TY - GEN
T1 - Optimal matching for prostate brachytherapy seed localization with dimension reduction
AU - Lee, Junghoon
AU - Labat, Christian
AU - Jain, Ameet K.
AU - Song, Danny Y.
AU - Burdette, Everette C.
AU - Fichtinger, Gabor
AU - Prince, Jerry L.
PY - 2009
Y1 - 2009
N2 - In prostate brachytherapy, x-ray fluoroscopy has been used for intra-operative dosimetry to provide qualitative assessment of implant quality. More recent developments have made possible 3D localization of the implanted radioactive seeds. This is usually modeled as an assignment problem and solved by resolving the correspondence of seeds. It is, however, NP-hard, and the problem is even harder in practice due to the significant number of hidden seeds. In this paper, we propose an algorithm that can find an optimal solution from multiple projection images with hidden seeds. It solves an equivalent problem with reduced dimensional complexity, thus allowing us to find an optimal solution in polynomial time. Simulation results show the robustness of the algorithm. It was validated on 5 phantom and 18 patient datasets, successfully localizing the seeds with detection rate of ≥97.6 % and reconstruction error of ≤1.2 mm. This is considered to be clinically excellent performance.
AB - In prostate brachytherapy, x-ray fluoroscopy has been used for intra-operative dosimetry to provide qualitative assessment of implant quality. More recent developments have made possible 3D localization of the implanted radioactive seeds. This is usually modeled as an assignment problem and solved by resolving the correspondence of seeds. It is, however, NP-hard, and the problem is even harder in practice due to the significant number of hidden seeds. In this paper, we propose an algorithm that can find an optimal solution from multiple projection images with hidden seeds. It solves an equivalent problem with reduced dimensional complexity, thus allowing us to find an optimal solution in polynomial time. Simulation results show the robustness of the algorithm. It was validated on 5 phantom and 18 patient datasets, successfully localizing the seeds with detection rate of ≥97.6 % and reconstruction error of ≤1.2 mm. This is considered to be clinically excellent performance.
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U2 - 10.1007/978-3-642-04268-3_8
DO - 10.1007/978-3-642-04268-3_8
M3 - Conference contribution
C2 - 20425971
AN - SCOPUS:79551682078
SN - 3642042678
SN - 9783642042676
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 59
EP - 66
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009 - 12th International Conference, Proceedings
T2 - 12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Y2 - 20 September 2009 through 24 September 2009
ER -