A Bayesian reconstruction method for micro-rotation imaging in light microscopy

2008 ◽  
Vol 71 (2) ◽  
pp. 158-167 ◽  
Author(s):  
Danai Laksameethanasan ◽  
Sami S. Brandt ◽  
Peter Engelhardt ◽  
Olivier Renaud ◽  
Spencer L. Shorte
Keyword(s):  
Light Microscopy ◽  
Reconstruction Method ◽  
Bayesian Reconstruction

Sparse Bayesian reconstruction method for multispectral bioluminescence tomography

2010 ◽  
Vol 8 (10) ◽  
pp. 1010-1014 ◽  
Author(s):  
冯金超 Jinchao Feng ◽  
贾克斌 Kebin Jia ◽  
秦承虎 Chenghu Qin ◽  
朱守平 Shouping Zhu ◽  
杨鑫 Xin Yang ◽  
...  
Keyword(s):  
Reconstruction Method ◽  
Bayesian Reconstruction ◽  
Bioluminescence Tomography

scholarly journals Recut: a Concurrent Framework for Sparse Reconstruction of Neuronal Morphology

2021 ◽  
Author(s):  
Karl Marrett ◽  
Muye Zhu ◽  
Yuze Chi ◽  
Zhe Chen ◽  
Chris Choi ◽  
...  
Keyword(s):  
Light Microscopy ◽  
Data Representation ◽  
Neuronal Morphology ◽  
Reconstruction Method ◽  
Technological Advancement ◽  
Adaptive Grids ◽  
Reconstruction Process ◽  
Fine Grained ◽  
Resource Limited ◽  
Neuroimaging Data

Interpreting the influx of microscopy and neuroimaging data is bottlenecked by neuronal reconstruction's long-standing issues in accuracy, automation, and scalability. Rapidly increasing data size is particularly concerning for modern computing infrastructure due to the wall in memory bandwidth which historically has witnessed the slowest rate of technological advancement. Recut is an end to end reconstruction pipeline that takes raw large-volume light microscopy images and yields filtered or tuned automated neuronal reconstructions that require minimal proofreading and no other manual intervention. By leveraging adaptive grids and other methods, Recut also has a unified data representation with up to a 509× reduction in memory footprint resulting in an 89.5× throughput increase and enabling an effective 64× increase in the scale of volumes that can be skeletonized on servers or resource limited devices. Recut also employs coarse and fine-grained parallelism to achieve speedup factors beyond CPU core count in sparse settings when compared to the current fastest reconstruction method. By leveraging the sparsity in light microscopy datasets, this can allow full brains to be processed in-memory, a property which may significantly shift the compute needs of the neuroimaging community. The scale and speed of Recut fundamentally changes the reconstruction process, allowing an interactive yet deeply automated workflow.

scholarly journals A fast Edge-preserving Bayesian reconstruction method for Parallel Imaging applications in cardiac MRI

2010 ◽  
Vol 65 (1) ◽  
pp. 184-189 ◽  
Author(s):  
Gurmeet Singh ◽  
Ashish Raj ◽  
Bryan Kressler ◽  
Thanh D. Nguyen ◽  
Pascal Spincemaille ◽  
...  
Keyword(s):  
Cardiac Mri ◽  
Parallel Imaging ◽  
Reconstruction Method ◽  
Edge Preserving ◽  
Bayesian Reconstruction

scholarly journals Jointly super-resolved and optically sectioned Bayesian reconstruction method for structured illumination microscopy

2019 ◽  
Vol 27 (23) ◽  
pp. 33251 ◽  
Author(s):  
Yann Lai-Tim ◽  
Laurent M. Mugnier ◽  
François Orieux ◽  
Roberto Baena-Gallé ◽  
Michel Paques ◽  
...  
Keyword(s):  
Reconstruction Method ◽  
Structured Illumination ◽  
Structured Illumination Microscopy ◽  
Bayesian Reconstruction

Bayesian reconstruction method for underwater 3D range-gated imaging enhancement

2020 ◽  
Vol 59 (2) ◽  
pp. 370
Author(s):  
Xiaojun Yin ◽  
Hu Cheng ◽  
Kecheng Yang ◽  
Min Xia
Keyword(s):  
Reconstruction Method ◽  
Bayesian Reconstruction ◽  
Gated Imaging
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