基于PET/CT形变配准技术对放疗前后靶区变化的评估

doi:10.3969/j.issn.0258-8021.2017.04.002

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Abstract The metabolic information provided by PET images can be used to define the high recurrence region, which is of great significance for accurate treatment planning. This study proposed a multi-resolution deformable registration method to extract the deformation field between CT images in two inter-fractions, and then applied the deformation field to PET images. By defining the SUV (standard uptake value) thresholds we determined the outlines in two PET images and then calculated their overlapping ratio to obtain high uptake regions, which are good for retrospectively performing accurate radiation therapy in the next fraction. Twenty-two patients with lung cancer were included in this study. The preliminary results showed that residual hyper-metabolic region after radiotherapy well overlapped with gross tumor volume (GTV) before radiotherapy: the overlapping ratios were 95.2±0.6%, 96.6±3.4% and 100% with the SUV_max threshold defined to 70%, 80% and 90%, respectively; the overlapping ratio was 86.0±6.6% and 97.0±3.0% with the thresholds confined to SUV2.5 and SUV5.0, respectively. The high overlap rate of the high uptake area of fluorodeoxyglucose showed that the location of the lesion was relatively stable before and after radiotherapy, and the residual tumor after radiotherapy was located in the area of FDG uptake before radiotherapy. Preliminary results showed that this study had a potential ability of determining target reaction to radiotherapy and was good for retrospectively increasing radiation dose to residual tumor target with good protection to organs and tissues, showing potentials of realizing accurate radiotherapy.

Key words： ¹⁸F FDGPET CT images lung cancer overlapping area deformable registration

Received: 09 November 2016

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R318

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	Articles by authors
	Zhang Lulu
	Yang Juan
	Huang Pu
	Zhao Rui
	Ma Changsheng
	Yin Yong
	Li Dengwang

Cite this article:

Zhang Lulu,Yang Juan,Huang Pu, et al. Evaluation of Tumor Target Changes Before and After Radiation Therapy Based on PET/CT Deformation Image Registration[J]. Chinese Journal of Biomedical Engineering, 2017, 36(4): 394-400.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021.2017.04.002 OR http://cjbme.csbme.org/EN/Y2017/V36/I4/394

[1] Habib Zaidi, Issam El Naqa.PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques[J].Eur J NuclMed MolImaging,2010,37(11): 2165-2187.
[2] Ford EC,HermanJ,YorkeE,et al.¹⁸F-FDG PET/CT for image-guided and intensity-modulated radiotherapy[J].J Nucl Med,2009,50(10): 1655-1665.
[3] MawlawiO,Townsend DW. Multimodality imaging: an update on PET/CT technology[J].Eur J Nucl Med Mol Imaging,2009,36: 15-29.
[4] Li Dengwang, Liu Li,Xing Lei, et al. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours[J].Phys Med Biol,2017,62(1):272-288.
[5] Li Dengwang,ZangPengxiao, Xing Lei,et al.Automatic multi-organ segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models[J]. Med Phys,2016,43(10):5426-5436.
[6] 李登旺, 李洪升, 王慧, 等.基于边缘保护尺度空间的形变配准方法及在自适应放疗中的应用[J].自动化学报, 2012,38 (5): 751-758.
[7] KezbanBerberogˇlu.Use of Positron Emission Tomography/Computed Tomography in radiation treatment planning for lung cancer[J].Mol Imaging Radio Ther, 2016,25(2):50-62.
[8] KernstineKH, Mclaughlin KA, Menda Y,et al. Can FDG-PET reduce theneed for mediastinoscopy in potentially resectable non-small celllungcancer?[J]. Ann ThoracSurg,2002,73(2):394-401.
[9] Paulino AC, Johnstone PA. FDG-PET in radiotherapy treatmentplanning: Pandora's box?[J].Int J Radiat Oncol Biol Phys,2004, 59:4-5.
[10] Ashamalla H, Guirgius A, Bieniek E, et al.The inpact of PET/CT in edge delineation of gross tumor volume for head and neck cancer[J].Int J Radiat Onco Biol Phys, 2007,68(2):388-395.
[11] Mac Manus MP, Everitt S, Bayne M, et al. The use of fused PET/CT images for patient selection and radical radiotherapy target volume definition in patients with non-small cell lung cancer: Results of a prospective study with mature survival data[J].Radiother Oncol,2013,106(3):292-298.
[12] van Elmpt W, Ollers M, Dingemans AM,et al. Response assessment using 18F-FDG PET early in the course of radiotherapy correlates with survival in advanced-stage Non-Small Cell Lung Cancer[J]. J Nucl Med,2012,53(10):1514-1520.
[13] Aerts HJ, Bussink J, Oyen WJ, et al. Identification of residual metabolic-active areas within NSCLC tumours using a pre-radiotherapy FDG-PET-CT scan: a prospective validation[J]. Lung Cancer,2012,75(1):73-76.
[14] 孟雪.功能分子影像在非小细胞肺癌中的应用研究[D].济南:山东大学,2012.
[15] Aerts HJ, Bosmans G, Van Baardwijk AA, et al. Stability of ¹⁸F-deoxyglucose uptake locations within tumor during radiotherapy for NSCLC: a prospective study[J]. Int J Radio Onco Biol Phys,2008,71(5):1402-1407.
[16] 石雪, 陈进琥, 李登旺, 等.基于感兴趣窄带区域的同步分割与配准方法及在IGRT中的应用[J].自动化学报.2015, 41(9):1589-1600.
[17] Rogelj P, Kovacis S.Symmetric image registration[J].Med Image Ana, 2006, 10(3):484-493.
[18] 周付根, 段卓镭.Demons算法在四维CT图像配准中的应用[J].CT理论与应用研究,2009,18(1):69-75.
[19] Peter J. Burt, Edward H. Adelson.The laplacian pyramid as a compact image code[J].IEEE Trans Commun,1983,31(4): 532-540.
[20] 李登旺, 王洪君, 尹勇,等. 基于边缘保护多尺度空间的医学图像配准方法模式识别与人工智能[J].2011, 24(1):117-122.
[21] Mac Manus MP, Hicks RJ, Matthews JP,et al. Metabolic (FDG-PET) response after radical radiotherapy/chemoradiotherapy for non-small cell lung cancer correlates with patterns of failure[J]. Lung Cancer,2005,49(1):95-108.
[22] Bentzen SM. Dose painting and theragnostic imaging: towards the prescription, planning and delivery of biologically targeted dose distributions in external beam radiation oncology[J]. Cancer Treat Res,2008,139:41-62.
[23] Sovk A, Malinen E, Skogmo HK,et al. Radiotherapy adapted to spatial and temporal variability in tumor hypoxia[J].Int J Rad Oncol Biol Phys,2007,68(5):1496-1504.
[24] Borst GR, Belderbos JS, Boellaard R, et al.Standardised FDG uptake: a prognostic factor for inoperable non-small cell lung cancer[J].Eur J Cancer,2005,41(11):1533-1541.
[25] Doney RJ, Akhurst T, Gonen M, et al.Preoperative¹⁸ F- FDGemission tomography maximal standardized uptake value predicts survival after lung cancer resection[J]. J Clin Oncol,2004,22(16):3255-3260.
[26] Escmann SM, Friedel G, Paulsen F, et al. Is standardised¹⁸F-FDG uptake value an outcome predictor in patients with stage III non-small cell lung cancer?[J]. Eur J Nucl Med Mol Imaging,2006,33(3):263-269.
[27] Van Baardwijk A,Dooms C, Van Suylen RJ,et al.The maximum uptake of ¹⁸F-deoxyglucose on positron emission tomography scan correlates with survival, hypoxia inducible factor-1alpha and GLUT-1 in non-small cell lung cancer[J].Eur J Cancer, 2007,43(9):1392-1398.
[28] Vansteenkiste JF, Stroobants SG, Dupont PJ, et al. Prognostic importance of the standardized uptake value on ¹⁸F-fluoro-2-deoxy-glucose-positron emission tomography scan in non-small-cell lung cancer: an analysis of 125 cases Leuven Lung Cancer Group[J].J Clin Oncol, 1999,17(10):3201-3206.
[29] Li Dengwang,Li Hongsheng,Yong Yin,et al. Multiscale registration of medical images based on edge preserving scale space with application in image guided radiation therapy[J]. Phys Med Biol, 2012,57(16):5187-5204.
[30] Li Dengwang, Wang Hongjun, Yin Yong, et al. Deformable registration using edge-preserving scale space for adaptive image-guided radiation therapy[J].J Appl Clin Med Phys, 2011,12(4):105-123.