Motion artifact removal and signal enhancement to achieve in vivo dynamic full field OCT
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TLDR
In this article, a filtering procedure based on singular value decomposition was proposed to remove artifacts arising from sample motion during dynamic full field OCT acquisitions, which succeeded in removing artifacts created by environmental noise from data acquired in a clinical setting, including in vivo data.Abstract:
We present a filtering procedure based on singular value decomposition to remove artifacts arising from sample motion during dynamic full field OCT acquisitions. The presented method succeeded in removing artifacts created by environmental noise from data acquired in a clinical setting, including in vivo data. Moreover, we report on a new method based on using the cumulative sum to compute dynamic images from raw signals, leading to a higher signal to noise ratio, and thus enabling dynamic imaging deeper in tissues.read more
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TL;DR: In this paper , a dynamic optical coherence tomography (OCT) system was used for label-free metabolic imaging of non-alcoholic fatty liver disease (NAFLD) mouse liver.
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Characterization and Analysis of Retinal Axial Motion at High Spatiotemporal Resolution and Its Implication for Real-Time Correction in Human Retinal Imaging
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Label-free metabolic imaging of non-alcoholic-fatty-liver-disease (NAFLD) liver by volumetric dynamic optical coherence tomography
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References
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Optical coherence tomography
David Huang,Eric A. Swanson,Charles P. Lin,Joel S. Schuman,William G. Stinson,Warren Chang,Michael R. Hee,Thomas J. Flotte,Kenton W. Gregory,Carmen A. Puliafito,James G. Fujimoto +10 more
TL;DR: OCT as discussed by the authors uses low-coherence interferometry to produce a two-dimensional image of optical scattering from internal tissue microstructures in a way analogous to ultrasonic pulse-echo imaging.
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Split-spectrum amplitude-decorrelation angiography with optical coherence tomography
Yali Jia,Ou Tan,Jason Tokayer,Benjamin Potsaid,Y. Wang,Jonathan J. Liu,Martin F. Kraus,Hrebesh M. Subhash,James G. Fujimoto,Joachim Hornegger,David Huang +10 more
TL;DR: In this paper, a split-spectrum amplitude-decorrelation angiography (SSADA) was proposed to improve the signal-to-noise ratio (SNR) of flow detection.
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Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
Ignacio Arganda-Carreras,Ignacio Arganda-Carreras,Verena Kaynig,Curtis Rueden,Kevin W. Eliceiri,Johannes Schindelin,Albert Cardona,H. Sebastian Seung +7 more
TL;DR: The Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically, is introduced.
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A review of optical coherence tomography angiography (OCTA).
TL;DR: OCTA is quick and non-invasive, and provides volumetric data with the clinical capability of specifically localizing and delineating pathology along with the ability to show both structural and blood flow information in tandem, its current limitations include a relatively small field of view.
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Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity
Charlie Demene,Thomas Deffieux,Mathieu Pernot,Bruno-Felix Osmanski,Valérie Biran,Jean-Luc Gennisson,Lim-Anna Sieu,Antoine Bergel,Stephanie Franqui,Jean-Michel Correas,Ivan Cohen,Olivier Baud,Mickael Tanter +12 more
TL;DR: The singular value decomposition (SVD) takes benefits of the different features of tissue and blood motion in terms of spatiotemporal coherence and strongly outperforms conventional clutter rejection filters based on high pass temporal filtering.
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