Inversion Recovery (IR) is a powerful tool for contrast manipulation in Mag-
netic Resonance Imaging (MRI). IR can provide strong contrast between tissues with
different values of T1 relaxation times. The tissue magnetization stored at an IR
image pixel can take positive as well as negative values. The corresponding polarity
information is contained in the phase of the complex image. Due to numerous factors
associated with the Magnetic Resonance (MR) scanner and the associated acquisition
system, the acquired complex image is modulated by a spatially varying background
phase which makes the retrieval of polarity information non-trivial. Many commercial
MR scanners perform magnitude-only reconstruction which, due to loss of polarity
information, reduces the dynamic contrast range. Phase sensitive IR (PSIR) can
provide enhanced image contrast by estimating and removing the background phase
and retrieving the correct polarity information. In this thesis, the background phase
of complex MR image is modeled using a statistical model based on Markov Ran-
dom Fields (MRF). Two model optimization methods have been developed. The first
method is a computationally effcient algorithm for finding semi-optimal solutions
satisfying the proposed model. Using an adaptive model neighborhood, it can recon-
struct low SNR images with slow phase variations. The second method presents a
region growing approach which can handle images with rapid phase variations. Ex-
perimental results using computer simulations and in vivo experiments show that the
proposed method is robust and can perform successful reconstruction even in adverse
cases of low signal to noise ratios (SNRs) and high phase variations.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/2574 |
| Date | 01 November 2005 |
| Creators | Garach, Ravindra Mahendrakumar |
| Contributors | Ji, Jim |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis, text |
| Format | 1192990 bytes, electronic, application/pdf, born digital |
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