Improved accuracy of markerless motion tracking on bone suppression images : preliminary study for image-guided radiation therapy (IGRT)

The bone suppression technique based on advanced image processing can suppress the conspicuity of bones on chest radiographs, creating soft tissue images obtained by the dual-energy subtraction technique. This study was performed to evaluate the usefulness of bone suppression image processing in image-guided radiation therapy. We demonstrated the improved accuracy of markerless motion tracking on bone suppression images. Chest fluoroscopic images of nine patients with lung nodules during respiration were obtained using a flat-panel detector system (120 kV, 0.1 mAs/pulse, 5 fps). Commercial bone suppression image processing software was applied to the fluoroscopic images to create corresponding bone suppression images. Regions of interest were manually located on lung nodules and automatic target tracking was conducted based on the template matching technique. To evaluate the accuracy of target tracking, the maximum tracking error in the resulting images was compared with that of conventional fluoroscopic images. The tracking errors were decreased by half in eight of nine cases. The average maximum tracking errors in bone suppression and conventional fluoroscopic images were 1.3 ± 1.0 and 3.3 ± 3.3 mm, respectively. The bone suppression technique was especially effective in the lower lung area where pulmonary vessels, bronchi, and ribs showed complex movements. The bone suppression technique improved tracking accuracy without special equipment and implantation of fiducial markers, and with only additional small dose to the patient. Bone suppression fluoroscopy is a potential measure for respiratory displacement of the target.

Medienart:

E-Artikel

Erscheinungsjahr:

2015

Erschienen:

2015

Enthalten in:

Zur Gesamtaufnahme - volume:60

Enthalten in:

Physics in medicine and biology - 60(2015), 10 vom: 21. Mai, Seite N209-18

Sprache:

Englisch

Beteiligte Personen:

Tanaka, Rie [VerfasserIn]
Sanada, Shigeru [VerfasserIn]
Sakuta, Keita [VerfasserIn]
Kawashima, Hiroki [VerfasserIn]

Themen:

Journal Article
Research Support, Non-U.S. Gov't

Anmerkungen:

Date Completed 18.01.2016

Date Revised 26.07.2019

published: Print

Citation Status MEDLINE

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM250679027