Middleton Lowry (wavebody88)

The improvement in FMD and baPWV had additive effects on risk reduction of the achievement of the optimal goals of traditional risk factors in patients with CAD. Thus, serial measurements of FMD and baPWV may be useful for identifying CAD patients at residual risk for adverse cardiovascular events following OMT. The improvement in FMD and baPWV had additive effects on risk reduction of the achievement of the optimal goals of traditional risk factors in patients with CAD. Thus, serial measurements of FMD and baPWV may be useful for identifying CAD patients at residual risk for adverse cardiovascular events following OMT. Volumetric modulated arc therapy (VMAT) can acquire projection images during rotational irradiation, and cone-beam computed tomography (CBCT) images during VMAT delivery can be reconstructed. The poor quality of CBCT images prevents accurate recognition of organ position during the treatment. The purpose of this study was to improve the image quality of CBCT during the treatment by cycle generative adversarial network (CycleGAN). Twenty patients with clinically localized prostate cancer were treated with VMAT, and projection images for intra-treatment CBCT (iCBCT) were acquired. Synthesis of PCT (SynPCT) with improved image quality by CycleGAN requires only unpaired and unaligned iCBCT and planning CT (PCT) images for training. We performed visual and quantitative evaluation to compare iCBCT, SynPCT and PCT deformable image registration (DIR) to confirm the clinical usefulness. We demonstrated suitable CycleGAN networks and hyperparameters for SynPCT. The image quality of SynPCT improved visually and quantitatively while preserving anatomical structures of the original iCBCT. #link# The undesirable deformation of PCT was reduced when SynPCT was used as its reference instead of iCBCT. We have performed image synthesis with preservation of organ position by CycleGAN for iCBCT and confirmed the clinical usefulness. We have performed image synthesis with preservation of organ position by CycleGAN for iCBCT and confirmed the clinical usefulness. The noise generated in ultra-high-resolution computed tomography (U-HRCT) images affects the quantitative analysis of emphysema. In this study, we compared the physical properties of reconstructed images for hybrid iterative reconstruction (HIR) and deep learning reconstruction (DLR), which are reconstruction methods for reducing image noise. Using clinical evaluation, we evaluated the correlation between low attenuation volume (LAV) % obtained by CT and forced expiratory volume in 1 s per forced vital capacity (FEV /FVC) obtained by respiratory function tests. CT data obtained by HIR and DLR were used for analysis (matrix size 1024´1024, slice thickness 0.25 mm). The physical characteristics were evaluated for the modulation transfer function (MTF) and noise power spectrum (NPS). Display-field of view (D-FOV) was analyzed by varying between 300 mm and 400 mm. The clinical data evaluated the relationship between LAV% and FEV /FVC by Spearman's correlation coefficient. The 10% MTFs were 1.3 cycles/mm (HIR) and 1.3 cycles/mm (DLR) at D-FOV 300 mm, and 1.2 cycles/mm (HIR) and 1.1 cycles/mm (DLR) at D-FOV 400 mm. The NPS had less noise in DLR than HIR in all frequency ranges. The correlation coefficients between LAV% and FEV /FVC were 0.64 and 0.71, respectively, in HIR and DLR. There was no difference in the resolution characteristics of HIR and DLR. DLR had better noise characteristics than HIR. The correlation between LAV% measured by HIR and DLR and FEV /FVC is equivalent. The noise characteristics of the DLR enable the reduction of exposure to emphysema quantitative analysis by CT. There was no difference in the resolution characteristics of HIR and DLR. DLR had better noise characteristics than HIR. The correlation between LAV% measured by HIR and DL