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Abstract / Description of output
The utility of MR parametric mapping is limited due to the lengthy acquisition time. A Maximum Likelihood Estimation (MLE) and Parallel Imaging
(PI) method is presented for MR parameteric mapping. The approach is based on a high Signal to Noise ratio (SNR) assumption such that the noise
can be modelled as Gaussian and estimates the parameters that maximizes the signal from a multichannel coil. The method was tested on a multiecho
gradient-echo T2* mapping experiment in a phantom and a human brain. Accurate T2* maps were reconstructed up to an acceleration factor
of 6 with a small error for phantom and human brain.
(PI) method is presented for MR parameteric mapping. The approach is based on a high Signal to Noise ratio (SNR) assumption such that the noise
can be modelled as Gaussian and estimates the parameters that maximizes the signal from a multichannel coil. The method was tested on a multiecho
gradient-echo T2* mapping experiment in a phantom and a human brain. Accurate T2* maps were reconstructed up to an acceleration factor
of 6 with a small error for phantom and human brain.
| Original language | English |
|---|---|
| Publication status | Published - 21 Apr 2017 |
| Event | ISMRM 25th Annual Meeting - Hawaii, Honolulu, United States Duration: 22 Apr 2017 → 27 Apr 2017 https://www.ismrm.org/2017-annual-meeting-exhibition/ |
Conference
| Conference | ISMRM 25th Annual Meeting |
|---|---|
| Country/Territory | United States |
| City | Honolulu |
| Period | 22/04/17 → 27/04/17 |
| Internet address |
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Dive into the research topics of 'Accelerating T2* Mapping with Maximum Likelihood Estimation (MLE) and Parallel Imaging(PI)'. Together they form a unique fingerprint.Projects
- 3 Finished
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SpaRTaN: SpaRTaN: Sparse Representations and Compressed Sensing Training Network
1/10/14 → 30/09/18
Project: Research