| pubmed-article:18401066 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C1706515 | lld:lifeskim |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C1704640 | lld:lifeskim |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C0599880 | lld:lifeskim |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C0678536 | lld:lifeskim |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C1709694 | lld:lifeskim |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C0336791 | lld:lifeskim |
| pubmed-article:18401066 | lifeskim:mentions | umls-concept:C0331858 | lld:lifeskim |
| pubmed-article:18401066 | pubmed:issue | 9 | lld:pubmed |
| pubmed-article:18401066 | pubmed:dateCreated | 2008-4-18 | lld:pubmed |
| pubmed-article:18401066 | pubmed:abstractText | An optimal plan in modern treatment planning tools is found through the use of an iterative optimization algorithm, which deals with a high amount of patient-related data and number of treatment parameters to be optimized. Thus, calculating a good plan is a very time-consuming process which limits the application for patients in clinics and for research activities aiming for more accuracy. A common technique to handle the vast amount of radiation dose data is the concept of the influence matrix (DIJ), which stores the dose contribution of each bixel to the patient in the main memory of the computer. This study revealed that a bottleneck for the optimization time arises from the data transfer of the dose data between the memory and the CPU. In this note, we introduce a new method which speeds up the data transportation from stored dose data to the CPU. As an example we used the DIJ approach as is implemented in our treatment planning tool KonRad, developed at the German Cancer Research Center (DKFZ) in Heidelberg. A data cycle reordering method is proposed to take the advantage of modern memory hardware. This induces a minimal eviction policy which results in a memory behaviour exhibiting a 2.6 times faster algorithm compared to the naive implementation. Although our method is described for the DIJ approach implemented in KonRad, we believe that any other planning tool which uses a similar approach to store the dose data will also benefit from the described methods. | lld:pubmed |
| pubmed-article:18401066 | pubmed:language | eng | lld:pubmed |
| pubmed-article:18401066 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:18401066 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:18401066 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:18401066 | pubmed:month | May | lld:pubmed |
| pubmed-article:18401066 | pubmed:issn | 0031-9155 | lld:pubmed |
| pubmed-article:18401066 | pubmed:author | pubmed-author:LudwigThomasT | lld:pubmed |
| pubmed-article:18401066 | pubmed:author | pubmed-author:NillSimeonS | lld:pubmed |
| pubmed-article:18401066 | pubmed:author | pubmed-author:OelfkeUweU | lld:pubmed |
| pubmed-article:18401066 | pubmed:author | pubmed-author:WilkensJan... | lld:pubmed |
| pubmed-article:18401066 | pubmed:author | pubmed-author:ZiegenheinPet... | lld:pubmed |
| pubmed-article:18401066 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:18401066 | pubmed:day | 7 | lld:pubmed |
| pubmed-article:18401066 | pubmed:volume | 53 | lld:pubmed |
| pubmed-article:18401066 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:18401066 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:18401066 | pubmed:pagination | N157-64 | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:meshHeading | pubmed-meshheading:18401066... | lld:pubmed |
| pubmed-article:18401066 | pubmed:year | 2008 | lld:pubmed |
| pubmed-article:18401066 | pubmed:articleTitle | Speed optimized influence matrix processing in inverse treatment planning tools. | lld:pubmed |
| pubmed-article:18401066 | pubmed:affiliation | German Cancer Research Center (DKFZ), Department of Medical Physics in Radiation Oncology, Heidelberg, Germany. p.ziegenhein@dkfz.de | lld:pubmed |
| pubmed-article:18401066 | pubmed:publicationType | Journal Article | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:18401066 | lld:pubmed |
