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 language | English |
|---|---|
| Title of host publication | ACM International Conference Proceeding Series |
| Editors | Eve Lee |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 1-6 |
| Number of pages | 6 |
| Volume | Part F125793 |
| ISBN (Electronic) | 9781450348249 |
| DOIs | |
| Publication status | Published - 12 Nov 2016 |
| Event | 3rd International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2016 - Taipei, Taiwan, Province of China Duration: 12 Nov 2016 → 14 Nov 2016 |
Conference
| Conference | 3rd International Conference on Biomedical and Bioinformatics Engineering, ICBBE 2016 |
|---|---|
| Country | Taiwan, Province of China |
| City | Taipei |
| Period | 12/11/16 → 14/11/16 |
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A novel framework for repeated measurements in diffusion tensor imaging. / Alipoor, Mohammad; Gu, Irene Y H; Mehnert, Andrew; Starck, Göran; Maier, Stephan E.
ACM International Conference Proceeding Series. ed. / Eve Lee. Vol. Part F125793 Association for Computing Machinery (ACM), 2016. p. 1-6.Research output: Chapter in Book/Conference paper › Conference paper
TY - GEN
T1 - A novel framework for repeated measurements in diffusion tensor imaging
AU - Alipoor, Mohammad
AU - Gu, Irene Y H
AU - Mehnert, Andrew
AU - Starck, Göran
AU - Maier, Stephan E.
PY - 2016/11/12
Y1 - 2016/11/12
N2 - 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.
AB - 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.
KW - Diffusion signal estimation
KW - Diffusion-weighted MRI
KW - Repeated measurements
KW - Rician noise
KW - Tensor estimation
UR - http://www.scopus.com/inward/record.url?scp=85014804576&partnerID=8YFLogxK
U2 - 10.1145/3022702.3022707
DO - 10.1145/3022702.3022707
M3 - Conference paper
VL - Part F125793
SP - 1
EP - 6
BT - ACM International Conference Proceeding Series
A2 - Lee, Eve
PB - Association for Computing Machinery (ACM)
ER -