Blackwell Chen (burstfish4)

When using the T_sum for timing detection, the CTR full width at half-maximum (FWHM) values were ~100 ps regardless of the scintillator structure. However, when using the Max signal approach, the CTRs of the monolithic plates, laser-processing arrays, and fine-pitch laser-processing arrays were drastically degraded with increasing thickness. On the other hand, the CTRs of the pixelated arrays exhibited almost no degradation. To improve the CTRs of the monolithic plate and the (fine pitch) laser-processing array that exhibit a large light spread in the scintillator block, we applied the P_sum and Ave methods. The resulting CTRs significantly improved upon using P_sum; however, the Ave approach only worked for thicknesses of >6 mm.Objectives.To test the effect of traditional up-sampling slice thickness (ST) methods on the reproducibility of CT radiomics features of liver tumors and investigate the improvement using a deep neural network (DNN) scheme.Methods.CT images with ≤ 1 mm ST in the public dataset were converted to low-resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm ST and 5 mm ST to 1 mm ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Gypenoside L order Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.Results.CT images of 108 patients were used for training (n = 63), validation (n = 11) and testing (n = 34). The DNN method showed significantly higher PSNR and SSIM values (p less then 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3-1 mm, and from 305(64%) to 353(74%) for the conversion of 5-1 mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.Conclusions.The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.Objective.To evaluate the cerebral autoregulation (CA) in idiopathic intracranial hypertension (IIH) patients with transfer function analysis, and to explore its improvement after venous sinus stenting.Approach. In total, 15 consecutive IIH patients with venous sinus stenosis and 15 controls were recruited. All the patients underwent digital subtraction angiography and venous manometry. Venous sinus stenting was performed for IIH patients with a trans-stenosis pressure gradient ≥8 mmHg. CA was assessed before and after the operation with transfer function analysis, by using the spontaneous oscillations of the cerebral blood flow velocity in the bilateral middle cerebral artery and blood pressure.Main results. Compared with controls, the autoregulatory parameters, phase shift and rate of recovery, were both significantly lower in IIH patients [(57.94° ± 23.22° versus 34.59° ± 24.15°,p less then 0.001; (39.87 ± 21.95) %/s versus (20.56 ± 46.66) %/s,p= 0.045, respectively). In total, six patients with bilateral transverse or sigmoid sinus stenosis received venous sinus stenting, in whom, the phase shift significantly improved after venous sinus stenting (39.62° ± 20.26° versus 22.79° ± 19.96°,p = 0.04).Significance. The study revealed that dynamic CA was impaired in IIH patients and was improved after venous sinus stenting. CA assessment has the potential to be used for investigating the hemodynamics