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Wei Wu,1,* Yan-Qing Yang,2,* Yang Yang,1,* Yu-Ming Yang,2 Hong Wang,2 Kai-Yuan Zhang,1 Li Guo,3 Hong-Fei Ge,1 Jie Liu,2 Hua Feng11Department of Neurosurgery, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, People’s Republic of China; 2Key Laboratory of Flexible Electronics (KLOFE) Institute of Advanced Materials (IAM), Nanjing Tech University, Nanjing 211800, People’s Republic of China; 3Department of Endocrinology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, People’s Republic of China*These authors contributed equally to this workBackground: In vivo fluorescence imaging in the second near-infrared (NIR-II, 1000–1700 nm) window using organic fluorophores has great advantages, but generally suffers from a relatively low fluorescence quantum yield (mostly less than 2%). In this study, organic nanoparticles (L1013 NPs) with a high fluorescence quantum yield (9.9%) were systhesized for in vivo imaging.Methods: A molecule (BTPPA) with donor-acceptor-donor structure and aggregation-induced emission enabling moieties was prepared. BTPPA molecules were then encapsulated into nanoparticles (L1013 NPs) using a nanoprecipitation method. The L1013 NPs were intravenously injected into the mice (including normal, stroke and tumor models) for vascular and tumor imaging.Results: L1013 NPs excited at 808 nm exhibit NIR-II emission with a peak at 1013 nm and an emission tail extending to 1400 nm. They have a quantum yield of 9.9% and also show excellent photo/colloidal stabilities and negligible in vitro and in vivo toxicity. We use L1013 NPs for noninvasive real-time visualization of mouse hindlimb and cerebral vessels (including stroke pathology) under a very low power density (4.6–40 mW cm‒2) and short exposure time (40–100 ms). Moreover, L1013 NPs are able to localize tumor pathology, with a tumor-to-normal tissue ratio of 11.7±1.3, which is unusually high for NIR-II fluorescent imaging through passive targeting strategy.Conclusion: L1013 NPs demonstrate the potential for a range of clinical applications, especially for tumor surgery.Keywords: fluorescence NIR-II imaging, aggregation-induced emission, organic nanoparticle, vascular imaging, tumor imaging

Accurate muscle geometry for musculoskeletal models is important to enable accurate subject-specific simulations. Commonly, linear scaling is used to obtain individualised muscle geometry. More advanced methods include non-linear scaling using segmented bone surfaces and manual or semi-automatic digitisation of muscle paths from medical images. In this study, a new scaling method combining non-linear scaling with reconstructions of bone surfaces using statistical shape modelling is presented. Statistical Shape Models (SSMs) of femur and tibia/fibula were used to reconstruct bone surfaces of nine subjects. Reference models were created by morphing manually digitised muscle paths to mean shapes of the SSMs using non-linear transformations and inter-subject variability was calculated. Subject-specific models of muscle attachment and via points were created from three reference models. The accuracy was evaluated by calculating the differences between the scaled and manually digitised models. The points defining the muscle paths showed large inter-subject variability at the thigh and shank – up to 26mm; this was found to limit the accuracy of all studied scaling methods. Errors for the subject-specific muscle point reconstructions of the thigh could be decreased by 9% to 20% by using the non-linear scaling compared to a typical linear scaling method. We conclude that the proposed non-linear scaling method is more accurate than linear scaling methods. Thus, when combined with the ability to reconstruct bone surfaces from incomplete or scattered geometry data using statistical shape models our proposed method is an alternative to linear scaling methods.