Rational and Objectives
To investigate whether a nonlinear-blending algorithm improves tumor conspicuity and image quality in the evaluation of renal masses at dual-energy computed tomography (DECT) during nephrographic phase of enhancement.
Materials and Methods
The Institutional Review Board approved this retrospective study from archival material from patients consenting to the use of medical records for research purposes. A retrospective review of contrast-enhanced abdominal DECT scans in 45 patients (mean age, 59.5 years; range, 24–84 years) was performed. DECT data were reconstructed using nonlinear and linear blending. A region of interest was located within tumors and adjacent normal parenchyma; attenuation differences and contrast-to-noise ratios (CNRs) were calculated for renal masses on nonlinear- and linear-blended images. The two datasets were subjectively compared in terms of tumor detection and image quality. An exact Wilcoxon’s matched pairs signed rank and marginal homogeneity tests were used to test whether differences in attenuation, CNR, and subjective assessment were greater using nonlinear blending.
Results
The mean difference in attenuation for renal masses and adjacent portion of renal parenchyma was 138.4 Hounsfield units ± 28.9 SD using nonlinear blending, and 121.6 HU ± 18.0 SD using linear blending ( P < .001). Mean CNR was 12.6 ± 2.5 SD using nonlinear blending, and 9.6 ± 2.2 SD using 0.3 linear-blended ( P < .001). No significant difference in tumor detection was observed between the two algorithms. Image quality was significantly better ( P < .001) using nonlinear blending.
Conclusion
Compared with standard linear blending, nonlinear-blending algorithm improves tumor conspicuity and image quality in renal masses at DECT evaluation during nephrographic phase of enhancement.
Dual-energy computed tomography (DECT) rose to prominence in the last decade thanks to its clinical value in the analysis of tissues chemical composition in applications such as automated bone removal , kidney stone characterization , gout detection , pulmonary embolism evaluation , endoleak detection , iodine removal and quantification (eg, in pancreatic and renal masses characterization, myocardial viability assessment and intracranial bleeding) . However, DECT imaging has other potential application than composition analysis. Most recently, great interest has been paid to the capability of improving image quality and tumor conspicuity at low-kVp DECT images in some clinical settings, such as hepatic and pancreatic masses evaluation . Operating at two different energy levels involves a continuous dilemma between the two main aspects—contrast resolution and noise characteristics of acquired images (ie, contrast-to-noise ratio; CNR). In particular, low-kVp (80-kVp) scanning provide higher contrast resolution and greater iodine conspicuity but results in excessively noisy images. Conversely, although high-kVp (140-kVp) scanning permits minimal noise levels, it provides lower contrast resolution and iodine conspicuity . A valuable compromise is represented by the possibility on dual-source (DS) DECT systems to blend low- and high-kVp information in a fused image dataset, resulting in a balance of both advantages and disadvantages. The first adopted strategy of image blending on DS-DECT was a linear blending approach in which 140- and 80-kVp images were combined in a fixed ratio. However, although linear blending provides an improvement in image quality, its downside is image contrast and conspicuity of lesions . Thus, as an alternative to linear blending, several nonlinear image-blending strategies have been developed ; preliminary studies showed that the sigmoid blending (referred to as moidal or sigmoid modified blending) is preferred by radiologists . To date, only a few studies in vivo, in which nonlinear and linear blending were compared in hepatic lesions, exist .
The aim of our study was to investigate whether a nonlinear-blending algorithm improves tumor conspicuity and image quality at DECT in renal masses evaluation during the nephrographic phase of enhancement.
Materials and methods
Subjects
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DECT Data Acquisition
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DECT Data Interpretation
Quantitative analysis
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Qualitative analysis
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Tumor detection
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Image quality
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Statistical Analysis
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Results
Subjects
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Quantitative Analysis
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Table 1
Attenuation Values of Renal Masses, Normal Renal Parenchyma, Kidney-to-tumor Attenuation, and CNR Measured at Nonlinear- and 0.3 Linear-blended DECT Images
Patient No. Nonlinear-blended DECT Images 0.3 Linear-blended DECT Images Renal Attenuation (HU) Renal Masses (HU) Difference (HU) CNR Renal Attenuation (HU) Renal Masses (HU) Difference (HU) CNR 1 268 78 190 19.0 201 67 134 16.7 2 270 86 184 14.1 155 69 86 5.7 3 243 74 169 14.1 183 55 128 10.6 4 205 81 124 12.4 175 68 107 9.7 5 182 56 126 12.6 152 43 109 7.7 6 179 43 136 9.7 157 32 125 8.3 7 243 64 179 14.9 192 44 148 11.3 8 201 61 140 14.2 169 42 127 10.5 9 212 72 140 15.5 188 53 135 13.5 10 173 37 136 15.1 171 25 146 11.2 11 222 58 164 14.9 191 47 144 9.6 12 243 82 161 16.1 182 65 117 9.0 13 232 31 201 16.7 179 24 155 11.1 14 188 52 136 11.3 158 46 112 8.6 15 244 52 192 16.0 189 38 151 12.6 16 202 57 145 14.5 162 34 128 9.8 17 265 71 194 14.9 198 54 144 10.3 18 228 30 198 15.9 170 27 143 15.2 19 197 92 105 8.7 164 77 87 6.7 20 232 62 170 15.4 192 41 151 11.6 21 174 50 124 12.4 157 31 126 10.5 22 192 37 155 14.0 175 28 147 11.3 23 182 69 114 11.4 162 57 105 8.7 24 174 38 136 10.4 154 29 125 8.8 25 171 51 120 10.9 147 38 109 7.7 26 169 58 111 11.1 153 44 109 9.0 27 182 49 133 14.7 163 34 129 10.7 28 176 79 97 10.7 161 67 94 8.5 29 165 60 105 13.1 154 53 101 11.2 30 168 48 120 10.9 147 38 109 9.9 31 172 36 136 11.3 154 26 128 8.5 32 164 58 106 9.6 149 45 104 8.0 33 186 94 92 9.2 157 72 85 7.0 34 179 57 122 8.7 162 46 116 6.8 35 174 45 129 14.3 159 33 126 12.5 36 182 63 119 13.2 161 49 112 7.4 37 176 36 140 12.7 152 28 124 9.5 38 187 48 139 11.6 166 36 130 9.2 39 169 59 110 10.0 149 47 102 7.2 40 177 64 113 8.6 160 51 109 6.8 41 161 48 113 10.2 149 34 115 8.8 42 164 38 126 11.4 153 29 124 9.5 43 178 49 129 10.7 161 37 124 8.2 44 190 74 116 9.6 169 55 114 8.7 45 171 36 135 11.2 154 26 128 9.1
CNR, contrast-to-noise ratio; DECT, dual-energy computed tomography; HU, Hounsfield units.
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Qualitative Analysis
Tumor detection
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Image quality
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Discussion
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