Determinant of the information matrix: a new rotation invariant optimality metric to design gradient encoding schemes
Paper in proceedings, 2015

Minimum condition number (CN) gradient encoding scheme was introduced to diffusion MRI community more than a decade ago. It’s computation requires tedious numerical optimization which usually leads to sub-optimal solutions. The CN does not reflect any benefits in acquiring more measurements, i.e. it’s optimal value is constant for any number of measurements. Further, it is variable under rotation. In this paper we (i) propose an accurate method to compute minimum condition number scheme; and (ii) introduce determinant of the information matrix (DIM) as a new optimality metric that scales with number of measurements and does reflect what one would gain from acquiring more measurements. Theoretical analysis shows that DIM is rotation invariant. Evaluations on state-of-the-art encoding schemes proves the relevance and superiority of the proposed metric compared to condition number.

optimality metrics

Condition number

Diffusion tensor imaging

determinant of information matrix

Author

Mohammad Alipoor

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 16-19 April 2015

1945-8452 (eISSN)

462-465

Subject Categories

Medical Engineering

Roots

Basic sciences

Areas of Advance

Life Science Engineering

DOI

10.1109/ISBI.2015.7163911

ISBN

978-1-4799-2374-8

More information

Created

10/8/2017