Abstract
Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis.
| Original language | English |
|---|---|
| Pages (from-to) | 323-331 |
| Number of pages | 9 |
| Journal | Technology in Cancer Research and Treatment |
| Volume | 12 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2013 |
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Keywords
- B-Splines
- Deformable image registration
- Demons
- Head and neck cancer
- MIMvista
- Regularization
ASJC Scopus subject areas
- Cancer Research
- Oncology
- Medicine(all)
Cite this
The role of regularization in deformable image registration for head and neck adaptive radiotherapy. / Ciardo, D.; Peroni, M.; Riboldi, M.; Alterio, D.; Baroni, G.; Orecchia, R.
In: Technology in Cancer Research and Treatment, Vol. 12, No. 4, 2013, p. 323-331.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - The role of regularization in deformable image registration for head and neck adaptive radiotherapy
AU - Ciardo, D.
AU - Peroni, M.
AU - Riboldi, M.
AU - Alterio, D.
AU - Baroni, G.
AU - Orecchia, R.
PY - 2013
Y1 - 2013
N2 - Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis.
AB - Deformable image registration provides a robust mathematical framework to quantify morphological changes that occur along the course of external beam radiotherapy treatments. As clinical reliability of deformable image registration is not always guaranteed, algorithm regularization is commonly introduced to prevent sharp discontinuities in the quantified deformation and achieve anatomically consistent results. In this work we analyzed the influence of regularization on two different registration methods, i.e. B-Splines and Log Domain Diffeomorphic Demons, implemented in an open-source platform. We retrospectively analyzed the simulation computed tomography (CTsim) and the corresponding re-planning computed tomography (CTrepl) scans in 30 head and neck cancer patients. First, we investigated the influence of regularization levels on hounsfield units (HU) information in 10 test patients for each considered method. Then, we compared the registration results of the open-source implementation at selected best performing regularization levels with a clinical commercial software on the remaining 20 patients in terms of mean volume overlap, surface and center of mass distances between manual outlines and propagated structures. The regularized B-Splines method was not statistically different from the commercial software. The tuning of the regularization parameters allowed open-source algorithms to achieve better results in deformable image registration for head and neck patients, with the additional benefit of a framework where regularization can be tuned on a patient specific basis.
KW - B-Splines
KW - Deformable image registration
KW - Demons
KW - Head and neck cancer
KW - MIMvista
KW - Regularization
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UR - http://www.scopus.com/inward/citedby.url?scp=84880059991&partnerID=8YFLogxK
U2 - 10.7785/tcrt.2012.500327
DO - 10.7785/tcrt.2012.500327
M3 - Article
C2 - 23448576
AN - SCOPUS:84880059991
VL - 12
SP - 323
EP - 331
JO - Technology in Cancer Research and Treatment
JF - Technology in Cancer Research and Treatment
SN - 1533-0346
IS - 4
ER -