A suggested method for setting up GSI profiles on the GE Revolution CT scanner

Gauntt, David M.

doi:10.1002/acm2.12754

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Recently, some researchers have found that the reconstruction of monochromatic energy images (MonoE) by spectral CT can reduce metal artifacts [ 17–19 ]. The spectral CT can be achieved by various techniques, such as rapid kVp switching (80 and 140 kVp) [ 20 ], dual source [ 21 ] and detector or dual-layer detector [ 22 ]. The synthetic MonoE can be used to reduce beam hardening artifacts [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

Meng

Jiang

et al. 2022

Journal of Radiation Research

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The purpose of the study was to evaluate the accuracy of monochromatic energy (MonoE) computed tomography (CT) images reconstructed by spectral CT in predicting the stopping power ratio $( SP{R}_w)$ of materials in the presence of metal. The CIRS062 phantom was scanned three times using spectral CT. In the first scan, a solid water insert was placed at the center of the phantom $(C{T}_{no\ metal})$. In the second scan, the solid water insert was replaced with a titanium alloy femoral head $(C{T}_{metal})$. The metal artifact reduction (MAR) algorithm was used in the last scan $(C{T}_{metal+ MAR})$. The MonoE-CT images of 40 keV and 80 keV were reconstructed. Finally, the single-energy CT method (SECT) and the dual-energy CT method (DECT) were used to calculate the $SP{R}_w$. The mean absolute error (MAE) of the $SP{R}_w$ of the inner layer inserts calculated by the SECT method were 3.19%, 13.88% and 2.71%, corresponding to $C{T}_{no\ metal}$, $C{T}_{metal}$ and $C{T}_{metal+ MAR}$, respectively. For the outer layer inserts, the MAE of $SP{R}_w$ were 3.43%, 5.42% and 2.99%, respectively. Using the DECT method, the MAE of the $SP{R}_w$ of the inner layer inserts was 1.30%, 3.69% and 1.46% and the MAE of the outer layer inserts– was 1.34%, 1.36% and 1.05%. The studies shows that, compared with the SECT method, the accuracy of the DECT method in predicting the $SP{R}_w$ of a material is more robust to the presence of metal. Using the MAR algorithm when performing CT scans can further improve the accuracy of predicting the SPR of materials in the presence of metal.

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Section: Introductionmentioning

confidence: 99%

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

Meng

Jiang

et al. 2022

Journal of Radiation Research

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“…The small and the large phantom setups were scanned with a diagnostic DECT scanner ( Revolution Apex , GE Healthcare, Waukesha, WI, USA) equipped with a flat radiotherapy couch top ( OGS‐4 , Diacor Inc., West Valley City, UT, USA) using the FKS‐scan mode, with the maximum circular 50 cm (scan and display) field‐of‐view. The scans were acquired, with a fixed tube current for each phantom setup, which were selected by the scanner software based on the attenuation in the localizer radiographs and the noise index of the protocol 55 . Detailed scan and reconstruction parameters are presented in Table 2.…”

Section: Methodsmentioning

confidence: 99%

“…The scans were acquired, with a fixed tube current for each phantom setup, which were selected by the scanner software based on the attenuation in the localizer radiographs and the noise index of the protocol. 55 Detailed scan and reconstruction parameters are presented in Table 2. Separate water and iodine MD image series were reconstructed, as well as a VM image series saved as GSI Data Files.…”

Section: Ct Imagingmentioning

confidence: 99%

Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

Pettersson,

Thilander‐Klang,

Bäck

2024

Medical Physics

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BackgroundProton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single‐energy computed tomography (SECT). Various studies have shown that multi‐energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age‐related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection‐based MECT systems for SPR prediction.PurposeTo present and evaluate a novel proton SPR prediction method based on MD images from dual‐energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.MethodsA theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non‐tissue materials, by minimizing the root‐mean‐square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD‐SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data‐fitting. The goodness of fit of the MD‐SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head‐ and an abdomen‐sized phantom setup, were acquired with a clinical projection‐based fast‐kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non‐tissue materials in the phantoms, as well as used for input to the MD‐SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image‐based consecutive scan DECT for the same phantom setups.ResultsThe predicted SPRs of the tissue surrogates were similar for MD‐SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non‐adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between −0.7% and −1.8%. The SPRs of the non‐tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD‐SPR images, compared to 6.8% for DirectSPR.ConclusionsThe MD‐SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non‐tissue materials.

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“…All patients were scanned in a second-generation multi-detector CT (Revolution CT ES, GE Healthcare, Milwaukee, Brookfield, WI, USA). For group A, scanning parameters were as follows: dual-energy helical scanning with 80/140 peak kV (kVp) fast switching with an automatic exposure control (GSI Assist, GE Healthcare, Milwaukee, WI, USA) and a noise index of 13, rotation time of 0.5 s, pitch of 0.992, slice thickness of 3 mm, and beam width of 80 mm [18]. For group B, conventional non-contrast images were acquired with 120 kVp, AutomA function (range: 80-600 mA) with a noise index of either 13 or 18, rotation time of 0.5 s, helical pitch of 0.992, slice thickness of 3 mm, and beam width of 80 mm.…”

Section: Computed Tomography Acquisitionmentioning

confidence: 99%

Assessment of Liver Fat: Dual-Energy CT versus Conventional CT with and without Contrast

Boesen

Hansén

et al. 2022

Diagnostics

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We assessed the correlation between liver fat percentage using dual-energy CT (DECT) and Hounsfield unit (HU) measurements in contrast and non-contrast CT. This study included 177 patients in two patient groups: Group A (n = 125) underwent whole body non-contrast DECT and group B (n = 52) had a multiphasic DECT including a conventional non-contrast CT. Three regions of interest were placed on each image series, one in the left liver lobe and two in the right to measure Hounsfield Units (HU) as well as liver fat percentage. Linear regression analysis was performed for each group as well as combined. Receiver operating characteristic (ROC) curve was generated to establish the optimal fat percentage threshold value in DECT for predicting a non-contrast threshold of 40 HU correlating to moderate-severe liver steatosis. We found a strong correlation between fat percentage found with DECT and HU measured in non-contrast CT in group A and B individually (R2 = 0.81 and 0.86, respectively) as well as combined (R2 = 0.85). No significant difference was found when comparing venous and arterial phase DECT fat percentage measurements in group B (p = 0.67). A threshold of 10% liver fat found with DECT had 95% sensitivity and 95% specificity for the prediction of a 40 HU threshold using non-contrast CT. In conclusion, liver fat quantification using DECT shows high correlation with HU measurements independent of scan phase.

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A suggested method for setting up GSI profiles on the GE Revolution CT scanner

Cited by 8 publications

References 11 publications

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

Prediction of proton beam range in phantom with metals based on monochromatic energy CT images

Prediction of proton stopping power ratios using dual‐energy CT basis material decomposition

Assessment of Liver Fat: Dual-Energy CT versus Conventional CT with and without Contrast

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