TY - GEN
T1 - Plug-and-play priors for reconstruction-based placental image registration
AU - Xing, Jiarui
AU - Kamilov, Ulugbek
AU - Wu, Wenjie
AU - Wang, Yong
AU - Zhang, Miaomiao
N1 - Funding Information:
This work was supported by NIH grant R01HD094381, NIH grant R01AG053548, and BrightFocus FoundationA2017330S.
Funding Information:
Acknowledgement. This work was supported by NIH grant R01HD094381, NIH grant R01AG053548, and BrightFocus Foundation A2017330S.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.
AB - This paper presents a novel deformable registration framework, leveraging an image prior specified through a denoising function, for severely noise-corrupted placental images. Recent work on plug-and-play (PnP) priors has shown the state-of-the-art performance of reconstruction algorithms under such priors in a range of imaging applications. Integration of powerful image denoisers into advanced registration methods provides our model with a flexibility to accommodate datasets that have low signal-to-noise ratios (SNRs). We demonstrate the performance of our method under a wide variety of denoising models in the context of diffeomorphic image registration. Experimental results show that our model substantially improves the accuracy of spatial alignment in applications of 3D in-utero diffusion-weighted MR images (DW-MRI) that suffer from low SNR and large spatial transformations.
UR - http://www.scopus.com/inward/record.url?scp=85075744774&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32875-7_15
DO - 10.1007/978-3-030-32875-7_15
M3 - Conference contribution
AN - SCOPUS:85075744774
SN - 9783030328740
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 142
BT - Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis - 1st International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Wang, Qian
A2 - Gomez, Alberto
A2 - Hutter, Jana
A2 - Gomez, Alberto
A2 - Zimmer, Veronika
A2 - Hutter, Jana
A2 - Robinson, Emma
A2 - Christiaens, Daan
A2 - Melbourne, Andrew
A2 - McLeod, Kristin
A2 - Zettinig, Oliver
A2 - Licandro, Roxane
A2 - Turk, Esra Abaci
PB - Springer
T2 - 1st International Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -