PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10421549

Synchronizing dynamic multileaf collimators for producing two-dimensional intensity-modulated fields with minimum beam delivery time.

L Ma·A L Boyer·C M Ma·L Xing
View original source ↗Export metadata ↓

Abstract

PURPOSE: Leaf motion synchronization of dynamic multileaf collimators (DMLC) for intensity-modulated radiotherapy (IMRT) is important in improving dose distribution and reducing "tongue-and-groove" effects for a prescribed intensity profile. Leaf synchronization could also be used in transforming a one-dimensional leaf-setting algorithm into a two-dimensional leaf-setting algorithm. In this work, we aim to develop a generalized leaf synchronization method for delivering IMRT with the minimized beam delivery time and the optimized subfield variations for a leaf-setting sequence. METHODS AND MATERIALS: With the leaf synchronization procedure, all active MLC leaf pairs start and finish off a leaf sequence simultaneously. In this work, the MLC leaf pairs were synchronized under the condition that the resulting leaf sequence produces the desired intensity profile with the minimum beam delivery time. The parameter of the leaf synchronization function was determined through the least-square minimization of the area variations of all subfields within a leaf sequence. The leaf synchronization and optimization procedure were applied and analyzed for clinical relevant intensity profiles for treating the head-and-neck cancer patients using IMRT. RESULTS: The total monitor units and the optimized beam delivery time of generating a two-dimensional intensity profile was proven through this work to be the global minimum of all leaf-setting sequences including the unsynchronized leaf-setting sequences. The optimized parameter for subfield variations of the synchronized leaf trajectories was found to be dependent on individual intensity profiles. For all our studied cases, the unsynchronized leaf trajectories always have significantly larger subfield variations than the synchronized leaf trajectories. CONCLUSION: It is important and also feasible to synchronize and optimize dynamic MLC leaf motions while still keeping the total beam delivery time minimum for delivering arbitrary two-dimensional intensity-modulated fields.

Explore related subjects

AlgorithmsCalibrationHead and Neck NeoplasmsHumansPhysical PhenomenaPhysicsRadiotherapy, ConformalJournal ArticleResearch Support, U.S. Gov't, P.H.S.

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

L Ma, A L Boyer, C M Ma, L Xing. 1999-07-15. Synchronizing dynamic multileaf collimators for producing two-dimensional intensity-modulated fields with minimum beam delivery time.. https://doi.org/10.1016/s0360-3016(99)00121-2

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

A note on a generalized single step theory for any number of hierarchical genomic matrices.

BACKGROUND: The Single Step algorithm allows combining information from genotyped and un-genotyped individuals, provided they are connected by a pedigree. However, current single step theory is limited to a single list of markers. RESULTS: We present a generalized single step (GSS) method that can accommodate any number of hierarchical molecular datasets (e.g. sequence, high and low density arrays) and pedigree, avoiding imputation. We prove that a similar efficient inversion algorithm exists. The method is recursive, starting with the highest marker density scenario. We illustrate the method with simulation and show that GSS can increase predictive accuracy compared to standard single step. R code is provided so that custom scenarios can be easily compared, either with simulated or real data. CONCLUSION: The method developed generalizes extant single step theory to any number of hierarchical molecular relationship matrices, broadening the scenarios where single step can be applied. A topic of particular interest can be ecology field data or human populations where pedigree is not available, but where samples sequenced and genotyped at different densities can exist. GSS can also be a useful tool to optimize allocation of genotyping and / or sequencing resources.

Algorithms↗
PubMed2026

cgDist: Nucleotide-level distance calculation from cgMLST allelic profiles.

Bacterial genomic surveillance requires balancing computational efficiency with genetic resolution for effective cluster investigation. cgMLST distance calculations treat all allelic differences as equivalent units, obscuring nucleotide-level variation. Furthermore, single nucleotide polymorphism-based pipelines provide finer resolution at substantially higher computational cost, which limits their routine deployment in surveillance laboratories. We present cgDist, an algorithm that calculates nucleotide-level distances directly from cgMLST allelic profiles, providing finer resolution than allele-count distances by leveraging within-allele nucleotide variation. The cache architecture stores alignment statistics, enabling distance calculation modes without computation and supporting both dataset-specific and schema-complete cache generation. This design enables incremental surveillance analysis, with performance benefits as laboratories accumulate alignment data. cgDist functions as a precision 'zoom lens' for the investigation of clusters identified through initial cgMLST screening. Rather than restructuring population relationships, this targeted approach concentrates enhanced resolution where it is most informative. The algorithm ensures that cgDist distances are greater than or equal to corresponding cgMLST distances, preserving epidemiological interpretability while adding genetic discrimination. By increasing resolution within identified clusters, cgDist may also support outbreak investigation, a potential application that remains to be evaluated on outbreak-derived data.

Algorithms↗
PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗