guest ::
| Home > Publications database > Proton dose calculation with LSTM networks in presence of a magnetic field. > print |
| Home > Publications database > Proton dose calculation with LSTM networks in presence of a magnetic field. > print |
| 001 | 293598 | ||
| 005 | 20251028115603.0 | ||
| 024 | 7 | _ | |a 10.1088/1361-6560/ad7f1e |2 doi |
| 024 | 7 | _ | |a pmid:39317232 |2 pmid |
| 024 | 7 | _ | |a 0031-9155 |2 ISSN |
| 024 | 7 | _ | |a 1361-6560 |2 ISSN |
| 037 | _ | _ | |a DKFZ-2024-01915 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 530 |
| 100 | 1 | _ | |a Radonic, Domagoj |b 0 |
| 245 | _ | _ | |a Proton dose calculation with LSTM networks in presence of a magnetic field. |
| 260 | _ | _ | |a Bristol |c 2024 |b IOP Publ. |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1730121984_11638 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a Med. Biol. 69, 215019 |
| 520 | _ | _ | |a To present a long short-term memory (LSTM) network-based dose calculation method for magnetic resonance (MR)-guided proton therapy.35 planning computed tomography (CT) images of prostate cancer patients were collected for Monte Carlo (MC) dose calculation under a perpendicular 1.5 T magnetic field. Proton pencil beams (PB) at three energies (150, 175, and 200 MeV) were simulated (7560 PBs at each energy). A 3D relative stopping power (RSP) cuboid covering the extent of the PB dose was extracted and given as input to the LSTM model, yielding a 3D predicted PB dose. Three single-energy (SE) LSTM models were trained separately on the corresponding 150/175/200 MeV datasets and a multi-energy (ME) LSTM model with an energy embedding layer was trained on either the combined dataset with three energies or a continuous energy (CE) dataset with 1 MeV steps ranging from 125 to 200 MeV. For each model, training and validation involved 25 patients and 10 patients were for testing. Two single field uniform dose prostate treatment plans were optimized and recalculated with MC and the CE model.Test results of all PBs from the three SE models showed a mean gamma passing rate (2%/2mm, 10% dose cutoff) above 99.9% with an average center-of-mass (COM) discrepancy below 0.4 mm between predicted and simulated trajectories. The ME model showed a mean gamma passing rate exceeding 99.8% and a COM discrepancy of less than 0.5 mm at the three energies. Treatment plan recalculation by the CE model yielded gamma passing rates of 99.6% and 97.9%. The inference time of the models was 9-10 ms per PB.LSTM models for proton dose calculation in a magnetic field were developed and showed promising accuracy and efficiency for prostate cancer patients. |
| 536 | _ | _ | |a 315 - Bildgebung und Radioonkologie (POF4-315) |0 G:(DE-HGF)POF4-315 |c POF4-315 |f POF IV |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de |
| 650 | _ | 7 | |a LSTM |2 Other |
| 650 | _ | 7 | |a MR-guided proton therapy |2 Other |
| 650 | _ | 7 | |a deep learning |2 Other |
| 650 | _ | 7 | |a dose calculation |2 Other |
| 700 | 1 | _ | |a Xiao, Fan |0 0000-0002-7502-0730 |b 1 |
| 700 | 1 | _ | |a Wahl, Niklas |0 P:(DE-He78)dfd5aaf608015baaaed0a15b473f1336 |b 2 |u dkfz |
| 700 | 1 | _ | |a Voss, Luke |0 P:(DE-HGF)0 |b 3 |
| 700 | 1 | _ | |a Neishabouri, Ahmad |0 P:(DE-He78)05d1465abf6918875d2df2781d7aec35 |b 4 |u dkfz |
| 700 | 1 | _ | |a Delopoulos, Nikolaos |b 5 |
| 700 | 1 | _ | |a Marschner, Sebastian |b 6 |
| 700 | 1 | _ | |a Corradini, Stefanie |b 7 |
| 700 | 1 | _ | |a Belka, Claus |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Dedes, Georgios |b 9 |
| 700 | 1 | _ | |a Kurz, Christopher |b 10 |
| 700 | 1 | _ | |a Landry, Guillaume |0 0000-0003-1707-4068 |b 11 |
| 773 | _ | _ | |a 10.1088/1361-6560/ad7f1e |0 PERI:(DE-600)1473501-5 |p 215019 |t Physics in medicine and biology |v 69 |y 2024 |x 0031-9155 |
| 856 | 4 | _ | |y OpenAccess |u https://inrepo02.dkfz.de/record/293598/files/Radonic_2024_Phys._Med._Biol._69_215019.pdf |
| 856 | 4 | _ | |y OpenAccess |x pdfa |u https://inrepo02.dkfz.de/record/293598/files/Radonic_2024_Phys._Med._Biol._69_215019.pdf?subformat=pdfa |
| 909 | C | O | |o oai:inrepo02.dkfz.de:293598 |p openaire |p open_access |p VDB |p driver |p dnbdelivery |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 2 |6 P:(DE-He78)dfd5aaf608015baaaed0a15b473f1336 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 3 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 4 |6 P:(DE-He78)05d1465abf6918875d2df2781d7aec35 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 8 |6 P:(DE-HGF)0 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF4-310 |0 G:(DE-HGF)POF4-315 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-300 |4 G:(DE-HGF)POF |v Bildgebung und Radioonkologie |x 0 |
| 914 | 1 | _ | |y 2024 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2023-08-26 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b PHYS MED BIOL : 2022 |d 2023-08-26 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2023-08-26 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2023-08-26 |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2023-08-26 |
| 915 | _ | _ | |a National-Konsortium |0 StatID:(DE-HGF)0430 |2 StatID |d 2023-08-26 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2023-08-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2023-08-26 |
| 920 | 1 | _ | |0 I:(DE-He78)E040-20160331 |k E040 |l E040 Med. Physik in der Strahlentherapie |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)E210-20160331 |k E210 |l E210 KKE Translationale Radioonkologie |x 1 |
| 920 | 1 | _ | |0 I:(DE-He78)MU01-20160331 |k MU01 |l DKTK Koordinierungsstelle München |x 2 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | _ | _ | |a I:(DE-He78)E040-20160331 |
| 980 | _ | _ | |a I:(DE-He78)E210-20160331 |
| 980 | _ | _ | |a I:(DE-He78)MU01-20160331 |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|