PMID- 18501456
OWN - NLM
STAT- MEDLINE
DCOM- 20090210
LR  - 20220408
IS  - 0167-8140 (Print)
IS  - 0167-8140 (Linking)
VI  - 89
IP  - 1
DP  - 2008 Oct
TI  - Comparison of 12 deformable registration strategies in adaptive radiation therapy 
      for the treatment of head and neck tumors.
PG  - 1-12
LID - 10.1016/j.radonc.2008.04.010 [doi]
AB  - BACKGROUND AND PURPOSE: Weight loss, tumor shrinkage, and tissue edema induce 
      substantial modification of patient's anatomy during head and neck (HN) 
      radiotherapy (RT) or chemo-radiotherapy. These modifications may impact on the 
      dose distribution to both target volumes (TVs) and organs at risk (OARs). 
      Adaptive radiotherapy (ART) where patients are re-imaged and re-planned several 
      times during the treatment is a possible strategy to improve treatment delivery. 
      It however requires the use of specific deformable registration (DR) algorithms 
      that requires proper validation on a clinical material. MATERIALS AND METHODS: 
      Twelve voxel-based DR strategies were compared with a dataset of 5 patients 
      imaged with computed tomography (CT) before and once during RT (on average after 
      a mean dose of 36.8Gy): level-set (LS), level-set implemented in multi-resolution 
      (LS(MR)), Demons' algorithm implemented in multi-resolution (D(MR)), D(MR) 
      followed by LS (D(MR)-LS), fast free-form deformable registration via calculus of 
      variations (F3CV) and F3CV followed by LS (F3CV-LS). The use of an 
      edge-preserving denoising filter called "local M-smoothers" applied to the 
      registered images and combined to all the aforesaid strategies was also tested 
      (fLS, fLS(MR), fD(MR), fD(MR)-LS, fF3CV, fF3CV-LS). All these strategies were 
      compared to a rigid registration based on mutual information (MI, fMI). 
      Chronological and anti-chronological registrations were also studied. The various 
      DR strategies were evaluated using a volume-based criterion (i.e. Dice similarity 
      index, DSI) and a voxel-intensity criterion (i.e. correlation coefficient, CC) on 
      a total of 18 different manually contoured volumes. RESULTS: For the DSI 
      analysis, the best three strategies were D(MR), fD(MR)-LS, and fD(MR), with the 
      median values of 0.86, 0.85 and 0.85, respectively; corresponding inter-quartile 
      range (IQR) reached 9.6%, 10% and 10.2%. For the CC analysis, the best three 
      strategies were fD(MR)-LS, D(MR)-LS and D(MR) with the median values of 0.97, 
      0.96 and 0.94, respectively; corresponding IQR reached 11%, 9% and 15%. 
      Concerning the time-sequence analysis, the anti-chronological registration (all 
      deformable strategies pooled) showed a better median DSI value (0.84 vs 0.83, 
      p<0.001) and IQR (11.2% vs 12.4%). For CC, the anti-chronological registration 
      (all deformable strategies pooled) had a slightly lower median value (0.91 vs 
      0.912, p<0.001) but a better IQR (16.4% vs 21%). CONCLUSIONS: The use of 
      fD(MR)-LS is a good registration strategy for HN-ART as it is the best compromise 
      in terms of median and IQR for both DSI and CC. Even though less robust in terms 
      of CC, D(MR) is a good alternative. None of the time-sequence appears superior.
FAU - Castadot, Pierre
AU  - Castadot P
AD  - Department of Radiation Oncology, Universite catholique de Louvain, St-Luc 
      University Hospital, Brussels, Belgium.
FAU - Lee, John Aldo
AU  - Lee JA
FAU - Parraga, Adriane
AU  - Parraga A
FAU - Geets, Xavier
AU  - Geets X
FAU - Macq, Benoit
AU  - Macq B
FAU - Gregoire, Vincent
AU  - Gregoire V
LA  - eng
PT  - Comparative Study
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20080522
PL  - Ireland
TA  - Radiother Oncol
JT  - Radiotherapy and oncology : journal of the European Society for Therapeutic 
      Radiology and Oncology
JID - 8407192
SB  - IM
MH  - Algorithms
MH  - Head and Neck Neoplasms/diagnostic imaging/*radiotherapy
MH  - Humans
MH  - Outcome and Process Assessment, Health Care
MH  - Radiographic Image Interpretation, Computer-Assisted
MH  - Radiotherapy/*methods
MH  - Radiotherapy Dosage
MH  - Radiotherapy Planning, Computer-Assisted/*methods
MH  - Statistics, Nonparametric
MH  - Tomography, X-Ray Computed
EDAT- 2008/05/27 09:00
MHDA- 2009/02/12 09:00
CRDT- 2008/05/27 09:00
PHST- 2008/02/19 00:00 [received]
PHST- 2008/04/09 00:00 [revised]
PHST- 2008/04/20 00:00 [accepted]
PHST- 2008/05/27 09:00 [pubmed]
PHST- 2009/02/12 09:00 [medline]
PHST- 2008/05/27 09:00 [entrez]
AID - S0167-8140(08)00230-2 [pii]
AID - 10.1016/j.radonc.2008.04.010 [doi]
PST - ppublish
SO  - Radiother Oncol. 2008 Oct;89(1):1-12. doi: 10.1016/j.radonc.2008.04.010. Epub 
      2008 May 22.