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Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples.
Niepel, Katharina; Tattenberg, Sebastian; Marants, Raanan; Hu, Guyue; Bortfeld, Thomas; Verburg, Joost; Sudhyadhom, Atchar; Landry, Guillaume; Parodi, Katia.
Afiliação
  • Niepel K; Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany.
  • Tattenberg S; Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany.
  • Marants R; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America.
  • Hu G; Department of Radiation Oncology, Brigham and Women's Hospital, Boston, United States of America.
  • Bortfeld T; Department of Medical Physics, Ludwig-Maximilians-Universität München (LMU Munich), Garching, Germany.
  • Verburg J; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America.
  • Sudhyadhom A; Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America.
  • Landry G; Department of Radiation Oncology, Brigham and Women's Hospital, Boston, United States of America.
  • Parodi K; Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Phys Med Biol ; 69(16)2024 Aug 12.
Article em En | MEDLINE | ID: mdl-39074494
ABSTRACT
Objective.Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton rangein-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.Approach.A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.Results.The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous.Significance.Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute toin-vivodose verification measurements.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tomografia Computadorizada por Raios X / Imagens de Fantasmas Limite: Animals / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Tomografia Computadorizada por Raios X / Imagens de Fantasmas Limite: Animals / Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article