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Contents
Contents
Introduction
The intrinsic MR signal
Excitation and relaxation
Image formation
Signal and noise in MRI
Introduction
The MR signal
Imaging parameters
The noise
Thermal noise
Sample losses
Imaging parameters
Structured noise
Probability density functions of MR data
Introduction
PDF of raw MR data
PDF of magnitude data
The Rice distribution
The generalized Rice distribution
PDF of phase data
Noise estimation
Introduction
Parameter estimation
Minimum Variance Bound
Maximum Likelihood estimation
Noise estimation
Single image methods
Double image method
Experiments and discussion
Conclusions
Signal estimation
Introduction
Signal estimation from Rice distributed data
Conventional approach
Maximum Likelihood estimation
Experiments and discussion
Signal estimation from PCMR data
Methods
Experiments and discussion
Combined signal and noise estimation
Conclusions
Application: T
1
and T
2
estimation
Introduction
Method
Magnitude data PDF
Errors introduced in T
1
and T
2
estimation
Maximum Likelihood estimation
Experiments and Discussion
Simulation experiments
T
1
-map estimation
T
2
-map estimation
Conclusions
SNR estimation
Introduction
Single image methods
The NEMA standard
Cross correlation method
Theory
Experiment
Results and discussion
SNR Improvement
Introduction
Time averaging
Spatial averaging
Cross correlation
Anisotropic adaptive diffusion filter
Conclusions
3D Image Segmentation
Introduction
3D semi-interactive segmentation
Immersion-based watershed algorithm
Reduction of oversegmentation
Interactive segmentation
Experiments and discussion
Materials
Phantom object data
Mouse data
Applications
Fragile X knockout mice
Canary brain
Conclusions
Conclusions
Bibliography
The generalized Rice PDF
PDF of one PCMR pixel variable
PDF of the average of squared PCMR pixel variables
List of symbols and abbreviations
Greek symbols
Roman symbols
Abbreviations
Nederlandse samenvatting
Personalia
Curriculum Vitae
List of publications
Journal papers
Conference Proceedings
Jan Sijbers
1999-01-04