KEYWORDS: Tissues, Brain, Cancer, In vivo imaging, Magnetic resonance elastography, Atmospheric modeling, Elastography, Animal model studies, Actuators, Pathology
It is well known that many pathologic processes, like cancer, result in increased tissue
stiffness but the biologic mechanisms which cause pathologies to be stiffer than normal tissues
are largely unknown. Increased collagen density has been presumed to be largely responsible
because it has been shown to cause variations in normal tissue stiffness. However, other effects
such as increased tissue pressure are also thought to be significant. We examined the effects of
tissue pressure on shear modulus measured using MR elastography (MRE) by comparing the
shear modulus in the pre-mortem, edematous and post-mortem porcine brain and found that the
measured shear modulus increases with tissue pressure as expected. The slope of a linear fit to
this preliminary data varied from 0.3 kPa/mmHg to 0.1 kPa/mmHg. These results represent the
first in vivo demonstration of tissue pressure affecting intrinsic mechanical properties and have
several implications. First, if the linear relationship described is correct, tissue pressure could
contribute significantly (~20%) to the increase in stiffness observed in cancer. Second, tissue
pressure effects must be considered when in vitro mechanical properties are extrapolated to in
vivo settings. Moreover, MRE might provide a means to characterize pathologic conditions
associated with increased or decreased tissue pressure, such as edema and ischemia, in a diverse
set of diseases including cancer, diabetes, stroke, and transplant rejection.
KEYWORDS: Tissues, Motion estimation, Scanners, Magnetic resonance imaging, Signal to noise ratio, Motion measurement, Magnetic resonance elastography, Phase measurement, Cancer, Medical imaging
A significant effort has been expended to measure the accuracy of the shear modulus estimates. Conversely, very little effort has been expended to establish the reproducibility of the
method in a clinical context. Previously we established the reproducibility in phantoms to be
3% for repeated measurements without moving the phantom and 5% when the phantom was moved,however, the clinical reproducibility has not been demonstrated. The reproducibility of the method was estimated by scanning subjects' heels repeatedly on a GE 1.5T scanner using previously described methods. Three subjects were scanned three times on different days (termed non-consecutive) and three subjects were scanned three times in the same session without changing the position of the foot (termed consecutive). The average difference between mean values within the field of view for the non-consecutive group was 7.75% ± 3.76% and for the consecutive group it was 5.30% ± 4.16%. These values represent remarkably good reproducibility considering the 20% variation in shear modulus observed within individual heels and the several hundred percent changes observed between normal and pathologic tissues. The variation in repeated examinations was caused by four factors: positioning error between examinations accounted for 4.8%, computational noise 3.0%, and the combination of MR noise and patient motion during the examination, 5.3%. Each of these sources of variation can be reduced in relatively straightforward ways if necessary but the current level of reproducibility is sufficient for most current applications.
KEYWORDS: Magnetic resonance elastography, Monte Carlo methods, Motion estimation, Elastography, Error analysis, Data centers, Wave propagation, Medical imaging, Motion analysis, Motion models
We have developed a one-dimensional route-finding phase unwrapping method to handle Magnetic Resonance
Elastography (MRE) phase data from very large induced motion. The method is able to unwrap data where the adjacent
phase differences are within range [-2π, 2π) as opposed to the [-π, π) requirement for most phase unwrapping methods.
With more unwrapping paths, the range of phase difference can be easily expanded to [-2Nπ, 2Nπ) (N = 1,2,3,...). The
possible unwrapping path when using different numbers of relative phase offsets can be found by Monte Carlo
simulation. Two phantom studies were performed to test the new unwrapping method. One study compared the new
method with the classical Itoh's one-dimensional method and the other study combined the new method with twodimensional
phase unwrapping method and unwraped three-dimensional MRE phase data with a sequential threedimensional unwrapping approach. The results from using different phase unwrapping methods were then compared and analyzed.
Motion estimation is an essential processing step common to all Magnetic Resonance Elastography (MRE) methods. For dynamic techniques, the motion is obtained from a sinusoidal fit of the image phase at multiple, uniformly spaced relative phase offsets, φ, between the motion and the motion encoding gradients (MEGs). Generally, 4 to 8 uniformly spaced values of φ are used. We introduce a method, termed RME (reduced motion encodes), of reducing the number of relative phases required, thereby reducing the imaging time for an MRE acquisition. A frequency-domain algorithm was implemented using the Discrete Fourier Transform (DFT) to derive the general least-squares solution for the motion amplitude and phase given an arbitrary number of phase offsets. Simulation result shows that the noise level decreases as the number of φ increases. The decrease is largest when smaller numbers of φ are used and becomes less significant as the number increases. The minimum noise is obtained for a specific number, n, of φ when the phase is evenly distributed with interval π/n. Phantom studies show a similar trend with noise level. The resulting displacement images from different numbers of phase offsets are compared.
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