase in attenuation with higher iodine concentrations, that is, an inverse effect to fat.…”

Image reconstruction and scan configurations enabled by optimization-based algorithms in multispectral CT

“…There also exist methods that were developed based upon a linear X-ray transform for approximately reconstructing images individually or jointly from the data sets and subsequently forming the basis images by linear combination of the reconstructed images [17–19]. Interest exists in developing the one-step inversion approach to reconstructing basis images directly from data collected by inverting the non-linear data model [20–26].…”

Multi-Material Decomposition Using Statistical Image Reconstruction for Spectral CT

“…For example, the organs and tissues of human body under CT scans contain typical basis materials of blood, fat, muscle, water, cortical bone, air and contrast agent [1], [2]. Material attenuation coefficients depend on the energy of the incident photons.…”

“…If uncorrected, this energy dependence causes artifacts in images reconstructed by conventional methods, such as beam-hardening artifacts [3]. This energy dependence also allows the possibility of basis-material decomposition [1], [4]–[8]. Numerous applications of two-material decomposition have been explored, including CT-based attenuation correction for positron emission tomography (PET) [7], [9], beam-hardening artifacts correction [10], [11], and virtual un-enhancement (VUE) CT [1], [8].…”