Intensity-based registration of freehand 3D ultrasound and CT-scan images of the kidney
Tóm tắt
Objectives This paper presents a method to register a pre-operative computed-tomography (CT) volume to a sparse set of intra-operative ultra-sound (US) slices. In the context of percutaneous renal puncture, the aim is to transfer planning information to an intra-operative coordinate system.
Materials and methods The spatial position of the US slices is measured by optically localizing a calibrated probe. Assuming the reproducibility of kidney motion during breathing, and no deformation of the organ, the method consists in optimizing a rigid 6 degree of freedom transform by evaluating at each step the similarity between the set of US images and the CT volume. The correlation between CT and US images being naturally rather poor, the images were preprocessed in order to increase their similarity. Among the similarity measures formerly studied in the context of medical image registration, correlation ratio turned out to be one of the most accurate and appropriate, particularly with the chosen non-derivative minimization scheme, namely Powell-Brent’s. The resulting matching transforms are compared to a standard rigid surface registration involving segmentation, regarding both accuracy and repeatability.
Results The obtained results are presented and discussed.
Tài liệu tham khảo
Leroy A, Mozer P, Payan Y, Richard F, Chartier-Kastler E, Troccaz J (2002) Percutaneous renal puncture: requirements and preliminary results. Surgetica’02, pp 303–309
Mozer P, Leroy A, Payan Y, Troccaz J, Chartier-Kastler E and Richard F (2005). Computer-assisted access to the kidney. Med Robot Comput Assist Surg 1(4): 58–66
Wells WM, Viola P, Atsumi H, Nakajima S and Kikinis R (1996). Multi-modal volume registration by maximization of mutual information. Med Image Anal 1(1): 35–51
Maes F, Collignon A, Vandermeulen D, Marchal G and Suetens P (1997). Multimodality image registration by maximization of mutual information. IEEE Trans Med Imag 16(2): 187–198
Maes F, Vandermeulen D and Suetens P (1999). Comparative evaluation of multiresolution optimization strategies for multimodality registration by maximization of mutual information. Med Image Anal 3(4): 373–386
Jenkinson M and Smith S (2001). A global optimization method for robust affine registration of brain images. Med Image Anal 5(2): 143–156
Studholme C, Hill DLG and Hawkes DJ (1996). Automated 3-D registration of MR and CT images of the head. Med Image Anal 1(2): 163–175
Sarrut D, Miguet S (1999) Similarity measures for image registration. European workshop on content-based multimedia indexing. IHMPT-IRIT, Toulouse, France, pp 263–270
Pluim JPW, Maintz JBA and Viergever MA (2000). Image registration by maximization of combined mutual information and gradient information. IEEE Trans Med Imag 19(8): 809–814
Haber E, Modersitzki J (2006) Intensity gradient based registration and fusion of multi-modal images, MICCAI 2006. LNCS 4191, pp 726–733
Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration, MICCAI’98. pp 1115–1124
Roche A, Pennec X, Malandain G and Ayache N (2001). Rigid of 3D ultrasound with MR images: a new approach combining intensity and gradient. IEEE Trans Med Imag 20(10): 1038–1049
Penney GP, Blackall JM, Hamady MS, Sabharwal T, Adam A and Hawkes DJ (2004). Registration of freehand 3D ultrasound and magnetic resonance liver images. Med Image Anal 8(1): 81–91
Jannin P, Fitzpatrick JM, Hawkes DJ, Pennec X, Shahidi R and Vannier MW (2002). Validation of medical image processing in image-gudied therapy. IEEE Trans Med Imag 21(12): 1445–1449
Langen K and Jones D (2001). Organ motion and its management. Int J Radiat Oncol Biol Phys 50(1): 265–278
Schwarz L, Richaud J, Buffat L, Touboul E and Schlienger M (1994). Kidney mobility during respiration. Radiother Oncol 32: 84–86
Lango T (2000) Ultrasound guided surgery: image processing and navigation. PhD Thesis, Norwegian University of Science and Technology
Czerwinski RN, Jones DL and O’Brien WD (1999). Detection of lines and boundaries in speckle images—application to medical ultrasound. IEEE Trans Med Imag 18(2): 126–136
Press WH, Flannery BP, Teukolsky SA and Vetterling WT (1992). Numerical recipes in C: the art of scientific computing. Cambridge University Press, London
Arun KS and Huang TS (1987). Least-squares fitting of 2 3-D point sets. IEEE Trans Pattern Anal Mach Intell 9(5): 699–700
Bansal R, Staiab L, Chen Z, Rangarajan A, Kniseky J, Nath R and Duncan J (1999). A minimax entropy registration framework for patient setup verification in radiotherapy. Comput Aided Surg 4(6): 287–304
Ionescu G, Lavallée S, Demongeot J (1999) Automated registration of ultrasound with CT images: application to computer assisted prostate radiotherapy and orthopedics. In: Proceedings of MICCAI’99. LNCS, vol 1679, pp 768–777