nach oben

Erschienen in:

20.06.2016 | Original Article

Open source deformable image registration system for treatment planning and recurrence CT scans

Validation in the head and neck region

verfasst von: Ruta Zukauskaite, Carsten Brink, Christian Rønn Hansen, Anders Bertelsen, Jørgen Johansen, Cai Grau, Jesper Grau Eriksen

Erschienen in: Strahlentherapie und Onkologie | Ausgabe 8/2016

Einloggen, um Zugang zu erhalten

Abstract

Background

Clinical application of deformable registration (DIR) of medical images remains limited due to sparse validation of DIR methods in specific situations, e. g. in case of cancer recurrences. In this study the accuracy of DIR for registration of planning CT (pCT) and recurrence CT (rCT) images of head and neck squamous cell carcinoma (HNSCC) patients was evaluated.

Patients and materials

Twenty patients treated with definitive IMRT for HNSCC in 2010–2012 were included. For each patient, a pCT and an rCT scan were used. Median interval between the scans was 8.5 months. One observer manually contoured eight anatomical regions-of-interest (ROI) twice on pCT and once on rCT.

Methods

pCT and rCT images were deformably registered using the open source software elastix. Mean surface distance (MSD) and Dice similarity coefficient (DSC) between contours were used for validation of DIR. A measure for delineation uncertainty was estimated by assessing MSD from the re-delineations of the same ROI on pCT. DIR and manual contouring uncertainties were correlated with tissue volume and rigidity.

Results

MSD varied 1–3 mm for different ROIs for DIR and 1–1.5 mm for re-delineated ROIs performed on pCT. DSC for DIR varied between 0.58 and 0.79 for soft tissues and was 0.79 or higher for bony structures, and correlated with the volumes of ROIs (r = 0.5, p < 0.001) and tissue rigidity (r = 0.54, p < 0.001).

Conclusion

DIR using elastix in HNSCC on planning and recurrence CT scans is feasible; an uncertainty of the method is close to the voxel size length of the planning CT images.

Kessler M (2006) Image registration and data fusion in radiation therapy. Br J Radiol 79(Spec No 1):S99–108CrossRefPubMed

Barker JL Jr, Garden AS, Ang KK, O’Daniel JC, Wang H, Court LE, Morrison WH, Rosenthal DI, Chao KSC, Tucker SL, Mohan R, Dong L (2004) Quantification of volumetric and geometric changes occurring during fractionated radiotherapy for head-and-neck cancer using an integrated CT/linear accelerator system. Int J Radiat Oncol Biol Phys 59(4):960–970CrossRefPubMed

Hamming-Vrieze O, Kranen SR van, Beek S van, Heemsbergen W, Herk M van, Brekel MWM van den, Sonke J‑J, Rasch CRN (2012) Evaluation of tumor shape variability in head-and-neck cancer patients over the course of radiation therapy using implanted gold markers. Int J Radiat Oncol 84(2):e201–e207CrossRef

Mencarelli A, Kranen SR van, Hamming-Vrieze O, Beek S van, Rasch NCR, Herk M van, Sonke J‑J (2014) Deformable image registration for adaptive radiation therapy of head and neck cancer: accuracy and precision in the presence of tumor changes. Int J Radiat Oncol Biol Phys 90(3):680–687CrossRefPubMed

Shakam A, Scrimger R, Liu D, Mohamed M, Parliament M, Field GC, El-Gayed A, Cadman P, Jha N, Warkentin H, Skarsgard D, Zhu Q, Ghosh S (2011) Dose-volume analysis of locoregional recurrences in head and neck IMRT, as determined by deformable registration: A prospective multi-institutional trial. Radiother Oncol 99(2):101–107CrossRefPubMed

http://elastix.isi.uu.nl/. Accessed May 2015

Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW (2010) elastix: A toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196–205CrossRefPubMed

Bertelsen A, Schytte T, Bentzen SM, Hansen O, Nielsen M, Brink C (2011) Radiation dose response of normal lung assessed by Cone Beam CT – A potential tool for biologically adaptive radiation therapy. Radiother Oncol 100(3):351–355CrossRefPubMed

Leibfarth S, Mönnich D, Welz S, Siegel C, Schwenzer N, Schmidt H, Zips D, Thorwarth D (2013) A strategy for multimodal deformable image registration to integrate PET/MR into radiotherapy treatment planning. Acta Oncol 52(7):1353–1359CrossRefPubMed

10.

Fortunati V, Verhaart RF, Angeloni F, Lugt A van der, Niessen WJ, Veenland JF, Paulides MM, Walsum T van (2014) Feasibility of multimodal deformable registration for head and neck tumor treatment planning. Int J Radiat Oncol Biol Phys 90(1):85–93CrossRefPubMed

11.

Brink C, Bernchou U, Bertelsen A, Hansen O, Schytte T, Bentzen SM (2014) Locoregional control of non-small cell lung cancer in relation to automated early assessment of tumor regression on cone beam computed tomography. Int J Radiat Oncol Biol Phys 89(4):916–923CrossRefPubMed

12.

Dice LR (1945) Measures of the amount of ecologic assocoation between species. Ecology 26:297–302CrossRef

13.

Due AK, Vogelius IR, Aznar MC, Bentzen SM, Berthelsen AK, Korreman SS, Kristensen CA, Specht L (2012) Methods for estimating the site of origin of locoregional recurrence in head and neck squamous cell carcinoma. Strahlenther Onkol 188(8):671–676CrossRefPubMed

14.

Brock KK, Velec M, Lee JHM (2014) Validation of Image registration. Image processing in radiation therapy. CRC Press, Boca Raton London New York, pp 41–61

15.

Wang H, Dong L, O’Daniel J, Mohan R, Garden AS, Ang KK, Kuban DA, Bonnen M, Chang JY, Cheung R (2005) Validation of an accelerated “demons” algorithm for deformable image registration in radiation therapy. Phys Med Biol 50(12):2887CrossRefPubMed

16.

Hou J, Guerrero M, Chen W, D’Souza WD (2011) Deformable planning CT to cone-beam CT image registration in head-and-neck cancer. Med Phys 38(4):2088–2094CrossRefPubMed

17.

Mencarelli A, Beek S van, Kranen S van, Rasch C, Herk M van, Sonke J‑J (2012) Validation of deformable registration in head and neck cancer using analysis of variance. Med Phys 39(11):6879–6884CrossRefPubMedPubMedCentral

18.

Castadot P, Lee JA, Parraga A, Geets X, Macq B, Grégoire V (2008) Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. Radiother Oncol 89(1):1–12CrossRefPubMed

19.

Lawson JD, Schreibmann E, Jani AB, Fox T (2007) Quantitative evaluation of a Cone Beam Computed Tomography (CBCT)-CT deformable image registration method for adaptive radiation therapy. J Appl Clin Med Phys 8(4):2432CrossRefPubMed

20.

Varadhan R, Karangelis G, Krishnan K, Hui S (2013) A framework for deformable image registration validation in radiotherapy clinical applications. J Appl Clin Med Phys / American College of Medical Physics 14(1):4066PubMedPubMedCentral

21.

Hoffmann C, Krause S, Stoiber EM, Mohr A, Rieken S, Schramm O, Debus J, Sterzing F, Bendl R, Giske K (2014) Accuracy quantification of a deformable image registration tool applied in a clinical setting. J Appl Clin Med Phys 15(1):4564PubMed

Titel: Open source deformable image registration system for treatment planning and recurrence CT scans
Validation in the head and neck region
verfasst von: Ruta Zukauskaite
Carsten Brink
Christian Rønn Hansen
Anders Bertelsen
Jørgen Johansen
Cai Grau
Jesper Grau Eriksen
Publikationsdatum: 20.06.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Strahlentherapie und Onkologie / Ausgabe 8/2016
Print ISSN: 0179-7158
Elektronische ISSN: 1439-099X
DOI: https://doi.org/10.1007/s00066-016-0998-4

Klimawandel und Gesundheit: Was Sie in der Hausarztpraxis wissen müssen und tun können

Springer Medizin

Open source deformable image registration system for treatment planning and recurrence CT scans

Validation in the head and neck region