Dual-Source Linear Energy Prediction (LINE-P) Model in the Context of WSNs.
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ABSTRACT:
SUBMITTER: Ahmed F
PROVIDER: S-EPMC5539532 | biostudies-other | 2017 Jul
REPOSITORIES: biostudies-other
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Project description:To evaluate the image quality of virtual monochromatic images synthesized from dual-source dual-energy computed tomography (CT) in comparison with conventional polychromatic single-energy CT for the same radiation dose.In dual-energy CT, besides the material-specific information, one may also synthesize monochromatic images at different energies, which can be used for routine diagnosis similar to conventional polychromatic single-energy images. In this work, the authors assessed whether virtual monochromatic images generated from dual-source CT scanners had an image quality similar to that of polychromatic single-energy images for the same radiation dose. First, the authors provided a theoretical analysis of the optimal monochromatic energy for either the minimum noise level or the highest iodine contrast to noise ratio (CNR) for a given patient size and dose partitioning between the low- and high-energy scans. Second, the authors performed an experimental study on a dual-source CT scanner to evaluate the noise and iodine CNR in monochromatic images. A thoracic phantom with three sizes of attenuating rings was used to represent four adult sizes. For each phantom size, three dose partitionings between the low-energy (80 kV) and the high-energy (140 kV) scans were used in the dual-energy scan. Monochromatic images at eight energies (40 to 110 keV) were generated for each scan. Phantoms were also scanned at each of the four polychromatic single energy (80, 100, 120, and 140 kV) with the same radiation dose.The optimal virtual monochromatic energy depends on several factors: phantom size, partitioning of the radiation dose between low- and high-energy scans, and the image quality metrics to be optimized. With the increase of phantom size, the optimal monochromatic energy increased. With the increased percentage of radiation dose on the low energy scan, the optimal monochromatic energy decreased. When maximizing the iodine CNR in monochromatic images, the optimal energy was lower than that when minimizing noise level. When the total radiation dose was equally distributed between low and high energy in dual-energy scans, for minimum noise, the optimal energies were 68, 71, 74, and 77 keV for small, medium, large, and extra-large (xlarge) phantoms, respectively; for maximum iodine CNR, the optimal energies were 66, 68, 70, 72 keV. With the optimal monochromatic energy, the noise level was similar to and the CNR was better than that in a single-energy scan at 120 kV for the same radiation dose. Compared to an 80 kV scan, however, the iodine CNR in monochromatic images was lower for the small, medium, and large phantoms.In dual-source dual-energy CT, optimal virtual monochromatic energy depends on patient size, dose partitioning, and the image quality metric optimized. With the optimal monochromatic energy, the noise level was similar to and the iodine CNR was better than that in 120 kV images for the same radiation dose. Compared to single-energy 80 kV images, the iodine CNR in virtual monochromatic images was lower for small to large phantom sizes.
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Project description:To determine the accuracy of iodine quantification with dual energy computed tomography (DECT) in two high-end CT systems with different spectral imaging techniques.Five tubes with different iodine concentrations (0, 5, 10, 15, 20 mg/ml) were analysed in an anthropomorphic thoracic phantom. Adding two phantom rings simulated increased patient size. For third-generation dual source CT (DSCT), tube voltage combinations of 150Sn and 70, 80, 90, 100 kVp were analysed. For dual layer CT (DLCT), 120 and 140 kVp were used. Scans were repeated three times. Median normalized values and interquartile ranges (IQRs) were calculated for all kVp settings and phantom sizes.Correlation between measured and known iodine concentrations was excellent for both systems (R = 0.999-1.000, p < 0.0001). For DSCT, median measurement errors ranged from -0.5% (IQR -2.0, 2.0%) at 150Sn/70 kVp and -2.3% (IQR -4.0, -0.1%) at 150Sn/80 kVp to -4.0% (IQR -6.0, -2.8%) at 150Sn/90 kVp. For DLCT, median measurement errors ranged from -3.3% (IQR -4.9, -1.5%) at 140 kVp to -4.6% (IQR -6.0, -3.6%) at 120 kVp. Larger phantom sizes increased variability of iodine measurements (p < 0.05).Iodine concentration can be accurately quantified with state-of-the-art DECT systems from two vendors. The lowest absolute errors were found for DSCT using the 150Sn/70 kVp or 150Sn/80 kVp combinations, which was slightly more accurate than 140 kVp in DLCT.• High-end CT scanners allow accurate iodine quantification using different DECT techniques. • Lowest measurement error was found in scans with largest photon energy separation. • Dual-source CT quantified iodine slightly more accurately than dual layer CT.
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Project description:ObjectiveTo investigate the value of a radiomics model based on dual-energy computed tomography (DECT) venous-phase iodine map (IM) and 120 kVp equivalent mixed images (MIX) in predicting the Lauren classification of gastric cancer.MethodsA retrospective analysis of 240 patients undergoing preoperative DECT and postoperative pathologically confirmed gastric cancer was done. Training sets (n = 168) and testing sets (n = 72) were randomly assigned with a ratio of 7:3. Patients are divided into intestinal and non-intestinal groups. Traditional features were analyzed by two radiologists, using logistic regression to determine independent predictors for building clinical models. Using the Radiomics software, radiomics features were extracted from the IM and MIX images. ICC and Boruta algorithm were used for dimensionality reduction, and a random forest algorithm was applied to construct the radiomics model. ROC and DCA were used to evaluate the model performance.ResultsGender and maximum tumor thickness were independent predictors of Lauren classification and were used to build a clinical model. Separately establish IM-radiomics (R-IM), mixed radiomics (R-MIX), and combined IM + MIX image radiomics (R-COMB) models. In the training set, each radiomics model performed better than the clinical model, and the R-COMB model showed the best prediction performance (AUC: 0.855). In the testing set also, the R-COMB model had better prediction performance than the clinical model (AUC: 0.802).ConclusionThe R-COMB radiomics model based on DECT-IM and 120 kVp equivalent MIX images can effectively be used for preoperative noninvasive prediction of the Lauren classification of gastric cancer.Critical relevance statementThe radiomics model based on dual-energy CT can be used for Lauren classification prediction of preoperative gastric cancer and help clinicians formulate individualized treatment plans and assess prognosis.
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