Medical Implants: Enabling Angioplasty‐Ready “Smart” Stents to Detect In‐Stent Restenosis and Occlusion (Adv. Sci. 5/2018)
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Medical Implants: Enabling Angioplasty‐Ready “Smart” Stents to Detect In‐Stent Restenosis and Occlusion (Adv. Sci. 5/2018) ; volume:5 ; number:5 ; year:2018 ; extent:1
Advanced science ; 5, Heft 5 (2018) (gesamt 1)
- Creator
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Chen, Xing
Assadsangabi, Babak
Hsiang, York
Takahata, Kenichi
- DOI
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10.1002/advs.201870027
- URN
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urn:nbn:de:101:1-2022091006413091831131
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
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Associated
- Chen, Xing
- Assadsangabi, Babak
- Hsiang, York
- Takahata, Kenichi
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Evaluation of femoropopliteal in-stent restenosis characteristics stratified by stent design
A coupled multiphysics approach for modelling in-stent restenosis
Deep learning‐based surrogate modeling of coronary in‐stent restenosis
Deep learning‐based surrogate modeling of coronary in‐stent restenosis
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Rotarex®S rotational atherectomy combined with drug-coated balloon angioplasty for treating femoropopliteal artery in-stent restenosis
Intraprocedural endothelial cell seeding of arterial stents via biotin/avidin targeting mitigates in-stent restenosis
The impact of tissue characterization for in-stent restenosis with optical coherence tomography during excimer laser coronary angioplasty
Data-driven reduced order surrogate modeling for coronary in-stent restenosis
Enabling Angioplasty‐Ready “Smart” Stents to Detect In‐Stent Restenosis and Occlusion
In-stent restenosis in real world
In-stent restenosis and longitudinal stent deformation: a case report
Evaluation of femoropopliteal in-stent restenosis characteristics stratified by stent design
A coupled multiphysics approach for modelling in-stent restenosis
Deep learning‐based surrogate modeling of coronary in‐stent restenosis
Deep learning‐based surrogate modeling of coronary in‐stent restenosis
Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis
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Intraprocedural endothelial cell seeding of arterial stents via biotin/avidin targeting mitigates in-stent restenosis
The impact of tissue characterization for in-stent restenosis with optical coherence tomography during excimer laser coronary angioplasty