A novel framework for repeated measurements in diffusion tensor imaging

Mohammad Alipoor, Irene Y H Gu, Andrew Mehnert, Göran Starck, Stephan E. Maier

Research output: Chapter in Book/Conference paperConference paper

Abstract

In the context of diffusion tensor imaging (DTI), the utility of making repeated measurements in each diffusion sensitizing direction has been the subject of numerous studies. One can estimate the true signal value using either the raw complex-valued data or the real-valued magnitude signal. While conventional methods focus on the former strategy, this paper proposes a new framework for acquiring/processing repeated measurements based on the latter strategy. The aim is to enhance the DTI processing pipeline by adding a diffusion signal estimator (DSE). This permits us to exploit the knowledge of the noise distribution to estimate the true signal value in each direction. An extensive study of the proposed framework, including theoretical analysis, experiments with synthetic data, performance evaluation and comparisons is presented. Our results show that the precision of estimated diffusion parameters is dependent on the number of available samples and the manner in which the DSE accounts for noise. The proposed framework improves the precision in estimation of diffusion parameters given a sufficient number of unique measurements. This encourages future work with rich real datasets and downstream applications.

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
EditorsEve Lee
PublisherAssociation for Computing Machinery (ACM)
Pages1-6
Number of pages6
VolumePart F125793
ISBN (Electronic)9781450348249
DOIs
Publication statusPublished - 12 Nov 2016
Event3rd International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2016 - Taipei, Taiwan, Province of China
Duration: 12 Nov 201614 Nov 2016

Conference

Conference3rd International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2016
CountryTaiwan, Province of China
CityTaipei
Period12/11/1614/11/16

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