Estimation of electron density, effective atomic number and stopping power ratio using dual-layer computed tomography for radiotherapy treatment planning

Nuclear interaction correction based on dual-energy computed tomography in carbon-ion radiotherapy

Objective.Accurate dose predictions are crucial to maximizing the benefits of carbon-ion therapy (CIT). Carbon beams incident on the human body cause nuclear interactions with tissues, resulting in changes in the constituent nuclides and leading to dose errors that are conventionally corrected using conventional single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) has frequently been used for stopping power estimation in particle therapy and is well suited for correcting nuclear reactions because of its detailed body-tissue elemental information. This study proposes a correction method for the absolute dose in CIT that considers changes in nuclide composition resulting from nuclear reactions with body tissues, as a novel application of DECT.Approach.The change in dose associated with nuclear reactions is determined by correcting each integrated depth dose component of the carbon beam using a nuclear interaction correction factor. This factor is determined based on the stopping power, mass density, and nuclear interaction cross-section in body tissue. The stopping power and mass density were calculated using established methods, whereas the nuclear interaction cross-section was newly defined through a conversion equation derived from the effective atomic number.Main results.Nuclear interaction correction factors and corrected doses were determined for 85 body tissues with known compositions, comparing them with existing SECT-based methods. The root-mean-square errors of the SECT- and DECT-based nuclear interaction correction factors relative to theoretical values were 0.66% and 0.39%, respectively.Significance.This indicates a notable enhancement in the estimation accuracy with DECT. The dose calculations in uniform body tissues derived from SECT showed slight over-correction in adipose and bone tissues, whereas those based on DECT were almost consistent with theoretical values. Our proposed method demonstrates the potential of DECT for enhancing dose calculation accuracy in CIT, complementing its established role in stopping power estimation.

Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique

The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70 keV (VMI70) and 140 keV (VMI140) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI70 and VMI140 measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT120 (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT140 (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.

Dual-energy computed tomography-based iodine concentration as a predictor of histopathological response to preoperative chemoradiotherapy for pancreatic cancer

To explore predictors of the histopathological response to preoperative chemoradiotherapy (CRT) in patients with pancreatic cancer (PC) using dual-energy computed tomography-reconstructed images. This retrospective study divided 40 patients who had undergone preoperative CRT (50-60 Gy in 25 fractions) followed by surgical resection into two groups: the response group (Grades II, III and IV, evaluated from surgical specimens) and the nonresponse group (Grades Ia and Ib). The computed tomography number [in Hounsfield units (HUs)] and iodine concentration (IC) were measured at the locations of the aorta, PC and pancreatic parenchyma (PP) in the contrast-enhanced 4D dual-energy computed tomography images. Logistic regression analysis was performed to identify predictors of histopathological response. Univariate analysis did not reveal a significant relation between any parameter and patient characteristics or dosimetric parameters of the treatment plan. The HU and IC values in PP and the differences in HU and IC between the PP and PC (ΔHU and ΔIC, respectively) were significant predictors for distinguishing the response (n = 24) and nonresponse (n = 16) groups (P < 0.05). The IC in PP and ΔIC had a higher area under curve values [0.797 (95% confidence interval, 0.659-0.935) and 0.789 (0.650-0.928), respectively] than HU in PP and ΔHU [0.734 (0.580-0.889) and 0.721 (0.562-0.881), respectively]. The IC value could potentially be used for predicting the histopathological response in patients who have undergone preoperative CRT.

Dual- and multi-energy CT for particle stopping-power estimation: current state, challenges and potential

Range uncertainty has been a key factor preventing particle radiotherapy from reaching its full physical potential. One of the main contributing sources is the uncertainty in estimating particle stopping power (ρ_s) within patients. Currently, theρ_sdistribution in a patient is derived from a single-energy CT (SECT) scan acquired for treatment planning by converting CT number expressed in Hounsfield units (HU) of each voxel toρ_susing a Hounsfield look-up table (HLUT), also known as the CT calibration curve. HU andρ_sshare a linear relationship with electron density but differ in their additional dependence on elemental composition through different physical properties, i.e. effective atomic number and mean excitation energy, respectively. Because of that, the HLUT approach is particularly sensitive to differences in elemental composition between real human tissues and tissue surrogates as well as tissue variations within and among individual patients. The use of dual-energy CT (DECT) forρ_sprediction has been shown to be effective in reducing the uncertainty inρ_sestimation compared to SECT. The acquisition of CT data over different x-ray spectra yields additional information on the material elemental composition. Recently, multi-energy CT (MECT) has been explored to deduct material-specific information with higher dimensionality, which has the potential to further improve the accuracy ofρ_sestimation. Even though various DECT and MECT methods have been proposed and evaluated over the years, these approaches are still only scarcely implemented in routine clinical practice. In this topical review, we aim at accelerating this translation process by providing: (1) a comprehensive review of the existing DECT/MECT methods forρ_sestimation with their respective strengths and weaknesses; (2) a general review of uncertainties associated with DECT/MECT methods; (3) a general review of different aspects related to clinical implementation of DECT/MECT methods; (4) other potential advanced DECT/MECT applications beyondρ_sestimation.

Potential of a Second-Generation Dual-Layer Spectral CT for Dose Calculation in Particle Therapy Treatment Planning

In particle therapy treatment planning, dose calculation is conducted using patient-specific maps of tissue ion stopping power ratio (SPR) to predict beam ranges. Improving patient-specific SPR prediction is therefore essential for accurate dose calculation. In this study, we investigated the use of the Spectral CT 7500, a second-generation dual-layer spectral computed tomography (DLCT) system, as an alternative to conventional single-energy CT (SECT) for patient-specific SPR prediction. This dual-energy CT (DECT)-based method allows for the direct prediction of SPR from quantitative measurements of relative electron density and effective atomic number using the Bethe equation, whereas the conventional SECT-based method consists of indirect image data-based prediction through the conversion of calibrated CT numbers to SPR. The performance of the Spectral CT 7500 in particle therapy treatment planning was characterized by conducting a thorough analysis of its SPR prediction accuracy for both tissue-equivalent materials and common non-tissue implant materials. In both instances, DLCT was found to reduce uncertainty in SPR predictions compared to SECT. Mean deviations of 0.7% and 1.6% from measured SPR values were found for DLCT- and SECT-based predictions, respectively, in tissue-equivalent materials. Furthermore, end-to-end analyses of DLCT-based treatment planning were performed for proton, helium, and carbon ion therapies with anthropomorphic head and pelvic phantoms. 3D gamma analysis was performed with ionization chamber array measurements as the reference. DLCT-predicted dose distributions revealed higher passing rates compared to SECT-predicted dose distributions. In the DLCT-based treatment plans, measured distal-edge evaluation layers were within 1 mm of their predicted positions, demonstrating the accuracy of DLCT-based particle range prediction. This study demonstrated that the use of the Spectral CT 7500 in particle therapy treatment planning may lead to better agreement between planned and delivered dose compared to current clinical SECT systems.

Significance of the spectral correction of photon counting detector response in material classification from spectral x-ray CT

Purpose: Photon counting imaging detectors (PCD) has paved the way for spectral x-ray computed tomography (spectral CT), which simultaneously measures a sample's linear attenuation coefficient (LAC) at multiple energies. However, cadmium telluride (CdTe)-based PCDs working under high flux suffer from detector effects, such as charge sharing and photon pileup. These effects result in the severe spectral distortions of the measured spectra and significant deviation of the extracted LACs from the reference attenuation curve. We analyze the influence of the spectral distortion correction on material classification performance. Approach: We employ a spectral correction algorithm to reduce the primary spectral distortions. We use a method for material classification that measures system-independent material properties, such as electron density, ρ e , and effective atomic number, Z eff . These parameters are extracted from the LACs using attenuation decomposition and are independent of the scanner specification. The classification performance with the raw and corrected data is tested on different numbers of energy bins and projections and different radiation dose levels. We use experimental data with a broad range of materials in the range of 6 ≤ Z eff ≤ 15 , acquired with a custom laboratory instrument for spectral CT. Results: We show that using the spectral correction leads to an accuracy increase of 1.6 and 3.8 times in estimating ρ e and Z eff , respectively, when the image reconstruction is performed from only 12 projections and the 15 energy bins approach is used. Conclusions: The correction algorithm accurately reconstructs the measured attenuation curve and thus gives better classification performance.

Machine learning based oxygen and carbon concentration derivation using dual-energy CT for PET-based dose verification in proton therapy

PURPOSE

Online dose verification based on proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g. C and O). We developed a machine learning framework for deriving oxygen and carbon concentration based on dual-energy CT (DECT).

METHODS

Digital phantoms at the head site were constructed based on single-energy CT (SECT) and stoichiometric calibration. DECT images (80 kVp and 140 kVp) were synthesized using two methods: 1) theoretical CT numbers with Gaussian noise (method 1) and 2) forward/backward image reconstruction with poly-energetic energy spectrum and Poisson noise modeled (method 2). Two architectures of convolutional neural networks, UNet and ResNet, were investigated to map from DECT images to C/O weights. Four cases (UNet-1: Method 1+UNet, ResNet-1: Method 1+ResNet, UNet-2: Method 2+UNet, and ResNet-2: Method 2 +ResNet) were tested for different tissue types and different noise levels. Monte-Carlo simulation was employed to identify the impact of fluctuation in oxygen and carbon concentration on activity/dose distribution.

RESULTS

When no noise present, all four cases are able to obtain <2% mean absolute errors (MAE) and <4% root mean square error (RMSE). For the worst image quality (e.g. lowest image SNR), the RMSE for O among all tissue types is 3.02% (UNet-1), 4.46% (ResNet-1), 4.38% (UNet-2) and 6.31% (ResNet-2), respectively. For UNet-1 and ResNet-1, the model performed slightly better in terms of RMSE for skeletal tissue than soft tissues. Such trend is not observed for UNet-2 and ResNet-2. With regard to the comparison between UNet and ResNet, different accuracy and noise immunity are observed. The activity profiles exhibit 3-5% difference in terms of mean relative error (MRE) between the ground truth and machine learning outcome.

CONCLUSION

We explored the feasibility of a machine learning framework to derive elemental concentration of oxygen and carbon based on DECT images. Two machine learning models, UNet and ResNet, are able to utilize spatial correlation and obtain accurate carbon and oxygen concentration. This study lays a foundation for us to apply the proposed approach to clinical DECT images. This article is protected by copyright. All rights reserved.

Dual-layer spectral CT for proton, helium, and carbon ion beam therapy planning of brain tumors

Pretreatment computed tomography (CT) imaging is an essential component of the particle therapy treatment planning chain. Treatment planning and optimization with charged particles require accurate and precise estimations of ion beam range in tissues, characterized by the stopping power ratio (SPR). Reduction of range uncertainties arising from conventional CT-number-to-SPR conversion based on single-energy CT (SECT) imaging is of importance for improving clinical practice. Here, the application of a novel imaging and computational methodology using dual-layer spectral CT (DLCT) was performed toward refining patient-specific SPR estimates. A workflow for DLCT-based treatment planning was devised to evaluate SPR prediction for proton, helium, and carbon ion beam therapy planning in the brain. DLCT- and SECT-based SPR predictions were compared in homogeneous and heterogeneous anatomical regions. This study included eight patients scanned for diagnostic purposes with a DLCT scanner. For each patient, four different treatment plans were created, simulating tumors in different parts of the brain. For homogeneous anatomical regions, mean SPR differences of about 1% between the DLCT- and SECT-based approaches were found. In plans of heterogeneous anatomies, relative (absolute) proton range shifts of 0.6% (0.4 mm) in the mean and up to 4.4% (2.1 mm) at the distal fall-off were observed. In the investigated cohort, 12% of the evaluated organs-at-risk (OARs) presented differences in mean or maximum dose of more than 0.5 Gy (RBE) and up to 6.8 Gy (RBE) over the entire treatment. Range shifts and dose differences in OARs between DLCT and SECT in helium and carbon ion treatment plans were similar to protons. In the majority of investigated cases (75th percentile), SECT- and DLCT-based range estimations were within 0.6 mm. Nonetheless, the magnitude of patient-specific range deviations between SECT and DLCT was clinically relevant in heterogeneous anatomical sites, suggesting further study in larger, more diverse cohorts. Results indicate that patients with brain tumors may benefit from DLCT-based treatment planning.

Dual-layer detector spectral CT-a new supplementary method for preoperative evaluation of glioma

PURPOSE

To investigate the value of the iodine concentration (IC) measured by dual-layer detector spectral CT (DLDSCT) in evaluating the factors related to the treatment scheme and survival prognosis of patients with glioma.

METHODS

From 2018 to 2019, we prospectively collected the data of 99 patients with glioma. The degree of CT enhancement and the IC of low grade gliomas (LGGs, II), high grade gliomas (HGGs, III and IV), grade II and III gliomas, were compared. The predictive performance of the degree of CT enhancement and IC was examined via receiver operating characteristic (ROC) analysis. The correlations between IC and Ki-67 labeling index, isocitrate dehydrogenase (IDH) mutation, chromosome 1p/19q deletion status of the tumor were examined.

RESULTS

Both IC and the degree of CT enhancement of patients with HGG were significantly higher than those of patients with LGG (p < 0.001; χ² =41.707, p <  0.001); IC had large area under the ROC curve for diagnostic HGG (0.931; 95 % CI: 0.882-0.979; p <  0.001). The IC in the grade III gliomas was significantly higher than that in grade II gliomas (p < 0.001); IC had a large area under the ROC curve for diagnostic grade III gliomas (0.865; 95 % CI: 0.779-0.952; p <  0.001). There was a significant positive correlation between IC and Ki-67 LI (r = 0.679; p < 0.001).

CONCLUSIONS

The DLDSCT technology can be used as a supplementary method to provide more information for preoperative grading of the gliomas and the prognosis assessment of the patients.

Status and innovations in pre-treatment CT imaging for proton therapy

Pre-treatment CT imaging is a topic of growing importance in particle therapy. Improvements in the accuracy of stopping-power prediction are demanded to allow for a dose conformality that is not inferior to state-of-the-art image-guided photon therapy. Although range uncertainty has been kept practically constant over the last decades, recent technological and methodological developments, like the clinical application of dual-energy CT, have been introduced or arise at least on the horizon to improve the accuracy and precision of range prediction. This review gives an overview of the current status, summarizes the innovations in dual-energy CT and its potential impact on the field as well as potential alternative technologies for stopping-power prediction.

Radiation interaction parameters for blood samples of breast cancer patients: an MCNP study

The main goal of this study was to determine radiation interaction parameters such as mass attenuation coefficients, effective atomic numbers, and effective electron densities depending on element concentrations (Na, K, Cu, Zn, Al, Ca, Mg Cr, Fe, Se) in blood samples of patients with breast cancer. Eighty blood samples were collected and analyzed in this study (40 from breast cancer patients and 40 from healthy patients). The determination of element concentrations of the samples was performed with inductively coupled plasma-mass spectrometry (ICP-MS) and inductively coupled plasma-optical emission spectrometry (ICP-OES) after which the element concentrations were normalized to percentage. Mass attenuation coefficients were calculated by Monte Carlo simulation method. In addition, effective atomic numbers and effective electron density values of the blood samples were calculated with the ZXCOM program. One of the most important results of this study is that differences in radiation interaction parameters between the two groups were observed. More specifically, the mass attenuation coefficients of the healthy group's blood samples were higher than those of the cancerous group at photon energies of 50 keV, 100 keV, 250 keV and 500 keV, while they were lower at 1 MeV. All the MCNP results were consistent with the results obtained from ZXCOM. As the main result of this study it is concluded that photon atomic parameters such as mass attenuation coefficient, effective atomic number and electron density may be considered in cancer diagnosis or treatment modalities.