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MO‐DE‐210‐05: Improved Accuracy of Liver Feature Motion Estimation in B‐Mode Ultrasound for Image‐Guided Radiation Therapy
Author(s) -
O'shea T,
Bamber J,
Harris E
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 - 10.1118/1.4925367
Subject(s) - motion estimation , artificial intelligence , computer vision , computer science , match moving , template matching , feature (linguistics) , ultrasound , motion compensation , mathematics , nuclear medicine , motion (physics) , medicine , radiology , image (mathematics) , linguistics , philosophy
Purpose: In similarity‐measure based motion estimation incremental tracking (or template update) is challenging due to quantization, bias and accumulation of tracking errors. A method is presented which aims to improve the accuracy of incrementally tracked liver feature motion in long ultrasound sequences. Methods: Liver ultrasound data from five healthy volunteers under free breathing were used (15 to 17 Hz imaging rate, 2.9 to 5.5 minutes in length). A normalised cross‐correlation template matching algorithm was implemented to estimate tissue motion. Blood vessel motion was manually annotated for comparison with three tracking code implementations: (i) naive incremental tracking (IT), (ii) IT plus a similarity threshold (ST) template‐update method and (iii) ST coupled with a prediction‐based state observer, known as the alpha‐beta filter (ABST). Results: The ABST method produced substantial improvements in vessel tracking accuracy for two‐dimensional vessel motion ranging from 7.9 mm to 40.4 mm (with mean respiratory period: 4.0 ± 1.1 s). The mean and 95% tracking errors were 1.6 mm and 1.4 mm, respectively (compared to 6.2 mm and 9.1 mm, respectively for naive incremental tracking). Conclusions: High confidence in the output motion estimation data is required for ultrasound‐based motion estimation for radiation therapy beam tracking and gating. The method presented has potential for monitoring liver vessel translational motion in high frame rate B‐mode data with the required accuracy. This work is support by Cancer Research UK Programme Grant C33589/A19727.