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Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking

We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker‐based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before...

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Autores principales: Akimoto, Mami, Nakamura, Mitsuhiro, Mukumoto, Nobutaka, Yamada, Masahiro, Tanabe, Hiroaki, Ueki, Nami, Kaneko, Shuji, Matsuo, Yukinori, Mizowaki, Takashi, Kokubo, Masaki, Hiraoka, Masahiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5690072/
https://www.ncbi.nlm.nih.gov/pubmed/26103167
http://dx.doi.org/10.1120/jacmp.v16i2.4896
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author Akimoto, Mami
Nakamura, Mitsuhiro
Mukumoto, Nobutaka
Yamada, Masahiro
Tanabe, Hiroaki
Ueki, Nami
Kaneko, Shuji
Matsuo, Yukinori
Mizowaki, Takashi
Kokubo, Masaki
Hiraoka, Masahiro
author_facet Akimoto, Mami
Nakamura, Mitsuhiro
Mukumoto, Nobutaka
Yamada, Masahiro
Tanabe, Hiroaki
Ueki, Nami
Kaneko, Shuji
Matsuo, Yukinori
Mizowaki, Takashi
Kokubo, Masaki
Hiraoka, Masahiro
author_sort Akimoto, Mami
collection PubMed
description We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker‐based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. To perform IR Tracking, a four‐dimensional (4D) model was constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the model was expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, [Formula: see text] , was adjusted by the baseline drift of IR markers ([Formula: see text]) along the x‐axis, as function [Formula: see text]. Next, [Formula: see text] , that defined as the difference between the target positions indicated by the implanted fiducial markers ([Formula: see text]) and the predicted target positions with [Formula: see text] ([Formula: see text]) was determined using orthogonal kV X‐ray images at the peaks of the [Formula: see text] of the end‐inhale and end‐exhale phases for 10 s just before irradiation. [Formula: see text] was corrected with [Formula: see text] to compensate for the residual error. The final corrected 4D model was expressed as [Formula: see text]. We retrospectively applied this function to 53 paired log files of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between [Formula: see text] and [Formula: see text] ([Formula: see text]) was compared between [Formula: see text] and [Formula: see text]. The median 95th percentile of [Formula: see text] (units: mm) was 1.0, 1.7, and 3.5 for [Formula: see text] , and 0.6, 1.1, and 2.1 for [Formula: see text] in the left–right, anterior–posterior, and superior–inferior directions, respectively. Over all treatment sessions, the 95th percentile of [Formula: see text] peaked at 3.2 mm using [Formula: see text] compared with 8.4 mm using [Formula: see text]. Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation. PACS number: 87.19.rs, 87.19.Wx, 87.56.‐v, 87.59.‐e, 88.10.gc
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spelling pubmed-56900722018-04-02 Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking Akimoto, Mami Nakamura, Mitsuhiro Mukumoto, Nobutaka Yamada, Masahiro Tanabe, Hiroaki Ueki, Nami Kaneko, Shuji Matsuo, Yukinori Mizowaki, Takashi Kokubo, Masaki Hiraoka, Masahiro J Appl Clin Med Phys Radiation Oncology Physics We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker‐based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. To perform IR Tracking, a four‐dimensional (4D) model was constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the model was expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, [Formula: see text] , was adjusted by the baseline drift of IR markers ([Formula: see text]) along the x‐axis, as function [Formula: see text]. Next, [Formula: see text] , that defined as the difference between the target positions indicated by the implanted fiducial markers ([Formula: see text]) and the predicted target positions with [Formula: see text] ([Formula: see text]) was determined using orthogonal kV X‐ray images at the peaks of the [Formula: see text] of the end‐inhale and end‐exhale phases for 10 s just before irradiation. [Formula: see text] was corrected with [Formula: see text] to compensate for the residual error. The final corrected 4D model was expressed as [Formula: see text]. We retrospectively applied this function to 53 paired log files of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between [Formula: see text] and [Formula: see text] ([Formula: see text]) was compared between [Formula: see text] and [Formula: see text]. The median 95th percentile of [Formula: see text] (units: mm) was 1.0, 1.7, and 3.5 for [Formula: see text] , and 0.6, 1.1, and 2.1 for [Formula: see text] in the left–right, anterior–posterior, and superior–inferior directions, respectively. Over all treatment sessions, the 95th percentile of [Formula: see text] peaked at 3.2 mm using [Formula: see text] compared with 8.4 mm using [Formula: see text]. Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation. PACS number: 87.19.rs, 87.19.Wx, 87.56.‐v, 87.59.‐e, 88.10.gc John Wiley and Sons Inc. 2015-03-08 /pmc/articles/PMC5690072/ /pubmed/26103167 http://dx.doi.org/10.1120/jacmp.v16i2.4896 Text en © 2015 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Akimoto, Mami
Nakamura, Mitsuhiro
Mukumoto, Nobutaka
Yamada, Masahiro
Tanabe, Hiroaki
Ueki, Nami
Kaneko, Shuji
Matsuo, Yukinori
Mizowaki, Takashi
Kokubo, Masaki
Hiraoka, Masahiro
Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
title Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
title_full Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
title_fullStr Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
title_full_unstemmed Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
title_short Baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
title_sort baseline correction of a correlation model for improving the prediction accuracy of infrared marker‐based dynamic tumor tracking
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5690072/
https://www.ncbi.nlm.nih.gov/pubmed/26103167
http://dx.doi.org/10.1120/jacmp.v16i2.4896
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