公开(公告)号：US07085348B2

公开(公告)日：2006-08-01

申请号：US10736023

申请日：2003-12-15

申请人： Srijit Kamath ,  Sartaj Sahni ,  Jantinder Palta ,  Sanjay Ranka ,  Jonathan G. Li

发明人： Srijit Kamath ,  Sartaj Sahni ,  Jantinder Palta ,  Sanjay Ranka ,  Jonathan G. Li

IPC分类号： A61N5/10

CPC分类号： A61N5/103 ,  A61N5/1036 ,  A61N5/1042

摘要： A method of delivering radiation treatment using multi-leaf collimation includes the step of providing a radiation fluence map which includes an intensity profile. The fluence map is converted into a preliminary leaf sequence, wherein the preliminary leaf sequence minimizes machine on-time and is generated without leaf movement constraints. The leaf movement constraint is imposed on the preliminary leaf sequence. At least one constraint elimination algorithm is then applied, the algorithm adjusting the preliminary leaf sequence to minimize violations of the constraint while providing the desired fluence map and minimized radiation on-time. The method can be applied to SMLC and DLMC systems, and can include adjustment for the tongue-and-groove effect.

摘要翻译： 使用多叶准直进行放射治疗的方法包括提供包括强度分布的辐射注量图的步骤。 注量图被转换成初步的叶子序列，其中初步叶子序列使机器准时最小化，并且不产生叶片运动限制。 叶片运动约束对初步叶序列施加。 然后应用至少一个约束消除算法，该算法调整初步叶序列以最小化对约束的违反，同时提供期望的通量图和最小化的辐射on-time。 该方法可以应用于SMLC和DLMC系统，并且可以包括对榫槽效应的调整。

公开(公告)号：US07142635B2

公开(公告)日：2006-11-28

申请号：US11102083

申请日：2005-04-08

申请人： Srijit Kamath ,  Sartaj Sahni ,  Jonathan Gang Li ,  Jatinder Palta ,  Sanjay Ranka

发明人： Srijit Kamath ,  Sartaj Sahni ,  Jonathan Gang Li ,  Jatinder Palta ,  Sanjay Ranka

IPC分类号： A61N5/10

CPC分类号： A61N5/1042 ,  A61N5/1036

摘要： A method of delivering intensity modulated radiation therapy (IMRT) is disclosed. An intensity profile for the treatment of a patient is provided which spans a prescribed field width and includes a discrete profile having intensity values at each of a plurality of sample points bounded by the prescribed width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity profile is split into a plurality of intensity profile portions, each having respective widths less than the maximum width if the prescribed width is greater than the maximum width. The prescribed field is also divided into a plurality of different profile portion split arrangements. A monitor unit (MU) efficiency is calculated for each of the arrangements. One of the arrangements is selected for delivery by the system using a leaf sequencing method.

摘要翻译： 公开了一种递送强度调制放射治疗（IMRT）的方法。 提供了用于治疗患者的强度分布，其跨越规定的场宽度并且包括在由规定宽度限定的多个采样点中的每一个处具有强度值的离散轮廓。 将规定的宽度与辐射处理系统提供的最大场宽进行比较。 如果规定的宽度大于最大宽度，则强度分布被分成多个强度分布部分，每个强度分布部分各自具有小于最大宽度的宽度。 规定的场也被分成多个不同的轮廓部分割装置。 针对每个布置计算监视器单元（MU）的效率。 选择其中一种安排用于通过系统使用叶片测序方法递送。

公开(公告)号：US20050254623A1

公开(公告)日：2005-11-17

申请号：US11102083

申请日：2005-04-08

申请人： Srijit Kamath ,  Sartaj Sahni ,  Jonathan Li ,  Jatinder Palta ,  Sanjay Ranka

发明人： Srijit Kamath ,  Sartaj Sahni ,  Jonathan Li ,  Jatinder Palta ,  Sanjay Ranka

IPC分类号： A61N5/10

CPC分类号： A61N5/1042 ,  A61N5/1036

摘要： A method of delivering intensity modulated radiation therapy (IMRT) is disclosed. An intensity profile for the treatment of a patient is provided which spans a prescribed field width and includes a discrete profile having intensity values at each of a plurality of sample points bounded by the prescribed width. The prescribed width is compared to a maximum field width provided by the radiation treatment system. The intensity profile is split into a plurality of intensity profile portions, each having respective widths less than the maximum width if the prescribed width is greater than the maximum width. The prescribed field is also divided into a plurality of different profile portion split arrangements. A monitor unit (MU) efficiency is calculated for each of the arrangements. One of the arrangements is selected for delivery by the system using a leaf sequencing method.

摘要翻译： 公开了一种递送强度调制放射治疗（IMRT）的方法。 提供了用于治疗患者的强度分布，其跨越规定的场宽度并且包括在由规定宽度限定的多个采样点中的每一个处具有强度值的离散轮廓。 将规定的宽度与辐射处理系统提供的最大场宽进行比较。 如果规定的宽度大于最大宽度，则强度分布被分成多个强度分布部分，每个强度分布部分各自具有小于最大宽度的宽度。 规定的场也被分成多个不同的轮廓部分割装置。 针对每个布置计算监视器单元（MU）的效率。 选择其中一种安排用于通过系统使用叶片测序方法递送。

公开(公告)号：US06563952B1

公开(公告)日：2003-05-13

申请号：US09420252

申请日：1999-10-18

申请人： Anurag Srivastava ,  G. D. Ramkumar ,  Vineet Singh ,  Sanjay Ranka

发明人： Anurag Srivastava ,  G. D. Ramkumar ,  Vineet Singh ,  Sanjay Ranka

IPC分类号： G06K962

CPC分类号： G06K9/6276

摘要： The present invention is an apparatus and method for classifying high-dimensional sparse datasets. A raw data training set is flattened by converting it from categorical representation to a boolean representation. The flattened data is then used to build a class model on which new data not in the training set may be classified. In one embodiment, the class model takes the form of a decision tree, and large itemsets and cluster information are used as attributes for classification. In another embodiment, the class model is based on the nearest neighbors of the data to be classified. An advantage of the invention is that, by flattening the data, classification accuracy is increased by eliminating artificial ordering induced on the attributes. Another advantage is that the use of large itemsets and clustering increases classification accuracy.

摘要翻译： 本发明是用于对高维稀疏数据集进行分类的装置和方法。 原始数据训练集通过将其从分类表示转换为布尔表示而被平坦化。 然后，使用平坦化的数据来构建一个类别模型，在该类模型中，不在训练集中的新数据可以被分类。 在一个实施例中，类模型采用决策树的形式，并且使用大的项目集和集群信息作为分类的属性。 在另一个实施例中，类模型基于要分类的数据的最近邻。 本发明的优点在于，通过平坦化数据，通过消除对属性引起的人为排序来增加分类精度。 另一个优点是使用大项集和聚类提高了分类精度。

公开(公告)号：US06173280B2

公开(公告)日：2001-01-09

申请号：US09065837

申请日：1998-04-24

申请人： G D Ramkumar ,  Sanjay Ranka ,  Shalom Tsur

发明人： G D Ramkumar ,  Sanjay Ranka ,  Shalom Tsur

IPC分类号： G06F1730

CPC分类号： G06F17/30539 ,  G06F2216/03 ,  Y10S707/99936 ,  Y10S707/99943

摘要： The present invention discloses a data mining method and apparatus that assigns weight values to items and/or transactions based on the value to the user, thereby resulting in association rules of greater importance. A conservative method, aggressive method, or a combination of the two can be used when generating supersets.

摘要翻译： 本发明公开了一种数据挖掘方法和装置，该方法和装置根据该用户的价值向物品和/或交易分配权重值，从而产生更重要的关联规则。 当产生超集时，可以使用保守的方法，积极的方法或两者的组合。

公开(公告)号：US5983224A

公开(公告)日：1999-11-09

申请号：US962470

申请日：1997-10-31

申请人： Vineet Singh ,  Sanjay Ranka ,  Khaled Alsabti

发明人： Vineet Singh ,  Sanjay Ranka ,  Khaled Alsabti

IPC分类号： G06F17/30 ,  G06K9/62

CPC分类号： G06K9/6223 ,  G06F17/30412 ,  G06K9/6282 ,  Y10S707/99933 ,  Y10S707/99934 ,  Y10S707/99935 ,  Y10S707/99936

摘要： The present invention is directed to an improved data clustering method and apparatus for use in data mining operations. The present invention determines the pattern vectors of a k-d tree structure which are closest to a given prototype cluster by pruning prototypes through geometrical constraints, before a k-means process is applied to the prototypes. For each sub-branch in the k-d tree, a candidate set of prototypes is formed from the parent of a child node. The minimum and maximum distances from any point in the child node to any prototype in the candidate set is determined. The smallest of the maximum distances found is compared to the minimum distances of each prototype in the candidate set. Those prototypes with a minimum distance greater than the smallest of the maximum distances are pruned or eliminated. Pruning the number of remote prototypes reduces the number of distance calculations for the k-means process, significantly reducing the overall computation time.

摘要翻译： 本发明涉及用于数据挖掘操​​作的改进的数据聚类方法和装置。 本发明通过在将k-means过程应用于原型之前通过几何约束修剪原型来确定最靠近给定原型群的k-d树结构的模式向量。 对于k-d树中的每个子分支，从子节点的父节点形成候选的原型集合。 确定子节点中任何点到候选集中任何原型的最小和最大距离。 找到的最大距离中的最小距离与候选集中每个原型的最小距离进行比较。 最小距离大于最大距离最小距离的原型被修剪或消除。 修剪远程原型的数量减少了k-means过程的距离计算次数，从而大大减少了整个计算时间。