We have synthesized high-quality single crystals of volborthite, a seemingly distorted kagome antiferromagnet, and carried out high-field magnetization measurements up to 74 T and ^{51}V NMR measurements up to 30 T. An extremely wide 1/3 magnetization plateau appears above 28 T and continues over 74 T at 1.4 K, which has not been observed in previous studies using polycrystalline samples. NMR spectra reveal an incommensurate order (most likely a spin-density wave order) below 22 T and a simple spin structure in the plateau phase. Moreover, a novel intermediate phase is found between 23 and 26 T, where the magnetization varies linearly with magnetic field and the NMR spectra indicate an inhomogeneous distribution of the internal magnetic field. This sequence of phases in volborthite bears a striking similarity to those of frustrated spin chains with a ferromagnetic nearest-neighbor coupling J_{1} competing with an antiferromagnetic next-nearest-neighbor coupling J_{2}.

IntroductionCone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality.MethodsCBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCTr). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTr images. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCTr using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM).ResultsThe image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method.ConclusionWe have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner.

Purpose Cone‐beam computed tomography (CBCT) offers advantages over conventional fan‐beam CT in that it requires a shorter time and less exposure to obtain images. However, CBCT images suffer from low soft‐tissue contrast, noise, and artifacts compared to conventional fan‐beam CT images. Therefore, it is essential to improve the image quality of CBCT. Methods In this paper, we propose a synthetic approach to translate CBCT images with deep neural networks. Our method requires only unpaired and unaligned CBCT images and planning fan‐beam CT (PlanCT) images for training. The CBCT images and PlanCT images may be obtained from other patients as long as they are acquired with the same scanner settings. Once trained, three‐dimensionally reconstructed CBCT images can be directly translated into high‐quality PlanCT‐like images. Results We demonstrate the effectiveness of our method with images obtained from 20 prostate patients, and provide a statistical and visual comparison. The image quality of the translated images shows substantial improvement in voxel values, spatial uniformity, and artifact suppression compared to those of the original CBCT. The anatomical structures of the original CBCT images were also well preserved in the translated images. Conclusions Our method produces visually PlanCT‐like images from CBCT images while preserving anatomical structures.

We have performed NMR experiments on the quasi one-dimensional frustrated spin-1/2 system LiCuVO4 in magnetic fields H applied along the c-axis up to field values near the saturation field Hsat. For the field range Hc2 < H < Hc3 (µ0Hc2 ≈ 7.5 T and µ0Hc3 = [40.5 ± 0.2] T) the 51 V NMR spectra at T = 380 mK exhibit a characteristic double-horn pattern, as expected for a spinmodulated phase in which the magnetic moments of Cu 2+ ions are aligned parallel to the applied field H and their magnitudes change sinusoidally along the magnetic chains. For higher fields, the 51 V NMR spectral shape changes from the double-horn pattern into a single Lorentzian line. For this Lorentzian line, the internal field at the 51 V nuclei stays constant for µ0H > 41.4 T, indicating that the majority of magnetic moments in LiCuVO4 are already saturated in this field range. This result is inconsistent with the previously observed linear field dependence of the magnetization M (H) for Hc3 < H < Hsat with µ0Hsat = 45 T [L. E. Svistov et al., JETP Letters 93, 21 (2011)]. We argue that the discrepancy is due to non-magnetic defects in the samples. The results of the spin-lattice relaxation rate of 7 Li nuclei indicate an energy gap which grows with field twice as fast as the Zeeman energy of a single spin, therefore, suggesting that the two-magnon bound state is the lowest energy excitation. The energy gap tends to close at µ0H ≈ 41 T. Our results suggest that the theoretically predicted spin-nematic phase, if it exists in LiCuVO4, can be established only within the narrow field range 40.5 < µ0H < 41.4 T .