Improving the Accuracy of the Effective Atomic Number (EAN) and Relative Electron Density (RED) with Stoichiometric Calibration on PCD-CT Images

The photon counting detector (PCD) in computed tomography (CT) can count the number of incoming photons in order to obtain energy information for photons corresponding to user-defined thresholds. Research on the extraction of effective atomic number (EAN) and relative electron density (RED) using dual-energy CT (DECT) is currently underway. This study proposes a method for improving EAN and RED accuracy of tissue-equivalent materials by using PCD-CT-based stoichiometric calibration. After obtaining DECT images in energy bin (EB) and full spectrum (FS) modes for eight tissue-equivalent materials, the EAN was calculated with stoichiometric calibration. Using the EAN image, the RED image was acquired to evaluate the accuracy. The errors of both EAN and RED obtained with EB were within 4%. In particular, the accuracy of RED was higher than that of the FS method. Study results indicate that PCD-CT contributes to improving EAN and RED accuracy. Further studies will be aimed at reducing ring artifacts by pixel-correcting PCD images and improving stopping power ratio (SPR) measurements for dose calculation in particle therapy.

Evaluation of Computed Tomography Scanners for Feasibility of Using Averaged Hounsfield Unit-to-Stopping Power Ratio Calibration Curve

Purpose:

The purpose of this study was to quantify the variability of stoichiometric calibration curves for different computed tomography (CT) scanners and determine whether an averaged Hounsfield unit (HU)–to–stopping power ratio (SPR) calibration curve can be used across multiple CT scanners.

Materials and Methods:

Five CT scanners were used to scan an electron density phantom to establish HU values of known material plugs. A stoichiometric calibration curve was calculated for CT scanners and for the average curve. Animal tissue surrogates were used to compare the water-equivalent thickness (WET) of the animal tissue surrogates calculated by the treatment planning system (TPS) and the WET values measured with a multilayered ionization chamber. The calibration curves were optimized to reduce the percentage of difference between measured and TPS-calculated WET values. A second set of tissue surrogates was then used to evaluate the overall range of uncertainty for the optimized CT-specific and average calibration curves.

Results:

Overall, the average variation in HU for all 6 calibration curves before optimization was 8.3 HU. For both the averaged and CT-specific calibrations, the root mean square error (RMSE) of the percentage of difference between TPS-calculated and measured WET values before optimization was 4%. The RMSE of the percentage of difference for the TPS-calculated and multilayered ionization chamber measured WET values after the optimization for both averaged and CT-specific calibration curves was reduced to less than 1.5%. The overall RMSE of the TPS and the measured WET percentage of difference after optimization was 2.1% for both averaged and CT-specific calibration curves.

Conclusion:

Averaged CT calibration curves can be used to map the HU-to-SPR in TPSs, if the variations in HU values across all scanners is relatively small. Performing tissue surrogate optimization of the HU-to-SPR calibration curve has been shown to reduce the overall uncertainty of the calibration for averaged and CT-specific calibration curves and is recommended, especially if an averaged HU-to-SPR calibration curve is used.

Tissue decomposition from dual energy CT data for MC based dose calculation in particle therapy

PURPOSE

The authors describe a novel method of predicting mass density and elemental mass fractions of tissues from dual energy CT (DECT) data for Monte Carlo (MC) based dose planning.

METHODS

The relative electron density ϱ(e) and effective atomic number Z(eff) are calculated for 71 tabulated tissue compositions. For MC simulations, the mass density is derived via one linear fit in the ϱ(e) that covers the entire range of tissue compositions (except lung tissue). Elemental mass fractions are predicted from the ϱ(e) and the Z(eff) in combination. Since particle therapy dose planning and verification is especially sensitive to accurate material assignment, differences to the ground truth are further analyzed for mass density, I-value predictions, and stopping power ratios (SPR) for ions. Dose studies with monoenergetic proton and carbon ions in 12 tissues which showed the largest differences of single energy CT (SECT) to DECT are presented with respect to range uncertainties. The standard approach (SECT) and the new DECT approach are compared to reference Bragg peak positions.

RESULTS

Mean deviations to ground truth in mass density predictions could be reduced for soft tissue from (0.5±0.6)% (SECT) to (0.2±0.2)% with the DECT method. Maximum SPR deviations could be reduced significantly for soft tissue from 3.1% (SECT) to 0.7% (DECT) and for bone tissue from 0.8% to 0.1%. Mean I-value deviations could be reduced for soft tissue from (1.1±1.4%, SECT) to (0.4±0.3%) with the presented method. Predictions of elemental composition were improved for every element. Mean and maximum deviations from ground truth of all elemental mass fractions could be reduced by at least a half with DECT compared to SECT (except soft tissue hydrogen and nitrogen where the reduction was slightly smaller). The carbon and oxygen mass fraction predictions profit especially from the DECT information. Dose studies showed that most of the 12 selected tissues would profit significantly (up to 2.2%) from DECT material decomposition with no noise present. The ϱ(e) associated with an absolute noise of ±0.01 and Z(eff) associated with an absolute noise of ±0.2 resulted in ±10% standard variation in the carbon and oxygen mass fraction prediction.

CONCLUSIONS

Accurate stopping power prediction is mainly determined by the correct mass density prediction. Theoretical improvements in range predictions with DECT data in the order of 0.1%-2.1% were observed. Further work is needed to quantify the potential improvements from DECT compared to SECT in measured image data associated with artifacts and noise.