A conventional 3D phase contrast acquisition generates images with good spatial resolution, but often gives rise to artifacts due to pulsatile flow. 2D cine phase contrast, on the other hand, can register dynamic flow, but has a poor spatial resolution perpendicular to the imaging plane. A combination of both high spatial and temporal resolution may be advantageous in some cases, both in quantitative flow measurements and in MR angiography. The described 3D cine phase contrast pulse sequence creates a temporally resolved series of 3D data sets with velocity encoded data.
In order to enhance 3D image data from magnetic resonance angiography (MRA), a novel method based on the theory of multidimensional adaptive filtering has been developed. The purpose of the technique is to suppress image noise while enhancing important structures. The method is based on local structure estimation using six 3D orientation selective filters, followed by an adaptive filtering step controlled by the local structure information. The complete filtering procedure requires approximately 3 minutes of computational time on a standard workstation for a 256 ؋ 256 ؋ 64 data set. The method has been evaluated using a mathematical vessel model and in vivo MRA data (both phase contrast and time of flight (TOF)). 3D adaptive filtering results in a better delineation of small blood vessels and efficiently reduces the high-frequency noise. Depending on the data acquisition and the original data type, contrast-to-noise ratio (CNR) improvements of up to 179% (8.9 dB) were observed. 3D adaptive filtering may provide an alternative to prolonging the scan time or using contrast agents in MRA when the CNR is low. J. Index terms: angiography; adaptive filtering; noise reduction; tensor; image enhancement MOST MAGNETIC RESONANCE ANGIOGRAPHY (MRA) techniques produce a 3D data set, which is processed to selectively display the vasculature of interest. Angiograms based on both phase contrast (1) and time of flight (TOF) (2) can be reconstructed from the 3D data sets using maximum intensity projection (MIP), which creates a 2D image by recording the intensity of the brightest voxel along each projection ray through the 3D volume (3). The MIP technique is sensitive to highintensity values that tend to obscure regions of slow or recirculating flow, is unable to separately identify arterial and venous structures within the same data, and cannot discriminate high-intensity artifacts from true vessel signal (4). A high-resolution data set acquired while maintaining a short scan time often has a noise level high enough to obscure important diagnostic information in the angiograms. Several methods for reduction of noise in MRA data have been published (5-19). Song et al. presented a method to reduce the noise in 3D phase-contrast MRA (10). The method exploits the fact that the velocity field is divergence-free. The noise contribution, which is not divergence-free, can be removed using a projection operator method in Hilbert space. Du and coworkers showed that 3D TOF images could be improved by using a nonlinear second difference spatial filtering technique (11). Comparison with a Laplacian filtering technique demonstrated that the contrast-to-noise ratio (CNR) increased when the nonlinear second difference spatial method was employed. Reducing noise without blurring the lines and edges encourages intraregion smoothing in preference to smoothing across regions. Perona and Malik developed a multiscale smoothing and edge detection scheme, mathematically formulated as an anisotropic diffusion process that effectively performed intraregi...
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