Biophotonics Congress: Optics in the Life Sciences Congress 2019 (BODA,BRAIN,NTM,OMA,OMP) 2019
DOI: 10.1364/omp.2019.ow2d.4
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Automated registration for optoacoustic tomography and MRI

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Cited by 2 publications
(2 citation statements)
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“…Han et al [44] proposed a 3D modeling method to calculate the optical fluence distribution based on a dual-modality PA/US system, and then used this information to compensate the PA imaging results. For multimodal PAT-MRI imaging of rigid parts, such as the animal head [50-59], Ren et al developed a toolbox "RegOA" [50] and proposed a fully automated registration method for PAT-MRI multimodal brain imaging empowered by deep learning [53]. The datasets for network training were acquired in the PAT system and MRI scanner experimentally.…”
Section: Image Post-processing For Multimodal Imagingmentioning
confidence: 99%
See 1 more Smart Citation
“…Han et al [44] proposed a 3D modeling method to calculate the optical fluence distribution based on a dual-modality PA/US system, and then used this information to compensate the PA imaging results. For multimodal PAT-MRI imaging of rigid parts, such as the animal head [50-59], Ren et al developed a toolbox "RegOA" [50] and proposed a fully automated registration method for PAT-MRI multimodal brain imaging empowered by deep learning [53]. The datasets for network training were acquired in the PAT system and MRI scanner experimentally.…”
Section: Image Post-processing For Multimodal Imagingmentioning
confidence: 99%
“…However, they only applied image registration to rigid areas, such as the animal head, but not to abdomen. To improve image registration of both body and tumor For multimodal PAT-MRI imaging of rigid parts, such as the animal head [50-59], Ren et al developed a toolbox "RegOA" [50] and proposed a fully automated registration method for PAT-MRI multimodal brain imaging empowered by deep learning [53]. The datasets for network training were acquired in the PAT system and MRI scanner experimentally.…”
Section: Image Post-processing For Multimodal Imagingmentioning
confidence: 99%