Gradient non-linearity correction for spherical mean diffusion imaging

Paquette, Michael; Eichner, Cornelius; Anwander, Alfred

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

会議論文

Gradient non-linearity correction for spherical mean diffusion imaging

MPS-Authors

/persons/resource/persons220619

Paquette, Michael
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons39190

Eichner, Cornelius
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons19530

Anwander, Alfred
Department Neuropsychology, MPI for Human Cognitive and Brain Sciences, Max Planck Society;

External Resource

http://indexsmart.mirasmart.com/ISMRM2019/PDFfiles/0550.html
(全文テキスト（全般）)

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

Paquette, M., Eichner, C., & Anwander, A. (2019). Gradient non-linearity correction for spherical mean diffusion imaging. In Proceedings of the 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM).

引用: https://hdl.handle.net/21.11116/0000-0003-AC1E-B

要旨

Gradient non-linearities are a significant source of errors in MRI systems with strong gradients. In the case of diffusion imaging, they induce spatial deviation of the b-vectors. The spherical mean methods in diffusion relies on the acquisition of spherical b-shell. To recover accurate spherical mean values, it is necessary to undistort the diffusion signal. Therefore, we evaluated three correction methods for gradient non-linearities using the Connectom gradient system as a showcase. We show how a simple heuristic can reduce the spherical mean errors by 20 folds.