Ultrasound diffraction tomography (UDT) possesses the characteristics of high resolution, sensitive to dense tissue, and has high application value in clinics. To suppress the artifact and improve the quality of reconstructed image, classical interpolation method needs to be improved by increasing the number of projections and channels, which will increase the scanning time and the complexity of the imaging system. In this study, we tried to accurately reconstruct the object from limited projection based on compressed sensing. Firstly, we illuminated the object from random angles with limited number of projections. Then we obtained spatial frequency samples through Fourier diffraction theory. Secondly, we formulated the inverse problem of UDT by exploring the sparsity of the object. Thirdly, we solved the inverse problem by conjugate gradient method to reconstruct the object. We accurately reconstructed the object using the proposed method. Not only can the proposed method save scanning time to reduce the distortion by respiratory movement, but also can reduce cost and complexity of the system. Compared to the interpolation method, our method can reduce the reconstruction error and improve the structural similarity.
Algorithms ; Artifacts ; Image Processing, Computer-Assisted ; Tomography ; Ultrasonics

Objective To analyze the learning curve of Da Vinci robotic segmentectomy. Methods Cumulative sum analysis (CUSUM) was used to analyze the learning curve of Da Vinci robotic segmentectomy performed by the General Hospital of Northern Theater Command from February 2018 to December 2020. The learning curve was obtained by fitting, and R2 was used to judge the goodness of fitting. The clinical data of patients in different stages of learning curve were compared and analyzed. Results The first 50 patients who received Da Vinci robotic segmentectomy were included, including 24 males and 26 females, with an average age of 61.9±10.6 years. The operation time decreased gradually with the accumulation of operation patients. The goodness of fitting coefficient reached the maximum value when R2=0.907 (P<0.001), CUSUM (n) =0.009×n3−0.953×n2+24.968×n−7.033 (n was the number of patients). The fitting curve achieved vertex crossing when the number of patients reached 17. Based on this, 50 patients were divided into two stages: a learning and improving stage and a mastering stage. There were statistical differences in the operation time, intraoperative blood loss, postoperative drainage volume, number of lymph node dissection, postoperative catheter time, postoperative hospital stay, and postoperative complications between the two stages (P<0.05). Conclusion It shows that the technical competency for assuring feasible perioperative outcomes can be achieved when the cumulative number of surgical patients reaches 17.