Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications

Giovanni Fattori, Marco Riboldi, Maxime Desplanques, Barbara Tagaste, Andrea Pella, Roberto Orecchia, Guido Baroni

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

We propose a novel method for radio-opaque external marker localization in CT scans for infrared (IR) patient set-up in radiotherapy. Efforts were focused on the quantification of uncertainties in marker localization in the CT dataset as a function of algorithm performance. We implemented a 3-D approach to fiducial localization based on surface extraction and marker recognition according to geometrical prior knowledge. The algorithm parameters were optimized on a clinical CT dataset coming from 35 cranial and extra-cranial patients; the localization accuracy was benchmarked at variable image resolution versus laser tracker measurements. The applicability of conventional IR optical tracking systems for localizing external surrogates in daily patient set-up procedures was also investigated in 121 proton therapy treatment sessions. Our study shows that the implemented algorithm features surrogates localization with uncertainties lower than 0.3mm and with a true positive rate of 90.1, being this latter mainly influenced by fiducial homogeneity in the CT images. The reported clinical validation in proton therapy confirmed the submillimetric accuracy and the expected algorithm sensitivity. Geometrical prior knowledge allows judging the reliability of the extracted fiducial coordinates, ensuring the highest accuracy in patient set-up.

Original languageEnglish
Article number6198875
Pages (from-to)2191-2199
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Volume59
Issue number8
DOIs
Publication statusPublished - 2012

Fingerprint

Radiotherapy
Processing
Protons
Infrared radiation
Computerized tomography
Image resolution
Lasers
Uncertainty

Keywords

  • Automated detection
  • CT scanning
  • fiducial localization
  • particle therapy
  • patient positioning
  • radiotherapy

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications. / Fattori, Giovanni; Riboldi, Marco; Desplanques, Maxime; Tagaste, Barbara; Pella, Andrea; Orecchia, Roberto; Baroni, Guido.

In: IEEE Transactions on Biomedical Engineering, Vol. 59, No. 8, 6198875, 2012, p. 2191-2199.

Research output: Contribution to journalArticle

Fattori, Giovanni ; Riboldi, Marco ; Desplanques, Maxime ; Tagaste, Barbara ; Pella, Andrea ; Orecchia, Roberto ; Baroni, Guido. / Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications. In: IEEE Transactions on Biomedical Engineering. 2012 ; Vol. 59, No. 8. pp. 2191-2199.
@article{7f9620ce64c049ffacba8bb924e7a16f,
title = "Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications",
abstract = "We propose a novel method for radio-opaque external marker localization in CT scans for infrared (IR) patient set-up in radiotherapy. Efforts were focused on the quantification of uncertainties in marker localization in the CT dataset as a function of algorithm performance. We implemented a 3-D approach to fiducial localization based on surface extraction and marker recognition according to geometrical prior knowledge. The algorithm parameters were optimized on a clinical CT dataset coming from 35 cranial and extra-cranial patients; the localization accuracy was benchmarked at variable image resolution versus laser tracker measurements. The applicability of conventional IR optical tracking systems for localizing external surrogates in daily patient set-up procedures was also investigated in 121 proton therapy treatment sessions. Our study shows that the implemented algorithm features surrogates localization with uncertainties lower than 0.3mm and with a true positive rate of 90.1, being this latter mainly influenced by fiducial homogeneity in the CT images. The reported clinical validation in proton therapy confirmed the submillimetric accuracy and the expected algorithm sensitivity. Geometrical prior knowledge allows judging the reliability of the extracted fiducial coordinates, ensuring the highest accuracy in patient set-up.",
keywords = "Automated detection, CT scanning, fiducial localization, particle therapy, patient positioning, radiotherapy",
author = "Giovanni Fattori and Marco Riboldi and Maxime Desplanques and Barbara Tagaste and Andrea Pella and Roberto Orecchia and Guido Baroni",
year = "2012",
doi = "10.1109/TBME.2012.2198822",
language = "English",
volume = "59",
pages = "2191--2199",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",
number = "8",

}

TY - JOUR

T1 - Automated fiducial localization in CT images based on surface processing and geometrical prior knowledge for radiotherapy applications

AU - Fattori, Giovanni

AU - Riboldi, Marco

AU - Desplanques, Maxime

AU - Tagaste, Barbara

AU - Pella, Andrea

AU - Orecchia, Roberto

AU - Baroni, Guido

PY - 2012

Y1 - 2012

N2 - We propose a novel method for radio-opaque external marker localization in CT scans for infrared (IR) patient set-up in radiotherapy. Efforts were focused on the quantification of uncertainties in marker localization in the CT dataset as a function of algorithm performance. We implemented a 3-D approach to fiducial localization based on surface extraction and marker recognition according to geometrical prior knowledge. The algorithm parameters were optimized on a clinical CT dataset coming from 35 cranial and extra-cranial patients; the localization accuracy was benchmarked at variable image resolution versus laser tracker measurements. The applicability of conventional IR optical tracking systems for localizing external surrogates in daily patient set-up procedures was also investigated in 121 proton therapy treatment sessions. Our study shows that the implemented algorithm features surrogates localization with uncertainties lower than 0.3mm and with a true positive rate of 90.1, being this latter mainly influenced by fiducial homogeneity in the CT images. The reported clinical validation in proton therapy confirmed the submillimetric accuracy and the expected algorithm sensitivity. Geometrical prior knowledge allows judging the reliability of the extracted fiducial coordinates, ensuring the highest accuracy in patient set-up.

AB - We propose a novel method for radio-opaque external marker localization in CT scans for infrared (IR) patient set-up in radiotherapy. Efforts were focused on the quantification of uncertainties in marker localization in the CT dataset as a function of algorithm performance. We implemented a 3-D approach to fiducial localization based on surface extraction and marker recognition according to geometrical prior knowledge. The algorithm parameters were optimized on a clinical CT dataset coming from 35 cranial and extra-cranial patients; the localization accuracy was benchmarked at variable image resolution versus laser tracker measurements. The applicability of conventional IR optical tracking systems for localizing external surrogates in daily patient set-up procedures was also investigated in 121 proton therapy treatment sessions. Our study shows that the implemented algorithm features surrogates localization with uncertainties lower than 0.3mm and with a true positive rate of 90.1, being this latter mainly influenced by fiducial homogeneity in the CT images. The reported clinical validation in proton therapy confirmed the submillimetric accuracy and the expected algorithm sensitivity. Geometrical prior knowledge allows judging the reliability of the extracted fiducial coordinates, ensuring the highest accuracy in patient set-up.

KW - Automated detection

KW - CT scanning

KW - fiducial localization

KW - particle therapy

KW - patient positioning

KW - radiotherapy

UR - http://www.scopus.com/inward/record.url?scp=84864246708&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84864246708&partnerID=8YFLogxK

U2 - 10.1109/TBME.2012.2198822

DO - 10.1109/TBME.2012.2198822

M3 - Article

C2 - 22588574

AN - SCOPUS:84864246708

VL - 59

SP - 2191

EP - 2199

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

IS - 8

M1 - 6198875

ER -