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Frequency-selective SVD-based magnetic resonance spectroscopy with prior knowledge
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systems and Control.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systems and Control.
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology. Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Mathematics and Computer Science, Department of Information Technology, Automatic control. Systems and Control.
2004 (English)In: Conference Record of the 38th Asilomar Conference on Signals, Systems, and Computers, 2004Conference paper, Published paper (Refereed)
Abstract [en]

We present a novel method for exploiting prior knowledge in magnetic resonance spectroscopy (MRS) based on the frequency-selective SELF-SVD method which was introduced in \cite{Sandgren}. More specifically, we use the common assumption that the magnetic resonance (MR) data is modeled by the superposition of a given number of exponentially damped sinusoids. As an application we consider the ATP (\textit{a}denosine \textit{t}ri\textit{p}hosphate) complex of an MRS signal and we use the fact that the dampings $\alpha_k$ and frequencies $\omega_k$ of the peaks of the ATP complex satisfy the following conditions: $\alpha_k = \alpha$ and $\omega_k = \omega + k\Delta$, where $\alpha$ and $\omega$ are unknown and $\Delta$ is known. Numerical examples mimicking $^{31}$P MRS data are included. The results show the superiority especially in speed of this new approach, which we will refer to as the FREEK (\textit{fre}quency-selective \textit{e}stimation with prior \textit{k}nowledge) method.

Place, publisher, year, edition, pages
2004.
Identifiers
URN: urn:nbn:se:uu:diva-70359OAI: oai:DiVA.org:uu-70359DiVA: diva2:98270
Available from: 2005-05-04 Created: 2005-05-04

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Sandgren, NiclasStoica, PeterSelén, Yngve

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