TY - GEN
T1 - Vascular registration in photoacoustic imaging by low-rank alignment via foreground,background and complement decomposition
AU - Bise, Ryoma
AU - Zheng, Yingqiang
AU - Sato, Imari
AU - Toi, Masakazu
N1 - Funding Information:
This work was funded by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).
Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.
AB - Photoacoustic (PA) imaging has been gaining attention as a new imaging modality that can non-invasively visualize blood vessels inside biological tissues. In the process of imaging large body parts through multi-scan fusion,alignment turns out to be an important issue,since body motion degrades image quality. In this paper,we carefully examine the characteristics of PA images and propose a novel registration method that achieves better alignment while effectively decomposing the shot volumes into low-rank foreground (blood vessels),dense background (noise),and sparse complement (corruption) components on the basis of the PA characteristics. The results of experiments using a challenging real data-set demonstrate the efficacy of the proposed method,which significantly improved image quality,and had the best alignment accuracy among the state-of-the-art methods tested.
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U2 - 10.1007/978-3-319-46726-9_38
DO - 10.1007/978-3-319-46726-9_38
M3 - Conference contribution
AN - SCOPUS:84996486800
SN - 9783319467252
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 326
EP - 334
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
A2 - Joskowicz, Leo
A2 - Sabuncu, Mert R.
A2 - Wells, William
A2 - Unal, Gozde
A2 - Ourselin, Sebastian
PB - Springer Verlag
ER -