Denoising of Z-spectra for stable CEST MRI using principal component analysis

Breitling, J; Deshmane, A; Goerke, S; Herz, K; Ladd, M; Scheffler, K; Bachert, P; Zaiss, M

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

ポスター

Denoising of Z-spectra for stable CEST MRI using principal component analysis

MPS-Authors

/persons/resource/persons215996

Deshmane, A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons216025

Herz, K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84187

Scheffler, K
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons214560

Zaiss, M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

External Resource

http://indexsmart.mirasmart.com/ISMRM2019/PDFfiles/0143.html
(要旨)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Breitling, J., Deshmane, A., Goerke, S., Herz, K., Ladd, M., Scheffler, K., Bachert, P., & Zaiss, M. (2019). Denoising of Z-spectra for stable CEST MRI using principal component analysis. Poster presented at 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019), Montréal, QC, Canada.

引用: https://hdl.handle.net/21.11116/0000-0003-96DB-D

要旨

Chemical exchange saturation transfer (CEST) MRI allows for the indirect detection of low-concentration biomolecules by their saturation transfer to the abundant water pool. However, reliable quantification of CEST effects remains challenging and requires a high image signal-to-noise ratio. In this study, we show that principle component analysis can provide a denoising capability which is comparable or better than 6-fold averaging. Principle component analysis allows identifying similarities across all noisy Z-spectra, and thus, extracting the relevant information. The resulting denoised Z-spectra provide a more stable basis for quantification of selective CEST effects, without requiring additional measurements.