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Xiaobo S Hu

Publications and source records attributed to Xiaobo S Hu.

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An improved MLC segmentation algorithm and software for step-and-shoot IMRT delivery without tongue-and-groove error.

We present an improved multileaf collimator (MLC) segmentation algorithm, denoted by SLS(NOTG) (static leaf sequencing with no tongue-and-groove error), for step-and-shoot intensity-modulated radiation therapy (IMRT) delivery. SLS(NOTG) is an improvement over the MLC segmentation algorithm called SLS that was developed by Luan et al. [Med. Phys. 31(4), 695-707 (2004)], which did not consider tongue-and-groove error corrections. The aims of SLS(NOTG) are (1) shortening the treatment times of IMRT plans by minimizing their numbers of segments and (2) minimizing the tongue-and-groove errors of the computed IMRT plans. The input to SLS(NOTG) is intensity maps (IMs) produced by current planning systems, and its output is (modified) optimized leaf sequences without tongue-and-groove error. Like the previous SLS algorithm [Luan et al., Med. Phys. 31(4), 695-707 (2004)], SLS(NOTG) is also based on graph algorithmic techniques in computer science. It models the MLC segmentation problem as a weighted minimum-cost path problem, where the weight of the path is the number of segments and the cost of the path is the amount of tongue-and-groove error. Our comparisons of SLS(NOTG) with CORVUS indicated that for the same intensity maps, the numbers of segments computed by SLS(NOTG) are up to 50% less than those by CORVUS 5.0 on the Elekta LINAC system. Our clinical verifications have shown that the dose distributions of the SLS(NOTG) plans do not have tongue-and-groove error and match those of the corresponding CORVUS plans, thus confirming the correctness of SLS(NOTG). Comparing with existing segmentation methods, SLS(NOTG) also has two additional advantages: (1) SLS(NOTG) can compute leaf sequences whose tongue-and-groove error is minimized subject to a constraint on the maximum allowed number of segments, which may be desirable in clinical situations where a treatment with the complete correction of tongue-and-groove error takes too much time, and (2) SLS(NOTG) can be used to minimize a more general type of error called the tongue-or-groove error.

Algorithms↗

A new MLC segmentation algorithm/software for step-and-shoot IMRT delivery.

We present a new MLC segmentation algorithm/software for step-and-shoot IMRT delivery. Our aim in this work is to shorten the treatment time by minimizing the number of segments. Our new segmentation algorithm, called SLS (an abbreviation for static leaf sequencing), is based on graph algorithmic techniques in computer science. It takes advantage of the geometry of intensity maps. In our SLS approach, intensity maps are viewed as three-dimensional (3-D) "mountains" made of unit-sized "cubes." Such a 3-D "mountain" is first partitioned into special-structured submountains using a new mixed partitioning scheme. Then the optimal leaf sequences for each submountain are computed by either a shortest-path algorithm or a maximum-flow algorithm based on graph models. The computations of SLS take only a few minutes. Our comparison studies of SLS with CORVUS (both the 4.0 and 5.0 versions) and with the Xia and Verhey segmentation methods on Elekta Linac systems showed substantial improvements. For instance, for a pancreatic case, SLS used only one-fifth of the number of segments required by CORVUS 4.0 to create the same intensity maps, and the SLS sequences took only 25 min to deliver on an Elekta SL 20 Linac system in contrast to the 72 min for the CORVUS 4.0 sequences (a three-fold improvement). To verify the accuracy of our new leaf sequences, we conducted film and ion-chamber measurements on phantom. The results showed that both the intensity distributions as well as dose distributions of the SLS delivery match well with those of CORVUS delivery. SLS can also be extended to other types of Linac systems.

Algorithms↗