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SU‐F‐303‐11: Implementation and Applications of Rapid, SIFT‐Based Cine MR Image Binning and Region Tracking
Author(s) -
Mazur T,
Wang Y,
FischerValuck B,
Acharya S,
Kashani R,
Li H,
Yang D,
Zoberi I,
Thomas M,
Mutic S,
Li H
Publication year - 2015
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 - 10.1118/1.4925238
Subject(s) - scale invariant feature transform , artificial intelligence , computer vision , pixel , computer science , tracking (education) , pattern recognition (psychology) , image registration , matching (statistics) , feature (linguistics) , frame (networking) , image (mathematics) , mathematics , psychology , telecommunications , pedagogy , linguistics , statistics , philosophy
Purpose: To develop a novel and rapid, SIFT‐based algorithm for assessing feature motion on cine MR images acquired during MRI‐guided radiotherapy treatments. In particular, we apply SIFT descriptors toward both partitioning cine images into respiratory states and tracking regions across frames. Methods: Among a training set of images acquired during a fraction, we densely assign SIFT descriptors to pixels within the images. We cluster these descriptors across all frames in order to produce a dictionary of trackable features. Associating the best‐matching descriptors at every frame among the training images to these features, we construct motion traces for the features. We use these traces to define respiratory bins for sorting images in order to facilitate robust pixel‐by‐pixel tracking. Instead of applying conventional methods for identifying pixel correspondences across frames we utilize a recently‐developed algorithm that derives correspondences via a matching objective for SIFT descriptors. Results: We apply these methods to a collection of lung, abdominal, and breast patients. We evaluate the procedure for respiratory binning using target sites exhibiting high‐amplitude motion among 20 lung and abdominal patients. In particular, we investigate whether these methods yield minimal variation between images within a bin by perturbing the resulting image distributions among bins. Moreover, we compare the motion between averaged images across respiratory states to 4DCT data for these patients. We evaluate the algorithm for obtaining pixel correspondences between frames by tracking contours among a set of breast patients. As an initial case, we track easily‐identifiable edges of lumpectomy cavities that show minimal motion over treatment. Conclusions: These SIFT‐based methods reliably extract motion information from cine MR images acquired during patient treatments. While we performed our analysis retrospectively, the algorithm lends itself to prospective motion assessment. Applications of these methods include motion assessment, identifying treatment windows for gating, and determining optimal margins for treatment.