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SU‐E‐J‐212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach
Author(s) -
Ruan D,
Yang Y,
Cao M,
Hu P,
Low D
Publication year - 2014
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 - 10.1118/1.4888265
Subject(s) - computer science , artificial intelligence , robustness (evolution) , pattern recognition (psychology) , contrast (vision) , modality (human–computer interaction) , biology , biochemistry , gene
Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI‐only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron‐density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR‐only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration‐to identify bones, we propose a novel learning‐based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE‐MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post‐processing is efficient. The improved efficiency and robustness enables a first translation to MR‐only routine. The scheme generalizes to multiple tissue classes.