# Measuring Identification and Quantification Errors in Spectral CT Material Decomposition
## 4 Discussion  
Material misidentification affects the overall sensitivity and specificity of the measurements which is the key requirement of specific identification and quantification of multiple tissue components for spectral molecular imaging. The MARS MD algorithm assumes that each voxel consists of primarily one high-Z material. Therefore, in the segmentation task, once a material is identified in any given voxel, the value for this voxel in other basis materials is assumed to be zero. Therefore, this particular  
Figure 14: Fused images of bovine knee specimens with protocol-1 (**a**) and protocol-2 (**b**) with a gadolinium channel displayed quantitatively using a green color map with concentration ranging from 0 to 50 mg Gd/mL; (**c**) shows a quantified correlation in gadolinium uptake between the two protocols with respect to cartilage thickness (corresponding to arrow directions in (**a**,**b**). It is evident that despite using the same biological specimen, the two imaging protocols are reporting different concentrations. This is due to greater misidentification of gadolinium as water with protocol-2 (refer to Figures 11 and 12a). For more insight about the biological sample, the reader is referred to [9]. Figure 13: A single energy spectral CT image of two bovine knee plugs and material component images for hydroxyapatite, gadolinium, and water obtained from the material decomposition with protocol-1 (**top row**) and protocol-2 (**bottom row**). The window levels are normalized from 0 to 1 for display. analysis method was designed using a sparse solution MD as a reference, only taking into account whether the solution is zero/non-zero in each material and therefore exploits this \"binary property\" and quantifies correct material identification at various material concentrations. However, for MD algorithms that have noise in all material channels (i.e., every voxel contains a non-zero value for all material channels), modification like low-value thresholding or smoothing using techniques such as compressed sensing can be applied. Figures 10 and 11 show that sensitivity of any given material in the post-MD images is density dependent whereas specificity is not, and therefore specificity ideally should be 100% for every voxel of each material at each of the given concentrations. There could be various factors that likely to have an effect on sensitivity, specificity and material mis-quantification. The statistical noise of the system could be one of the factors affecting the sensitivity and specificity of the given material. Noise not only affects the ability to detect low-Z materials but also affects the differentiation of low concentrations of high-Z materials [59]. Another noise related factor affecting material decomposition is pulse pileup [33] which contributes negatively to the energy calibration. It has its origin in photon statistics and also depends on the read-out speed of the detector electronics [60; 61]. Pileup occurs when two or more photons arrive within the same detector region and within the same integration time of the readout electronics. These events appear to be only one thus resulting in an information loss. It also leads to events in low energy ranges being detected which exceeds the maximum possible photon energy which also complicates the process of energy calibration and results in a loss of energy resolution [31]. Low X-ray flux was used with the brass filter protocol (protocol-1) to reduce the probability of the pileup effect whilst providing adequate statistics in a reasonable exposure time as shown in our previous studies [32; 62]. This might be the reason for less material misidentification in protocol-1 than protocol-2. The underestimation of the gadolinium quantity with protocol-2 can be associated with higher gadolinium misidentification as hydroxyapatite. The threshold energy settings for the selection of energy bins could also have a major impact on spectral image quality in terms of image contrast and noise level.