Effects of image distortion and Hounsfield unit variations on radiation treatment plans: An extended field-of-view reconstruction in a large bore CT scanner

Validation of deep learning-based CT image reconstruction for treatment planning

Deep learning-based CT image reconstruction (DLR) is a state-of-the-art method for obtaining CT images. This study aimed to evaluate the usefulness of DLR in radiotherapy. Data were acquired using a large-bore CT system and an electron density phantom for radiotherapy. We compared the CT values, image noise, and CT value-to-electron density conversion table of DLR and hybrid iterative reconstruction (H-IR) for various doses. Further, we evaluated three DLR reconstruction strength patterns (Mild, Standard, and Strong). The variations of CT values of DLR and H-IR were large at low doses, and the difference in average CT values was insignificant with less than 10 HU at doses of 100 mAs and above. DLR showed less change in CT values and smaller image noise relative to H-IR. The noise-reduction effect was particularly large in the low-dose region. The difference in image noise between DLR Mild and Standard/Strong was large, suggesting the usefulness of reconstruction intensities higher than Mild. DLR showed stable CT values and low image noise for various materials, even at low doses; particularly for Standard or Strong, the reduction in image noise was significant. These findings indicate the usefulness of DLR in treatment planning using large-bore CT systems.

The automated measurement of CT number linearity using an ACR accreditation phantom

We developed a software to automatically measure the linearity between the CT numbers and densities of objects using an ACR 464 CT phantom, and investigated the CT number linearity of 16 different CT scanners. The software included a segmentation-rotation method. After segmenting five objects within the phantom image, the software computed the mean CT number of each object and plotted a graph between the CT numbers and densities of the objects. Linear regression and coefficients of regression, R², were automatically calculated. The software was used to investigate the CT number linearity of 16 CT scanners from Toshiba, Siemens, Hitachi, and GE installed at 16 hospitals in Indonesia. The linearity of the CT number obtained on most of the scanners showed a strong linear correlation (R²> 0.99) between the CT numbers and densities of the five phantom materials. Two scanners (Siemens Emotion 16) had the strongest linear correlation withR²= 0.999, and two Hitachi Eclos scanners had the weakest linear correlation withR²< 0.99.

Assessing radiomics feature stability with simulated CT acquisitions

Medical imaging quantitative features had once disputable usefulness in clinical studies. Nowadays, advancements in analysis techniques, for instance through machine learning, have enabled quantitative features to be progressively useful in diagnosis and research. Tissue characterisation is improved via the “radiomics” features, whose extraction can be automated. Despite the advances, stability of quantitative features remains an important open problem. As features can be highly sensitive to variations of acquisition details, it is not trivial to quantify stability and efficiently select stable features. In this work, we develop and validate a Computed Tomography (CT) simulator environment based on the publicly available ASTRA toolbox (www.astra-toolbox.com). We show that the variability, stability and discriminative power of the radiomics features extracted from the virtual phantom images generated by the simulator are similar to those observed in a tandem phantom study. Additionally, we show that the variability is matched between a multi-center phantom study and simulated results. Consequently, we demonstrate that the simulator can be utilised to assess radiomics features’ stability and discriminative power.

The reliability of CT numbers as absolute values for diagnostic scanning, dental imaging, and radiation therapy simulation: A narrative review

BACKGROUND AND PURPOSE

The purpose of this review was to examine the reported factors that affect the reliability of Computed Tomography (CT) numbers and their impact on clinical applications in diagnostic scanning, dental imaging, and radiation therapy dose calculation.

METHODS

A comprehensive search of the literature was conducted using Medline (PubMed), Google Scholar, and Ovid databases which were searched using the keywords CT number variability, CT number accuracy and uniformity, tube voltage, patient positioning, patient off-centring, and size dependence. A narrative summary was used to compile the findings under the overarching theme.

DISCUSSION

A total of 47 articles were identified to address the aim of this review. There is clear evidence that CT numbers are highly dependent on the energy level applied based on the effective atomic number of the scanned tissue. Furthermore, body size and anatomical location have also indicated an influence on measured CT numbers, especially for high-density materials such as bone tissue and dental implants. Patient off-centring was reported during CT imaging, affecting dose and CT number reliability, which was demonstrated to be dependent on the shaping filter size.

CONCLUSION

CT number accuracy for all energy levels, body sizes, anatomical locations, and degrees of patient off-centring is observed to be a variable under certain common conditions. This has significant implications for several clinical applications. It is crucial for those involved in CT imaging to understand the limitations of their CT system to ensure radiologists and operators avoid potential pitfalls associated with using CT numbers as absolute values for diagnostic scanning, dental imaging, and radiation therapy dose calculation.

Evaluation of novel AI-based extended field-of-view CT reconstructions

Purpose

Modern computed tomography (CT) scanners have an extended field‐of‐view (eFoV) for reconstructing images up to the bore size, which is relevant for patients with higher BMI or non‐isocentric positioning due to fixation devices. However, the accuracy of the image reconstruction in eFoV is not well known since truncated data are used. This study introduces a new deep learning‐based algorithm for extended field‐of‐view reconstruction and evaluates the accuracy of the eFoV reconstruction focusing on aspects relevant for radiotherapy.

Methods

A life‐size three‐dimensional (3D) printed thorax phantom, based on a patient CT for which eFoV was necessary, was manufactured and used as reference. The phantom has holes allowing the placement of tissue mimicking inserts used to evaluate the Hounsfield unit (HU) accuracy. CT images of the phantom were acquired using different configurations aiming to evaluate geometric and HU accuracy in the eFoV. Image reconstruction was performed using a state‐of‐the‐art reconstruction algorithm (HDFoV), commercially available, and the novel deep learning‐based approach (HDeepFoV). Five patient cases were selected to evaluate the performance of both algorithms on patient data. There is no ground truth for patients so the reconstructions were qualitatively evaluated by five physicians and five medical physicists.

Results

The phantom geometry reconstructed with HDFoV showed boundary deviations from 1.0 to 2.5 cm depending on the volume of the phantom outside the regular scan field of view. HDeepFoV showed a superior performance regardless of the volume of the phantom within eFOV with a maximum boundary deviation below 1.0 cm. The maximum HU (absolute) difference for soft issue inserts is below 79 and 41 HU for HDFoV and HDeepFoV, respectively. HDeepFoV has a maximum deviation of −18 HU for an inhaled lung insert while HDFoV reached a 229 HU difference. The qualitative evaluation of patient cases shows that the novel deep learning approach produces images that look more realistic and have fewer artifacts.

Conclusion

To be able to reconstruct images outside the sFoV of the CT scanner there is no alternative than to use some kind of extrapolated data. In our study, we proposed and investigated a new deep learning‐based algorithm and compared it to a commercial solution for eFoV reconstruction. The deep learning‐based algorithm showed superior performance in quantitative evaluations based on phantom data and in qualitative assessments of patient data.

One step acid activation of bentonite derived adsorbent for the effective remediation of the new generation of industrial pesticides

Herein, the facile one step acid activation of bentonite derived functionalized adsorbent (AB) for the effective remediation of both ionic and non-ionic secondary pesticides, ametryn and metolachlor has been attempted. The surface characteristics of AB were examined by the nitrogen adsorption-desorption analysis, scanning electron microscopy (SEM), and Fourier Transforms Infrared (FTIR) Spectroscopy. The adsorptive behavior was evaluated with respect to the effect of contact time, initial concentrations and solution pH. The equilibrium data were fitted to the Langmuir, Freundlich and Temkin isotherm models, while the adsorption kinetic was analyzed using the pseudo-first order and pseudo-second order kinetic equations. Thermodynamic parameters including the standard enthalpy change (ΔH°), standard entropy change (ΔS°), and Gibbs free energy change (ΔG°) were established. Thermodynamic analysis illustrated that the adsorption process was feasible and exothermic in nature, while the characterization findings verified the alteration of FTIR bands, and a high specific surface area of 464.92 m2/g, with a series of pores distributed over the surface. Equilibrium data was best confronted to the pseudo-second order kinetic model, while the adsorptive removal of ametryn and metolachlor onto AB was satisfactory described by the Langmuir isotherm model, with the monolayer adsorption capacities for ametryn and metolachlor of 2.032 and 0.208 mmole/g respectively. The findings outlined the potential of the newly develop AB for the on-site treatment of pesticide polluted water.