Pixel-by-pixel correction of beam hardening artifacts by bowtie filter in fan-beam CT.

Objective. Beam hardening (BH) artifacts in computed tomography (CT) images originate from the polychromatic nature of x-ray photons. In a CT system with a bowtie filter, residual BH artifacts remain when polynomial fits are used. These artifacts lead to worse visuals, reduced contrast, and inaccurate CT numbers. This work proposes a pixel-by-pixel correction (PPC) method to reduce the residual BH artifacts caused by a bowtie filter.Approach. The energy spectrum for each pixel at the detector after the photons pass through the bowtie filter was calculated. Then, the spectrum was filtered through a series of water slabs with different thicknesses. The polychromatic projection corresponding to the thickness of the water slab for each detector pixel could be obtained. Next, we carried out a water slab experiment with a mono energyE= 69 keV to get the monochromatic projection. The polychromatic and monochromatic projections were then fitted with a 2nd-order polynomial. The proposed method was evaluated on digital phantoms in a virtual CT system and phantoms in a real CT machine.Main results. In the case of a virtual CT system, the standard deviation of the line profile was reduced by 23.8%, 37.3%, and 14.3%, respectively, in the water phantom with different shapes. The difference of the linear attenuation coefficients (LAC) in the central and peripheral areas of an image was reduced from 0.010 to 0.003cm-1and 0.007cm-1to 0 in the biological tissue phantom and human phantom, respectively. The method was also validated using CT projection data obtained from Activion16 (Canon Medical Systems, Japan). The difference in the LAC in the central and peripheral areas can be reduced by a factor of two.Significance. The proposed PPC method can successfully remove the cupping artifacts in both virtual and authentic CT images. The scanned object's shapes and materials do not affect the technique.

Deep learning-based target tracking with X-ray images for radiotherapy: a narrative review.

Background and Objective: As one of the main treatment modalities, radiotherapy (RT) (also known as radiation therapy) plays an increasingly important role in the treatment of cancer. RT could benefit greatly from the accurate localization of the gross tumor volume and circumambient organs at risk (OARs). Modern linear accelerators (LINACs) are typically equipped with either gantry-mounted or room-mounted X-ray imaging systems, which provide possibilities for marker-less tracking with two-dimensional (2D) kV X-ray images. However, due to organ overlapping and poor soft tissue contrast, it is challenging to track the target directly and precisely with 2D kV X-ray images. With the flourishing development of deep learning in the field of image processing, it is possible to achieve real-time marker-less tracking of targets with 2D kV X-ray images in RT using advanced deep-learning frameworks. This article sought to review the current development of deep learning-based target tracking with 2D kV X-ray images and discuss the existing limitations and potential solutions. Finally, it also discusses some common challenges and potential future developments. Methods: Manual searches of the Web of Science, and PubMed, and Google Scholar were carried out to retrieve English-language articles. The keywords used in the searches included "radiotherapy, radiation therapy, motion tracking, target tracking, motion estimation, motion monitoring, X-ray images, digitally reconstructed radiographs, deep learning, convolutional neural network, and deep neural network". Only articles that met the predetermined eligibility criteria were included in the review. Ultimately, 23 articles published between March 2019 and December 2023 were included in the review. Key Content and Findings: In this article, we narratively reviewed deep learning-based target tracking with 2D kV X-ray images in RT. The existing limitations, common challenges, possible solutions, and future directions of deep learning-based target tracking were also discussed. The use of deep learning-based methods has been shown to be feasible in marker-less target tracking and real-time motion management. However, it is still quite challenging to directly locate tumor and OARs in real-time with 2D kV X-ray images, and more technical and clinical efforts are needed. Conclusions: Deep learning-based target tracking with 2D kV X-ray images is a promising method in motion management during RT. It has the potential to track the target in real time, recognize motion, reduce the extended margin, and better spare the normal tissue. However, it still has many issues that demand prompt attention, and further development before it can be put into clinical practice.

Cross-Channel Constraints on Resonant Antikaon-Nucleon Scattering.

Chiral perturbation theory and its unitarized versions have played an important role in our understanding of the low-energy strong interaction. Yet, so far, such studies typically deal exclusively with perturbative or nonperturbative channels. In this Letter, we report on the first global study of meson-baryon scattering up to one-loop order. It is shown that covariant baryon chiral perturbation theory, including its unitarization for the negative strangeness sector, can describe meson-baryon scattering data remarkably well. This provides a highly nontrivial check on the validity of this important low-energy effective field theory of QCD. We show that the K[over ¯]N related quantities can be better described in comparison with those of lower-order studies, and with reduced uncertainties due to the stringent constraints from the πN and KN phase shifts. In particular, we find that the two-pole structure of Λ(1405) persists up to one-loop order reinforcing the existence of two-pole structures in dynamically generated states.

New physics searches at kaon and hyperon factories.

Rare meson decays are among the most sensitive probes of both heavy and light new physics. Among them, new physics searches using kaons benefit from their small total decay widths and the availability of very large datasets. On the other hand, useful complementary information is provided by hyperon decay measurements. We summarize the relevant phenomenological models and the status of the searches in a comprehensive list of kaon and hyperon decay channels. We identify new search strategies for under-explored signatures, and demonstrate that the improved sensitivities from current and next-generation experiments could lead to a qualitative leap in the exploration of light dark sectors.

Weak radiative hyperon decays in covariant baryon chiral perturbation theory.

Weak radiative hyperon decays, important to test the strong interaction and relevant in searches for beyond the standard model physics, have remained puzzling both experimentally and theoretically for a long time. The recently updated branching fraction and first measurement of the asymmetry parameter of Λ→nγ by the BESIII Collaboration further exacerbate the issue, as none of the existing predictions can describe the data. We show in this work that the covariant baryon chiral perturbation theory, with constraints from the latest measurements of hyperon non-leptonic decays, can well describe the BESIII data. The predicted branching fraction and asymmetry parameter for Ξ-→Σ-γ are also in agreement with the experimental data. We note that a more precise measurement of the asymmetry parameter, which is strongly constrained by chiral symmetry and related with that of Σ+→pγ, is crucial to test Hara's theorem. We further predict the branching fraction and asymmetry parameter of Σ0→nγ, whose future measurement can serve as a highly nontrivial check on our understanding of weak radiative hyperon decays and on the covariant baryon chiral perturbation theory.

Virtual computed-tomography system for deep-learning-based material decomposition.

Objective.Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental information about the real human body. To overcome this problem, we developed a virtual CT system model, by which various reconstructed images can be generated based on ICRP110 human phantoms with information about six major elements (H, C, N, O, P, and Ca).Approach.We generated CT datasets labelled with accurate elemental information using the proposed generative CT model and trained a deep learning (DL)-based model to estimate the material distribution with the ICRP110 based human phantom as well as the digital Shepp-Logan phantom. The accuracy in quad-, dual-, and single-energy CT cases was investigated. The influence of beam-hardening artefacts, noise, and spectrum variations were analysed with testing datasets including elemental density and anatomical shape variations.Main results.The results indicated that this DL approach can realise precise MD, even with single-energy CT images. Moreover, noise, beam-hardening artefacts, and spectrum variations were shown to have minimal impact on the MD.Significance.Present results suggest that the difficulty to prepare a large CT database can be solved by introducing the virtual CT system and the proposed technique can be applied to clinical radiodiagnosis and radiotherapy.

Accurate Relativistic Chiral Nucleon-Nucleon Interaction up to Next-to-Next-to-Leading Order.

We construct a relativistic chiral nucleon-nucleon interaction up to the next-to-next-to-leading order in covariant baryon chiral perturbation theory. We show that a good description of the np phase shifts up to T_{lab}=200 MeV and even higher can be achieved with a χ[over ˜]^{2}/d.o.f. less than 1. Both the next-to-leading-order results and the next-to-next-to-leading-order results describe the phase shifts equally well up to T_{lab}=200 MeV, but for higher energies, the latter behaves better, showing satisfactory convergence. The relativistic chiral potential provides the most essential inputs for relativistic ab initio studies of nuclear structure and reactions, which has been in need for almost two decades.

Physical density estimations of single- and dual-energy CT using material-based forward projection algorithm: a simulation study.

OBJECTIVES: This study aims to evaluate the accuracy of physical density prediction in single-energy CT (SECT) and dual-energy CT (DECT) by adapting a fully simulation-based method using a material-based forward projection algorithm (MBFPA). METHODS: We used biological tissues referenced in ICRU Report 44 and tissue substitutes to prepare three different types of phantoms for calibrating the Hounsfield unit (HU)-to-density curves. Sinograms were first virtually generated by the MBFPA with four representative energy spectra (i.e. 80 kVp, 100 kVp, 120 kVp, and 6 MVp) and then reconstructed to form realistic CT images by adding statistical noise. The HU-to-density curves in each spectrum and their pairwise combinations were derived from the CT images. The accuracy of these curves was validated using the ICRP110 human phantoms. RESULTS: The relative mean square errors (RMSEs) of the physical density by the HU-to-density curves calibrated with kV SECT nearly presented no phantom size dependence. The kV-kV DECT calibrated curves were also comparable with those from the kV SECT. The phantom size effect became notable when the MV X-ray beams were employed for both SECT and DECT due to beam-hardening effects. The RMSEs were decreased using the biological tissue phantom. CONCLUSION: Simulation-based density prediction can be useful in the theoretical analysis of SECT and DECT calibrations. The results of this study indicated that the accuracy of SECT calibration is comparable with that of DECT using biological tissues. The size and shape of the calibration phantom could affect the accuracy, especially for MV CT calibrations. ADVANCES IN KNOWLEDGE: The present study is based on a full simulation environment, which accommodates various situations such as SECT, kV-kV DECT, and even kV-MV DECT. In this paper, we presented the advances pertaining to the accuracy of the physical density prediction when applied to SECT and DECT in the MV X-ray energy range. To the best of our knowledge, this study is the first to validate the physical density estimation both in SECT and DECT using human-type phantoms.

Review of Deep Learning Based Automatic Segmentation for Lung Cancer Radiotherapy.

Lung cancer is the leading cause of cancer-related mortality for males and females. Radiation therapy (RT) is one of the primary treatment modalities for lung cancer. While delivering the prescribed dose to tumor targets, it is essential to spare the tissues near the targets-the so-called organs-at-risk (OARs). An optimal RT planning benefits from the accurate segmentation of the gross tumor volume and surrounding OARs. Manual segmentation is a time-consuming and tedious task for radiation oncologists. Therefore, it is crucial to develop automatic image segmentation to relieve radiation oncologists of the tedious contouring work. Currently, the atlas-based automatic segmentation technique is commonly used in clinical routines. However, this technique depends heavily on the similarity between the atlas and the image segmented. With significant advances made in computer vision, deep learning as a part of artificial intelligence attracts increasing attention in medical image automatic segmentation. In this article, we reviewed deep learning based automatic segmentation techniques related to lung cancer and compared them with the atlas-based automatic segmentation technique. At present, the auto-segmentation of OARs with relatively large volume such as lung and heart etc. outperforms the organs with small volume such as esophagus. The average Dice similarity coefficient (DSC) of lung, heart and liver are over 0.9, and the best DSC of spinal cord reaches 0.9. However, the DSC of esophagus ranges between 0.71 and 0.87 with a ragged performance. In terms of the gross tumor volume, the average DSC is below 0.8. Although deep learning based automatic segmentation techniques indicate significant superiority in many aspects compared to manual segmentation, various issues still need to be solved. We discussed the potential issues in deep learning based automatic segmentation including low contrast, dataset size, consensus guidelines, and network design. Clinical limitations and future research directions of deep learning based automatic segmentation were discussed as well.

kV-kV and kV-MV DECT based estimation of proton stopping power ratio - a simulation study.

PURPOSE: This study aims to estimate the proton stopping power ratio (SPR) by using 80-120 kV and 120 kV-6 MV dual-energy CT (DECT) in a fully simulation-based approach for proton therapy dose calculations. METHODS: Based on a virtual CT system, a two-step approach is applied to obtain the reference attenuation coefficient for image reconstruction. The effective atomic number (EAN) and electron density ratio (EDR) are estimated from two CT scans. The SPR is estimated using a calibration based on known materials to obtain a piecewise linear relationship between the EAN and the logarithm of the mean excitation energy, lnIm. The calibration phantoms are constructed from reference tissue materials taken from ICRU Report 44. Our approach is evaluated through using the ICRP110 human phantom. The respective influences of noise and beam hardening effects are studied. RESULTS: With the beam hardening correction applied, the results of 120 kV-6 MV DECT are comparable to those of 80-120 kV DECT in predicting the EAN, but the former demonstrated a clear improvement in predicting the EDR and SPR. The 120 kV-6 MV DECT is able to predict the SPR within an accuracy of 10% for lung tissue and 5% for pelvis tissue, thereby outperforming the 80-120 kV DECT. CONCLUSIONS: The 120 kV-6 MV DECT is less sensitive to noise but more susceptible to beam hardening effects. By applying beam hardening correction, the 120 kV-6 MV DECT can predict the SPR more accurately than the 80-120 kV DECT. To utilize our DECT approach most effectively, high-quality reconstructed images are required.

Emergence of a Complete Heavy-Quark Spin Symmetry Multiplet: Seven Molecular Pentaquarks in Light of the Latest LHCb Analysis.

A recent analysis by the LHCb Collaboration suggests the existence of three narrow pentaquarklike states-the P_{c}(4312), P_{c}(4440), and P_{c}(4457)-instead of just one in the previous analysis [the P_{c}(4450)]. The closeness of the P_{c}(4312) to the D[over ¯]Σ_{c} threshold and the P_{c}(4440) and P_{c}(4457) to the D[over ¯]^{*}Σ_{c} threshold suggests a molecular interpretation of these resonances. We show that these three pentaquarklike resonances can be naturally accommodated in a contact-range effective field theory description that incorporates heavy-quark spin symmetry. This description leads to the prediction of all the seven possible S-wave heavy antimeson-baryon molecules [that is, there should be four additional molecular pentaquarks in addition to the P_{c}(4312), P_{c}(4440), and P_{c}(4457)], providing the first example of a heavy-quark spin symmetry molecular multiplet that is complete. If this is confirmed, it will not only give us an impressive example of the application of heavy-quark symmetries and effective field theories in hadron physics, it will also uncover a clear and powerful ordering principle for the molecular spectrum, reminiscent of the SU(3)-flavor multiplets to which the light hadron spectrum conforms.