{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T15:42:04Z","timestamp":1712245324747},"reference-count":20,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T00:00:00Z","timestamp":1703030400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>The elemental concentration (especially oxygen and carbon) and mass density must be accurately assigned to perform Monte Carlo (MC) simulations for predicting proton\u2010induced nuclear reactions in the human body. We recently proposed an approach to quantify elemental concentrations and mass densities of human soft tissues from water content (WC) data obtained by quantitative magnetic resonance (MR) imaging (which we called \u201cMRWC\u201d).<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study presents the first implementation of MRWC\u2010derived elemental concentrations and mass densities as complementary inputs into MC simulations on a virtual head phantom, and demonstrates the simulation of positron emitter production yields in proton therapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>An MC code, PHITS, was used to simulate proton therapy with a monoenergetic 140 MeV beam for a digital head phantom provided by BrainWeb. Three different head images were synthesized as inputs: a conventional CT image, an ideal CT image as a reference, and a WC image coupled with the bone\u2010only CT image for a hybrid approach (MRWC\/CT). Thereafter, the performance of the MRWC\/CT method was evaluated by comparing its accuracy in predicting the production yields of positron emitters (<jats:sup>11<\/jats:sup>C and <jats:sup>15<\/jats:sup>O) with the gold\u2010standard CT\u2010only method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The MRWC\/CT method could predict <jats:sup>11<\/jats:sup>C and <jats:sup>15<\/jats:sup>O production yield maps that closely resembled the corresponding reference maps, while the CT\u2010only method failed. The structural similarity index measures between the reference and CT\u2010 or MRWC\/CT\u2010derived maps were improved from 0.67 (CT\u2010only) to 0.87 (MRWC\/CT) for <jats:sup>11<\/jats:sup>C and 0.76 (CT\u2010only) to 0.93 (MRWC\/CT) for <jats:sup>15<\/jats:sup>O. Furthermore, applying post\u2010processing normalizations to account for elemental density variations in the production yields of positron emitters facilitated the determination of distal fall\u2010off positions in depth activity profiles.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>At least in the head area, the MRWC\/CT method demonstrated potential for more precise predictions of proton\u2010induced positron emitter distributions via MC simulations than that of the CT\u2010only method.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16896","type":"journal-article","created":{"date-parts":[[2023,12,20]],"date-time":"2023-12-20T12:12:45Z","timestamp":1703074365000},"page":"2861-2870","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Technical note: Implementing MRI\/CT\u2010based elemental concentration data to Monte Carlo simulation for yielding positron emitters in proton therapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Masatoshi","family":"Saito","sequence":"first","affiliation":[{"name":"Department of Radiological Technology School of Health Sciences Faculty of Medicine Niigata University  Niigata Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryuga","family":"Matsumoto","sequence":"additional","affiliation":[{"name":"Department of Radiological Technology School of Health Sciences Faculty of Medicine Niigata University  Niigata Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2023,12,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/47\/1\/302"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/52\/12\/004"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/45\/2\/314"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/54\/14\/008"},{"key":"e_1_2_8_6_1","volume-title":"Quantitative MRI of the Brain: Principles of Physical Measurement","author":"Cercignani M","year":"2018"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16353"},{"key":"e_1_2_8_8_1","volume-title":"Nuclear and Particle Physics: An Introduction","author":"Martin BR","year":"2019"},{"issue":"4","key":"e_1_2_8_9_1","first-page":"425","article-title":"Brainweb: online interface to a 3D MRI simulated brain database","volume":"5","author":"Cocosco CA","year":"1997","journal-title":"NeuroImage"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/58\/15\/5029"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1259\/0007\u20101285\u201059\u2010708\u20101209"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1080\/00223131.2023.2275736"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.7150\/thno.5162"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nds.2023.01.004"},{"key":"e_1_2_8_16_1","doi-asserted-by":"crossref","unstructured":"Beebe\u2010WangJ PeggsS SmithL.Dependence of the production yields of positron emitters in proton therapy on the cross section data variations. 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