Proton stopping power prediction based on dual‐energy CT‐generated virtual monoenergetic images

Abstract

Purpose The purpose of this work was to assess a proof of concept for a novel method for predicting proton stopping power ratios (SPRs) based on a pair of dual‐energy CT generated virtual monoenergetic (VM) images. Materials and methods A rapid kV‐switching dual‐energy CT scanner was used to acquire Gemstone Spectral Imaging (GSI) and 120 kV conventional single‐energy CT (SECT) image data of the CIRS 062M phantom. The proposed method was applied to every possible pairing of VM images between 40 and 140 keV to find the optimal energy pairs for SPR prediction in lung tissue, soft tissue, and bone. The predicted SPRs were compared against SPRs predicted from the SECT data using the conventional SECT‐based method. The impact of different scan and reconstruction parameters was also investigated. Results The SPR residual root mean square errors (RMSE) yielded by the optimal pairs were 7.2% for lung tissue, 0.4% for soft tissue, and 0.8% for bone. While no direct comparison could be made to other DECT‐based methods for SPR prediction, as these methods could not be directly implemented on a fast kV‐switching system, the SPR RMSEs for soft tissue and bone in Table 4 are comparable to RMSEs reported in the literature. For the conventional SECT‐based method, the SPR RMSEs were 5.9% for lung tissue, 0.9% for soft tissue, and 5.1% for bone. Conclusions The proposed method is a valid alternative to, and has the potential to improve upon, the conventional SECT‐based method for predicting SPRs. The formalism used in the method is applied directly, with no approximations made on our part, and requires neither prior knowledge of the spectra nor calibration with a phantom. This work presents a way of optimizing the proposed method for a specific scanner by determining the optimal energy pairs to use as input and demonstrates the method's robustness to different levels of ASiR‐V, reconstruction kernels, and dose levels.

3D Printing of Porous Degradable Scaffolds with Polylactic Acid‐Loaded Bioactive Glass and Calcium Sulfate Hemihydrate for Bone Defect Repair

“…MDQ also improves the qualitative function of conventional CT, which only relies on CT value to judge the classification of substances, so that CT scanning can not only clearly display the specific substances in the anatomical structure, but also quantitatively determine the specific substances. [ 53 ] The base material pairs directly measured by GSI were set to be hydroxyapatite (HAP) and water, which can be considered as the bone density of cancellous bone avoiding the influence of sclerotic bone or cortical bone. At 20 weeks after surgery, the measurement of BMD in bone defects by GSI in the PLA‐CSH‐BAG group was higher than that in the PLA group and the control group ( p < 0.001), although there was a little different value in BMD measured by GSI and dual‐energy X‐ray absorptiometry (DXA), the results of the PLA‐CSH‐BAG and PLA groups within the two validation methods tended to be consistent, which shows that the PLA‐CSH‐BAG scaffold had an advantage in osteogenesis.…”

Stopping‐power ratio estimation for proton radiotherapy using dual‐energy computed tomography and prior‐image constrained denoising

“…Using monoenergetic reconstructions as the low-and high-energy scans for input into the algorithm could possibly provide similar benefits. 39 There is also much scope for testing alternative or additional penalties, to further improve image quality and accuracy in the reconstructions. In addition, the algorithm can be readily modified for other linear components (instead of ED and EAN) such as weight fractions 23 for a material basis set.…”

“…The use of mono plus would have the benefit of producing a prior image with minimal beam‐hardening artefacts. Using monoenergetic reconstructions as the low‐ and high‐energy scans for input into the algorithm could possibly provide similar benefits 39 . There is also much scope for testing alternative or additional penalties, to further improve image quality and accuracy in the reconstructions.…”

Investigation on the proton range uncertainty with spectral CT‐based virtual monoenergetic images

“…The residuals of CT-to-SPR conversion curve for different tissue was energy-dependent. Näsmark et al 11 investigated the optimal energy pairs with dual-energy CT-generated VM images for the SPR prediction of different tissues. SPR prediction was based on the formalism of Jackson and Hawkes.…”

“…SPR prediction was based on the formalism of Jackson and Hawkes 12 . The optimal energy pairs of SPR prediction was 52/53 keV, 48/51 keV, and 84/85 keV for lung group, soft tissue group and bone group, respectively 11 . Therefore, it is significant to employ the CT‐to‐SPR conversion curves of different energies for different tissues.…”