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TH‐D‐303A‐06: Automatic Image and Contour Warping Based On 3D Salient Points for Assessing the Need for Replanning in IGRT
Author(s) -
Allaire S,
Breen S,
Hope A,
Pekar V,
Jaffray D
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3182688
Subject(s) - image warping , artificial intelligence , computer vision , image registration , image guided radiation therapy , computer science , radiation treatment planning , cone beam computed tomography , medical imaging , computed tomography , mathematics , pattern recognition (psychology) , image (mathematics) , medicine , radiation therapy , radiology
Purpose: To help assess the need for RT replanning by automatically warping the CT image and patient contours from planning onto the current fraction Cone‐Beam CT image. Method and Materials: A non‐rigid auto‐registration scheme has been developed which uses anchor interest points in images. It involves four steps: a) Extract a patient‐specific compressed model in terms of multiscale distinctive salient points from the planning CT image, using a 3D SIFT detector adapted for both bony and soft tissue features; b) Retrieve these points in the current CBCT image, via multiscale template‐matching maximizing local correlation; c) Derive a thin‐plate‐spline non‐rigid transformation from point pairs; d) Warp the CT image and/or ROIs therein onto the CBCT image. The auto‐warped CT gray‐value densities are then useful as surrogate density attenuation parameters to update the dose map according to the treatment beams planned; along with the auto‐warped delineations, this leads to up‐to‐date DVHs, helping decisions. Four patients showing significant changes through 35‐fraction head‐and‐neck treatments were selected retrospectively, with their planning ROIs and several recontoured critical/node structures in the mid‐treatment CBCT. Results: For each patient, over 1000 truly salient points were extracted and retrieved within 2 minutes; the corresponding registration map was computed and applied within five minutes. Unlike rigid alignment, the warped image and contours clearly adapt to the deformed anatomy highlighted by the mid‐treatment CBCT, typically neck shrinking, node shifting, and spine flexions. This reveals e.g. that a gross node coverage planned at mean dose 2.1Gy/fraction can decrease to less than 1.8Gy/fraction at fraction 35. Conclusion: Image and ROI warping based on salient points is feasible, and recommendable for updated dosimetry checks. In replanning events, the delineations warped to a newly acquired CT may provide a starting point to support time‐efficient re‐contouring. Conflict of Interest: Research sponsored in part by Philips Healthcare corporation.