Proc. SPIE. 6515, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment
KEYWORDS: Target detection, Signal to noise ratio, Detection and tracking algorithms, Data modeling, Magnetic resonance imaging, Image processing, Wavelets, Interference (communication), Reconstruction algorithms, Signal detection
Some diagnostic tasks in MRI involve determining the presence of a faint feature (target) relative to a dark
background. In MR images produced by taking pixel magnitudes it is well known that the contrast between faint
features and dark backgrounds is reduced due to the Rician noise distribution. In an attempt to enhance detection
we implemented three different MRI reconstruction algorithms: the normal magnitude, phase-corrected real, and
a wavelet thresholding algorithm designed particularly for MRI noise suppression and contrast enhancement.
To compare these reconstructions, we had volunteers perform a two-alternative forced choice (2AFC) signal
detection task. The stimuli were produced from high-field head MRI images with synthetic thermal noise added
to ensure realistic backgrounds. Circular targets were located in regions of the image that were dark, but
next to bright anatomy. Images were processed using one of the three reconstruction techniques. In addition
we compared a channelized Hotelling observer (CHO) to the human observers in this task. We measured the
percentage correct in both the human and model observer experiments.
Our results showed better performance with the use of magnitude or phase-corrected real images compared
to the use of the wavelet algorithm. In particular, artifacts induced by the wavelet algorithm seem to distract
some users and produce significant inter-subject variability. This contradicts predictions based only on SNR.
The CHO matched the mean human results quite closely, demonstrating that this model observer may be used
to simulate human response in MRI target detection tasks.
Proc. SPIE. 6142, Medical Imaging 2006: Physics of Medical Imaging
KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Error analysis, Image restoration, Interference (communication), Image analysis, Data acquisition, Tellurium, Commercial off the shelf technology, Phase shifts
Signal from fat is normally removed from MR images either by fat separation techniques that distinguish water from fat signal after the data has been received, or by fat suppression techniques that prevent the fat signal from being received. Most approaches to fat separation are variations on Dixon imaging. The primary downside to Dixon imaging is the requirement for multiple images with stationary anatomy, often with specific TEs. An alternate approach is to take only one image, estimate phase errors to correct for inhomogeneity or other effects, and then separate the water and fat using the known phase shift. This has shown promise in previously published work, but the water and fat signals were always perpendicular, requiring a fixed TE. We consider the possibility of separation from a single, phase-corrected image with an arbitrary angle between water and fat signals. We note that a change of basis will separate water and fat signals into two images with additive zero-mean Gaussian noise. However, as the angle between water and fat nears pi or 0, the noise power in the separated images increases rapidly. We discuss techniques for reducing this noise magnification.
If the phase error at each pixel in a complex-valued MRI image is known the noise in the image can be reduced resulting in improved detection of medically significant details. However, given a complex-valued MRI image, estimating the phase error at each pixel is a difficult problem. Several approaches have previously been suggested including non-linear least squares fitting and smoothing filters. We propose a new scheme based on iteratively applying a series of non-linear filters, each used to modify the estimate into greater agreement with one piece of knowledge about the problem, until the output converges to a stable estimate. We compare our results with other phase estimation and MRI denoising schemes using synthetic data.