Purpose. To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials. Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results. MPSL algorithm segmented the GTV in 4DCT images in seconds per phase ( resolution) as compared to seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were . In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions. The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation. 1. Introduction In the practice of radiation therapy better local control and survival are often associated with increased delivered dose [1]. The greatest limitation to increasing treatment dose is induced by normal lung toxicity. Due to nonperiodic breathing pattern in patients, the planned dose is very often not delivered as intended. Interfractional target motion considerably deteriorates the geometric accuracy of the delivery process. In the recent past, systems and methodologies such as TomoTherapy [2, 3] and cone-beam computer tomography (CBCT) [4, 5] were developed and used in clinical practice to improve treatment planning and delivery. A quick and accurate method of contouring structures would be useful to improve the efficacy of these systems. Manual segmentation is too time-consuming, making rapid imaging and automated target delineation very attractive for motion management in radiation therapy. A typical four-dimensional (4D) data for radiation treatment planning in lung cancer includes 10 phases (separated by 10% difference from 0 to 100% of the breathing cycle) and approximately 100 images per phase. To estimate
References
[1]
F. M. Kong, R. K. Ten Haken, M. J. Schipper et al., “High-dose radiation improved local tumor control and overall survival in patients with inoperable/unresectable non-small-cell lung cancer: long-term results of a radiation dose escalation study,” International Journal of Radiation Oncology Biology Physics, vol. 63, no. 2, pp. 324–333, 2005.
[2]
T. R. Mackie, T. Holmes, S. Swerdloff et al., “Tomotherapy: a new concept for the delivery of dynamic conformal radiotherapy,” Medical Physics, vol. 20, no. 6, pp. 1709–1719, 1993.
[3]
K. J. Ruchala, G. H. Olivera, E. A. Schloesser, and T. R. Mackie, “Megavoltage CT on a tomotherapy system,” Physics in Medicine and Biology, vol. 44, no. 10, pp. 2597–2621, 1999.
[4]
J. Pouliot, A. Bani-Hashemi, J. Chen et al., “Low-dose megavoltage cone-beam CT for radiation therapy,” International Journal of Radiation Oncology Biology Physics, vol. 61, no. 2, pp. 552–560, 2005.
[5]
J. J. Sonke, L. Zijp, P. Remeijer, and M. Van Herk, “Respiratory correlated cone beam CT,” Medical Physics, vol. 32, no. 4, pp. 1176–1186, 2005.
[6]
P. Giraud, S. Elles, S. Helfre et al., “Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists,” Radiotherapy and Oncology, vol. 62, no. 1, pp. 27–36, 2002.
[7]
R. Speight, J. Sykes, R. Lindsay, K. Franks, and D. Thwaites, “The evaluation of a deformable image registration segmentation technique for semi-automating internal target volume (ITV) production from 4DCT images of lung stereotactic body radiotherapy (SBRT) patients,” Radiotherapy and Oncology, vol. 98, no. 2, pp. 277–283, 2011.
[8]
A. Al-Mayah, J. Moseley, M. Velec, S. Hunter, and K. Brock, “Deformable image registration of heterogeneous human lung incorporating the bronchial tree,” Medical Physics, vol. 37, no. 9, pp. 4560–4571, 2010.
[9]
Y. Yim, H. Hong, and Y. G. Shin, “Deformable lung registration between exhale and inhale CT scans using active cells in a combined gradient force approach,” Medical Physics, vol. 37, no. 8, pp. 4307–4317, 2010.
[10]
K. Wijesooriya, E. Weiss, V. Dill et al., “Quantifying the accuracy of automated structure segmentation in 4D CT images using a deformable image registration algorithm,” Medical Physics, vol. 35, no. 4, pp. 1251–1260, 2008.
[11]
V. V. Chebrolu, D. Tewatia, J. Dai, et al., “Four-dimensional MRI/CT based auto-adaptive segmentation for real-time radiotherapy in lung cancer treatment,” Medical Physics, vol. 38, p. 3846, 2011.
[12]
V. V. Chebrolu, J. Dai, D. Tewatia, et al., “Real-time automated target volume and motion quantification for adaptive four-dimensional MRI/CT guided radiation therapy,” International Journal of Radiation Oncology, Biology, Physics, vol. 81, no. 2, p. S814, 2011.
[13]
R. Sedgewick, Algorithms in C, Addison-Wesley, Reading, Mass, USA, 1998.
[14]
K. Malinowski, K. Lechleiter, J. Hubenschmidt, D. Low, and P. Parikh, “Use of the 4D phantom to test real—time targeted radiation therapy device accuracy,” Medical Physics, vol. 34, article 2611, 2007.
[15]
P. A. Yushkevich, J. Piven, H. C. Hazlett et al., “User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability,” NeuroImage, vol. 31, no. 3, pp. 1116–1128, 2006.
[16]
C. B. Caldwell, K. Mah, Y. C. Ung et al., “Observer variation in contouring gross tumor volume in patients with poorly defined non-small-cell lung tumors on CT: the impact of 18FDG-hybrid PET fusion,” International Journal of Radiation Oncology Biology Physics, vol. 51, no. 4, pp. 923–931, 2001.
[17]
R. J. H. M. Steenbakkers, J. C. Duppen, I. Fitton et al., “Observer variation in target volume delineation of lung cancer related to radiation oncologist-computer interaction: a ‘Big Brother’ evaluation,” Radiotherapy and Oncology, vol. 77, no. 2, pp. 182–190, 2005.
[18]
M. Staring, S. Klein, and J. P. W. Pluim, “A rigidity penalty term for nonrigid registration,” Medical Physics, vol. 34, no. 11, pp. 4098–4108, 2007.
[19]
J. Piper, “Evaluation of an intensity-based free-form deformable registration algorithm,” Medical Physics, vol. 34, no. 6, pp. 2353–2354, 2007.
[20]
J. Piper, M. Duchateau, A. Nelson, D. Verellen, and M. De Ridder, “Characterizing Accuracy in 4DCT Deformable Registration Using the POPI Model,” Med Phys, pp. 40–168, 2013.
