scholarly journals Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO)

2020 ◽  
Vol 85 (4) ◽  
pp. 2294-2308
Author(s):  
Barbara Dymerska ◽  
Korbinian Eckstein ◽  
Beata Bachrata ◽  
Bernard Siow ◽  
Siegfried Trattnig ◽  
...  
2020 ◽  
Author(s):  
Barbara Dymerska ◽  
Korbinian Eckstein ◽  
Beata Bachrata ◽  
Bernard Siow ◽  
Siegfried Trattnig ◽  
...  

ABSTRACTPurposeTo develop a rapid and accurate MRI phase unwrapping technique for challenging phase topographies encountered at high magnetic fields, around metal implants or post-operative cavities, that is sufficiently fast to be applied to large group studies including Quantitative Susceptibility Mapping and functional MRI (with phase-based distortion correction).MethodsThe proposed path-following phase unwrapping algorithm, ROMEO, estimates the coherence of the signal both in space - using MRI magnitude and phase information - and over time, assuming approximately linear temporal phase evolution. This information is combined to form a quality map that guides the unwrapping along a three-dimensional path through the object using a computationally efficient minimum spanning tree algorithm. ROMEO was tested against the two most commonly used exact phase unwrapping methods: PRELUDE and BEST PATH in simulated topographies and at several field strengths: in 3 T and 7 T in vivo human head images and 9.4 T ex vivo rat head images.ResultsROMEO was more reliable than PRELUDE and BEST PATH, yielding unwrapping results with excellent temporal stability for multi-echo or multi-time-point data. ROMEO does not require image masking and delivers results within seconds even in large, highly wrapped multi-echo datasets (e.g. 9 seconds for a 7 T head dataset with 31 echoes and a 208 x 208 x 96 matrix size).ConclusionOverall, ROMEO was both faster and more accurate than PRELUDE and BEST PATH delivering exact results within seconds, which is well below typical image acquisition times, enabling potential on-console application.


2014 ◽  
Vol 701-702 ◽  
pp. 50-53
Author(s):  
Jian Liang Meng ◽  
Da Wei Li

Query recommendation as an important tool to enhance the user search efficiency has gradually become a hotspot. In the context of big data, using the MapReduce programming model, combined with distributed minimum spanning tree algorithm, a parallel query recommended method based on MapReduce was proposed in this paper. The final results show that the efficiency of query recommendation was greatly improved through parallel computing.


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