Dual-energy CT scanning has significant potential for disease identification and classification. However, it dramatically increases the amount of data collected and therefore impacts the clinical workflow. One way to simplify image review is to fuse CT datasets of different tube energies into a unique blended dataset with desirable properties.A non-linear blending method based on a modified sigmoid function was compared to a standard 0.3 linear blending method. The methods were evaluated in both a liver phantom and patient study. The liver phantom contained six syringes of known CT contrast which were placed in a bovine liver. After scanning at multiple tube currents (45, 55, 65, 75, 85, 95, 105, and 115 mAs for the 140-kV tube), the datasets were blended using both methods. A contrast-to-noise (CNR) measure was calculated for each syringe. In addition, all eight scans were normalized using the effective dose and statistically compared. In the patient study, 45 dual-energy CT scans were retrospectively mixed using the 0.3 linear blending and modified sigmoid blending functions. The scans were compared visually by two radiologists.For the 15, 45, and 64 HU syringes, the non-linear blended images exhibited similar CNR to the linear blended images; however, for the 79, 116, and 145 HU syringes, the non-linear blended images consistently had a higher CNR across dose settings. The radiologists qualitatively preferred the nonlinear blended images of the phantom. In the patient study, the radiologists preferred non-linear blending in 31 of 45 cases with a strong preference in bowel and liver cases.Non-linear blending of dual energy data can provide an improvement in CNR over linear blending and is accompanied by a visual preference for non-linear blended images. Further study on selection of blending parameters and lesion conspicuity in non-linear blended images is being pursued.

Objective To determine the potential for radiation dose reduction using sigmoidally-blended mixed-kV images from dual energy (DE) hepatic CT. Methods Multiple contrast-enhanced, DE (80 kV/140 kV) datasets were reconstructed from 34 patients undergoing clinically-indicated examinations using routine CTDI vol . Noise was inserted in projection-space to simulate six dose levels reflecting 25-100% of the original dose. Three radiologists, blinded to dose, evaluated image preference, image quality, and diagnostic confidence (scale 1 to 5) using sigmoidally-blended, mixed-kV images, identifying the lowest acceptable dose (both image quality and confidence scores ≥4). At this lowest acceptable dose, the sigmoidal, 0.5 and 0.3 linear blended images were ranked in order of preference.Results Radiation dose level correlated with image preference (correlation coefficients = 0.94, 0.81, 0.94). However, 82% (28/34) and 97% (33/34) of examinations corresponding to dose reductions of 45% and 30%, respectively, yielded acceptable image quality and confidence for all three radiologists. These frequencies were similar whether or not a lesion was present. Each radiologist had specific preferences between mixed-kV image display techniques (p≤0.006), with two most often preferring sigmoidally-blended images. Conclusions There is potential for further dose reduction utilizing DE hepatic CT. Radiologist visual preference for mixed-kV images is idiosyncratic.

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Nonlinear blending of dual-energy CT data is available on current scanners. Selection of the blending parameters can be time-consuming and challenging. The purpose of this study was to determine if the Contrast-To-Noise Ratio (CNR) may be used ti automatic select of blending parameters. A Bovine liver was built with six syringes filled with varying concentrations of CT contrast yielding six 140kV HU levels (15, 47, 64, 79, 116, and145). The phantom was scanned using 95 mAs @ 140kV and 404mAs @ 80 kV. The 80 and 140 kV datasets were blended using a modified sigmoid (moidal) function which requires two parameters -level and width. Every combination of moidal level and width was applied to the data, and the CNR was calculated as (mean(syringe ROI) -mean(liver ROI)) / STD(water). The maximum CNR was determined for each of the 6 HU levels. Pairs of blended images were presented in a blind manner to observers. Nine comparisons for each of the 6 HU settings were made by a staff radiologist, a resident, and a physicist. For each comparison, the observer selected the more "visually appealing" image. Outcomes from the study were compared using the Fisher Sign Test statistic. Analysis by observer showed a statistical (p<0.01) preference towards the optimal CNR image ranging from 71%-81%. Using moidal settings which provide the maximal CNR within the image is consistent with visually appealing images. Optimization of the viewing parameters of nonlinearly blended dual energy CT data may provide consistency across radiologists and facilitate the clinical review process.