Research on real-time detection system of catheter delivering force in vascular interventional robots / 生物医学工程学杂志

In existing vascular interventional surgical robots, it is difficult to accurately detect the delivery force of the catheter/guidewire at the slave side. Aiming to solve this problem, a real-time force detection system was designed for vascular interventional surgical (VIS) robots based on catheter push force. Firstly, the transfer process of catheter operating forces in the slave end of the interventional robot was analyzed and modeled, and the design principle of the catheter operating force detection system was obtained. Secondly, based on the principle of stress and strain, a torque sensor was designed and integrated into the internal transmission shaft of the slave end of the interventional robot, and a data acquisition and processing system was established. Thirdly, an ATI high-precision torque sensor was used to build the experimental platform, and the designed sensor was tested and calibrated. Finally, sensor test experiments under ideal static/dynamic conditions and simulated catheter delivery tests based on actual human computed tomography (CT) data and vascular model were carried out. The results showed that the average relative detection error of the designed sensor system was 1.26% under ideal static conditions and 1.38% under ideal dynamic stability conditions. The system can detect on-line catheter operation force at high precision, which is of great significance towards improving patient safety in interventional robotic surgery.

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

Objective@#To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. @*Materials and Methods@#Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signedrank test were performed to compare the objective measurements and the subjective image quality scores, respectively. @*Results@#With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. @*Conclusion@#The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

Objective@#To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. @*Materials and Methods@#Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signedrank test were performed to compare the objective measurements and the subjective image quality scores, respectively. @*Results@#With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. @*Conclusion@#The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

Clinicopathological features and prognosis of immunoglobulin A nephropathy after renal transplantation / 中华器官移植杂志

Objective:To summarize the relationship between the clinicopathological features and prognosis of immunoglobulin A nephropathy (IgAN) after renal transplantation.Methods:A total of 34 patients with IgAN after renal transplantation confirmed by renal biopsy were enrolled. And another 34 patients with primary IgAN confirmed by initial renal biopsy were adopted as controls. Clinical and pathological features of two groups were compared to explore the relationship between clinicopathological features and prognosis of allograft IgAN.Results:As compared with primary IgAN group, renal function in allograft IgAN group included serum creatinine [(158.5±75.9) vs (84.8±26.8) umol/L], urea nitrogen [(9.7±6.1) vs (5.2±1.4) mmol/L], uric acid [(406.7±87.8) vs (359.0±92.6) umol/L], estimated glomerular filtration rate {(57.4±25.4) vs (91.2±28.6) [ml/(min·1.73m 2)]}. All were statistically significantly higher ( P<0.05) while other parameters showed no differences. Pathologically, the proportion of T1 type (50.0% vs 17.6%) of renal tubular atrophy/interstitial fibrosis was significantly higher in allograft IgAN group than control group ( P<0.05). Furthermore, univariate and multivariate Logistic regression analyses were performed between various pathological parameters and prognosis in allograft IgAN patients. It indicated that the degree of mesangial hyperplasia of patients with transplanted IgAN had a significantly negative impact on the prognosis. Conclusions:The clinicopathological features of patients with allograft IgAN show no difference from those of patients with primary IgAN. And among patients with allograft IgAN, those with severe mesangial hyperplasia often have a worse prognosis.