目的探讨Halo重力牵引对术前顶椎区脊髓形态分型为Ⅲ型的严重脊柱侧后凸畸形患者影像学的改善效果及手术安全性。方法回顾性分析2019年2月至2021年6月南京鼓楼医院收治术前脊髓形态分型为Ⅲ型的47例严重侧后凸畸形患者的病历资料,男18...目的探讨Halo重力牵引对术前顶椎区脊髓形态分型为Ⅲ型的严重脊柱侧后凸畸形患者影像学的改善效果及手术安全性。方法回顾性分析2019年2月至2021年6月南京鼓楼医院收治术前脊髓形态分型为Ⅲ型的47例严重侧后凸畸形患者的病历资料,男18例、女29例;年龄(22.5±12.8)岁(范围9~60岁)。接受Ⅰ期Halo重力牵引、Ⅱ期脊柱后路矫形内固定术治疗,牵引时间为(7.4±3.9)周(范围4~16周)。影像学参数包括牵引前后及手术后即刻的侧凸Cobb角、冠状面平衡[C_(7)铅垂线至骶骨中垂线的距离(C_(7)plumb line and center sacral vertical line,C_(7)PL-CSVL)]、矢状面最大后凸Cobb角及矢状面平衡(sagittal vertical axis,SVA),计算牵引后矫正率和手术后矫正率。采用Frankel分级评估手术前后的神经功能状态。结果47例患者Halo重力牵引及矫形内固定手术均顺利完成。牵引前侧凸Cobb角为116.0°±17.5°,Halo重力牵引后改善至87.9°±16.5°(t=9.10,P<0.001),牵引后矫正率为22.4%±10.3%;手术后改善至69.1°±21.0°(t=15.19,P<0.001),手术后矫正率为41.3%±14.5%。牵引前C_(7)PL-CSVL为(35.7±16.9)mm,手术后改善至(22.0±13.7)mm(t=13.75,P<0.001),手术后矫正率为39.9%±15.5%。牵引前后凸Cobb角为110.9°±22.1°,Halo重力牵引后改善至84.1°±19.9°(t=8.84,P<0.001),牵引后矫正率为23.7%±8.9%;手术后改善至65.3°±19.3°(t=10.63,P<0.001),手术后矫正率为40.1%±20.7%。牵引前SVA为(43.8±19.5)mm,手术后改善至(21.1±14.9)mm(t=10.32,P<0.001),手术后矫正率为53.1%±27.0%。14例患者牵引前存在下肢神经损害症状,8例于Halo重力牵引后神经功能明显改善,3例患者于手术后神经功能改善,余3例治疗过程中神经功能无明显改善。所有患者Halo重力牵引及手术后均未出现原有神经损害加重或出现新发神经损害。结论术前顶椎区脊髓形态分型为Ⅲ型的严重脊柱侧后凸畸形患者术前行Halo重力牵引可有效矫正畸形、改善神经功能、提高脊髓对矫形手术的耐受性及降低术中发生医源性神经损害的风险。展开更多
This paper proposes a synchronous parallel block coordinate descent algorithm for minimizing a composite function,which consists of a smooth convex function plus a non-smooth but separable convex function.Due to the g...This paper proposes a synchronous parallel block coordinate descent algorithm for minimizing a composite function,which consists of a smooth convex function plus a non-smooth but separable convex function.Due to the generalization of the proposed method,some existing synchronous parallel algorithms can be considered as special cases.To tackle high dimensional problems,the authors further develop a randomized variant,which randomly update some blocks of coordinates at each round of computation.Both proposed parallel algorithms are proven to have sub-linear convergence rate under rather mild assumptions.The numerical experiments on solving the large scale regularized logistic regression with 1 norm penalty show that the implementation is quite efficient.The authors conclude with explanation on the observed experimental results and discussion on the potential improvements.展开更多
文摘针对相关滤波在抗遮挡方面效果不佳的问题,本文在ECO_HC(efficient convolution operators handcraft)的基础上提出了一种多特征融合的抗遮挡相关滤波算法。在相关滤波算法的框架下,将目标ULBP(uniform local binary pattern)纹理特征和目标HOG(histogram of oriented gridients)特征进行线性加权融合;在模型建立与更新阶段通过高斯掩码函数缓解循环移位造成的边界效应;通过计算目标最大响应值的峰值均值比来判断目标状态,并将卡尔曼算法作为目标被遮挡后重定位策略。实验结果显示,在16个视频序列上,该文算法的平均精确度达到87.3%,成功率达到76.5%,相比基线算法,分别提升了27.7%和23.7%。
文摘目的探讨Halo重力牵引对术前顶椎区脊髓形态分型为Ⅲ型的严重脊柱侧后凸畸形患者影像学的改善效果及手术安全性。方法回顾性分析2019年2月至2021年6月南京鼓楼医院收治术前脊髓形态分型为Ⅲ型的47例严重侧后凸畸形患者的病历资料,男18例、女29例;年龄(22.5±12.8)岁(范围9~60岁)。接受Ⅰ期Halo重力牵引、Ⅱ期脊柱后路矫形内固定术治疗,牵引时间为(7.4±3.9)周(范围4~16周)。影像学参数包括牵引前后及手术后即刻的侧凸Cobb角、冠状面平衡[C_(7)铅垂线至骶骨中垂线的距离(C_(7)plumb line and center sacral vertical line,C_(7)PL-CSVL)]、矢状面最大后凸Cobb角及矢状面平衡(sagittal vertical axis,SVA),计算牵引后矫正率和手术后矫正率。采用Frankel分级评估手术前后的神经功能状态。结果47例患者Halo重力牵引及矫形内固定手术均顺利完成。牵引前侧凸Cobb角为116.0°±17.5°,Halo重力牵引后改善至87.9°±16.5°(t=9.10,P<0.001),牵引后矫正率为22.4%±10.3%;手术后改善至69.1°±21.0°(t=15.19,P<0.001),手术后矫正率为41.3%±14.5%。牵引前C_(7)PL-CSVL为(35.7±16.9)mm,手术后改善至(22.0±13.7)mm(t=13.75,P<0.001),手术后矫正率为39.9%±15.5%。牵引前后凸Cobb角为110.9°±22.1°,Halo重力牵引后改善至84.1°±19.9°(t=8.84,P<0.001),牵引后矫正率为23.7%±8.9%;手术后改善至65.3°±19.3°(t=10.63,P<0.001),手术后矫正率为40.1%±20.7%。牵引前SVA为(43.8±19.5)mm,手术后改善至(21.1±14.9)mm(t=10.32,P<0.001),手术后矫正率为53.1%±27.0%。14例患者牵引前存在下肢神经损害症状,8例于Halo重力牵引后神经功能明显改善,3例患者于手术后神经功能改善,余3例治疗过程中神经功能无明显改善。所有患者Halo重力牵引及手术后均未出现原有神经损害加重或出现新发神经损害。结论术前顶椎区脊髓形态分型为Ⅲ型的严重脊柱侧后凸畸形患者术前行Halo重力牵引可有效矫正畸形、改善神经功能、提高脊髓对矫形手术的耐受性及降低术中发生医源性神经损害的风险。
基金supported by the National Key R&D Program of China under Grant No.2018YFC0830300。
文摘This paper proposes a synchronous parallel block coordinate descent algorithm for minimizing a composite function,which consists of a smooth convex function plus a non-smooth but separable convex function.Due to the generalization of the proposed method,some existing synchronous parallel algorithms can be considered as special cases.To tackle high dimensional problems,the authors further develop a randomized variant,which randomly update some blocks of coordinates at each round of computation.Both proposed parallel algorithms are proven to have sub-linear convergence rate under rather mild assumptions.The numerical experiments on solving the large scale regularized logistic regression with 1 norm penalty show that the implementation is quite efficient.The authors conclude with explanation on the observed experimental results and discussion on the potential improvements.
