"x-Map 2.0" for Edema Signal Enhancement for Acute Ischemic Stroke Using Non-Contrast-Enhanced Dual-Energy Computed Tomography

Objectives A novel imaging technique ("X-map") has been developed to identify acute ischemic lesions for stroke patients using non-contrast-enhanced dual-energy computed tomography (NE-DE-CT). Using the 3-material decomposition technique, the original X-map ("X-map 1.0") eliminates fat and bone from the images, suppresses the gray matter (GM)-white matter (WM) tissue contrast, and makes signals of edema induced by severe ischemia easier to detect. The aim of this study was to address the following 2 problems with the X-map 1.0: (1) biases in CT numbers (or artifacts) near the skull of NE-DE-CT images and (2) large intrapatient and interpatient variations in X-map 1.0 values. Materials and Methods We improved both an iterative beam-hardening correction (iBHC) method and the X-map algorithm. The new iBHC (iBHC2) modeled x-ray physics more accurately. The new X-map ("X-map 2.0") estimated regional GM values - thus, maximizing the ability to suppress the GM-WM contrast, make edema signals quantitative, and enhance the edema signals that denote an increased water density for each pixel. We performed a retrospective study of 11 patients (3 men, 8 women; mean age, 76.3 years; range, 68-90 years) who presented to the emergency department with symptoms of acute stroke. Images were reconstructed with the old iBHC (iBHC1) and the iBHC2, and biases in CT numbers near the skull were measured. Both X-map 2.0 maps and X-map 1.0 maps were computed from iBHC2 images, both with and without a material decomposition-based edema signal enhancement (ESE) process. X-map values were measured at 5 to 9 locations on GM without infarct per patient; the mean value was calculated for each patient (we call it the patient-mean X-map value) and subtracted from the measured X-map values to generate zero-mean X-map values. The standard deviation of the patient-mean X-map values over multiple patients denotes the interpatient variation; the standard deviation over multiple zero-mean X-map values denotes the intrapatient variation. The Levene F test was performed to assess the difference in the standard deviations with different algorithms. Using 5 patient data who had diffusion weighted imaging (DWI) within 2 hours of NE-DE-CT, mean values at and near ischemic lesions were measured at 7 to 14 locations per patient with X-map images, CT images (low kV and high kV), and DWI images. The Pearson correlation coefficient was calculated between a normalized increase in DWI signals and either X-map or CT. Results The bias in CT numbers was lower with iBHC2 than with iBHC1 in both high- and low-kV images (2.5 ± 2.0 HU [95% confidence interval (CI), 1.3-3.8 HU] for iBHC2 vs 6.9 ± 2.3 HU [95% CI, 5.4-8.3 HU] for iBHC1 with high-kV images, P < 0.01; 1.5 ± 3.6 HU [95% CI, -0.8 to 3.7 HU] vs 12.8 ± 3.3 HU [95% CI, 10.7-14.8 HU] with low-kV images, P < 0.01). The interpatient variation was smaller with X-map 2.0 than with X-map 1.0, both with and without ESE (4.3 [95% CI, 3.0-7.6] for X-map 2.0 vs 19.0 [95% CI, 13.3-22.4] for X-map 1.0, both with ESE, P < 0.01; 3.0 [95% CI, 2.1-5.3] vs 12.0 [95% CI, 8.4-21.0] without ESE, P < 0.01). The intrapatient variation was also smaller with X-map 2.0 than with X-map 1.0 (6.2 [95% CI, 5.3-7.3] vs 8.5 [95% CI, 7.3-10.1] with ESE, P = 0.0122; 4.1 [95% CI, 3.6-4.9] vs 6.3 [95% CI, 5.5-7.6] without ESE, P < 0.01). The best 3 correlation coefficients (R) with DWI signals were -0.733 (95% CI, -0.845 to -0.560, P < 0.001) for X-map 2.0 with ESE, -0.642 (95% CI, -0.787 to -0.429, P < 0.001) for high-kV CT, and -0.609 (95% CI, -0.766 to -0.384, P < 0.001) for X-map 1.0 with ESE. Conclusion Both of the 2 problems outlined in the objectives have been addressed by improving both iBHC and X-map algorithm. The iBHC2 improved the bias in CT numbers and the visibility of GM-WM contrast throughout the brain space. The combination of iBHC2 and X-map 2.0 with ESE decreased both intrapatient and interpatient variations of edema signals significantly and had a strong correlation with DWI signals in terms of the strength of edema signals.