Enhancing Tissue Equivalence in

Bragg cure MC algorithm lithium-ion therapy polymeric biomaterials recoil

Journal

Journal of functional biomaterials
ISSN: 2079-4983
Titre abrégé: J Funct Biomater
Pays: Switzerland
ID NLM: 101570734

Informations de publication

Date de publication:
25 Nov 2023
Historique:
received: 06 09 2023
revised: 16 10 2023
accepted: 23 11 2023
medline: 22 12 2023
pubmed: 22 12 2023
entrez: 22 12 2023
Statut: epublish

Résumé

The unique physical properties of heavy ion beams, particularly their distinctive depth-dose distribution and sharp lateral dose reduction profiles, have led to their widespread adoption in tumor therapy worldwide. However, the physical properties of heavy ion beams must be investigated to deliver a sufficient dose to tumors without damaging organs at risk. These studies should be performed on phantoms made of biomaterials that closely mimic human tissue. Polymers can serve as soft tissue substitutes and are suitable materials for building radiological phantoms due to their physical, mechanical, biological, and chemical properties. Extensive research, development, and applications of polymeric biomaterials have been encouraged due to these properties. In this study, we investigated the ionization, recoils, phonon release, collision events, and lateral straggle properties of polymeric biomaterials that closely resemble soft tissue using lithium-ion beams and Monte Carlo Transport of Ions in Matter simulation. The results indicated that the Bragg peak position closest to soft tissue was achieved with a 7.3% difference in polymethylmethacrylate, with an average recoils value of 10.5%. Additionally, average values of 33% were observed in collision events and 22.6% in lateral straggle. A significant contribution of this study to the existing literature lies in the exploration of secondary interactions alongside the assessment of linear energy transfer induced by the

Identifiants

pubmed: 38132813
pii: jfb14120559
doi: 10.3390/jfb14120559
pii:
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Fatih Ekinci (F)

Institute of Nuclear Sciences, Ankara University, 06100 Ankara, Turkey.

Koray Acici (K)

Artificial Intelligence and Data Engineering, Ankara University, 06100 Ankara, Turkey.

Tunc Asuroglu (T)

Faculty of Medicine and Health Technology, Tampere University, 33014 Tampere, Finland.

Classifications MeSH