Generalized matched filtering for time‐resolved MR angiography of pulsatile flow

Wang, Yi; Weber, David M.; Korosec, Frank R.; Mistretta, Charles A.; Grist, Thomas M.; Swan, J. Shannon; Turski, P A

doi:10.1002/mrm.1910300511

Cited by 16 publications

(18 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A simple averaging of the time frames would yield a similar result. However, it has been shown that matched-filtering produces a better combination of time frames for improving SNR (18). The segmentation process keeps the SNR of the unsubtracted matched-filtered image, while it dramatically increases the CNR by suppressing the high static background signal.…”

Section: Discussionmentioning

confidence: 99%

“…The matched-filtered image is a weighted sum of all the time-resolved data (18). For optimal SNR in an arterial image, the weighting assigned to each time frame data depends on the signal intensity within the artery.…”

Section: Snr For Matched-filtering Processmentioning

confidence: 99%

“…Mask subtraction was used to generate a time series of images with moderate SNR and spatial resolution. A single arterial image with high SNR was achieved by forming a matched filter from the same time series but without mask subtrac-tion (18). In this case, the static background and venous signal were suppressed using cross-correlation analysis based on the temporal information (19 -21).…”

mentioning

confidence: 99%

See 2 more Smart Citations

SNR improvement for multiinjection time‐resolved high‐resolution CE‐MRA of the peripheral vasculature

Carroll

Block

et al. 2003

Magnetic Resonance in Med

View full text Add to dashboard Cite

Peripheral MR angiography (MRA) should ideally provide images over a large field of view with high spatial resolution and adequate temporal resolution to accommodate differences in regional filling times. Image subtraction is usually used to remove background signals. In examination protocols involving multiple injections at multiple sites, previously injected contrast present in the mask image provides a substantial decrease in the subtraction image signal. Bolus chase methods avoid this problem but provide limited time for acquisition of high-resolution images at each station. We present here a technique applied to peripheral angiography that provides high spatial and temporal resolution while maintaining high SNR in multiple injection examinations. Undersampled projection imaging was used to increase spatial resolution relative to a previously reported technique using a Cartesian acquisition technique. Late acquisition of high spatial frequencies and temporal matchedfiltering were used to increase spatial resolution and SNR, respectively. Temporal correlation analysis was applied to permit multistation examinations without mask subtraction, thus providing an additional gain in SNR relative to multistation subtraction methods. Quantitative analysis is provided to evaluate the signal and noise behavior in the matched-filtering process due to multiinjection and mask subtraction. Contrast-enhanced MR angiography (CE-MRA) has been more and more widely used for the imaging of the peripheral vasculature. To cover the large peripheral vasculature system, CE-MRA is commonly performed as a multistation exam including the abdominal aorta, the thigh, and calf stations (1-9). The full dose of contrast agent is apportioned among these imaged stations. There are two general approaches to utilize the full T 1 shortening of a bolus of contrast agent. The first approach is the "bolus chase" technique, as several investigators advocate (2-8). In bolus chase MRA, table motion is used to track a single injection of the full contrast agent dose in an attempt to acquire multistation images of the bolus as it flows from the abdominal aorta to the femoral, popliteal, and trifurcation arteries. However, the large interpatient variability of contrast transit times and the short scan time required by the fast contrast flowing speed are problematic for this approach and may lead to a low success rate, as reported by Wang et al. (7) and Yucel and Reid (8). Another approach is to use a time-resolved acquisition to capture the complex and unpredictable flow pattern, especially in patients (9,10). The use of time-resolved exams, such as time-resolved imaging of contrast kinetics (TRICKS), eliminates the need for timing scans or triggering methods and a separate acquisition of a precontrast mask image. The time series of images provides useful information including the delayed contrast arrival and depiction of arterio-venous malformations (11,12).However, the spatial resolution is traded for temporal resolution in the TRICKS technique (13). A...

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Snr For Matched-filtering Processmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

SNR improvement for multiinjection time‐resolved high‐resolution CE‐MRA of the peripheral vasculature

Carroll

Block

et al. 2003

Magnetic Resonance in Med

View full text Add to dashboard Cite

show abstract

“…For example, from the time-series images all mask images and arterial phase images can be summoned into one image of greater vascular detail with high signal-tonoise ratio (SNR) that is particularly useful for presentation in a surgical operation room where video display may not be available. Linear filtering techniques such as the matched filters can be used to produce a summation image (15,16) and have been attempted in time-resolved or dynamic CEMRA to generate a summary arteriogram (17)(18)(19)(20)(21). In practice, the major challenge for summarizing time series images is to identify the contrast bolus arrival and to avoid motion-corrupted mask images and arterial phase images that propagate severe motion artifacts into the final summation image (21).…”

mentioning

confidence: 99%

Automatic selection of mask and arterial phase images for temporally resolved MR digital subtraction angiography

Kim

Prince

Zabih

et al. 2002

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

For time-resolved background-subtracted contrast-enhanced magnetic resonance angiography, the bright and sparse arterial signal allows unique identification of contrast bolus arrival in the arteries. This article presents an automatic filtering algorithm using such arterial characterization for selecting arterial phase images and mask images to generate an optimal summary arteriogram. A paired double-blinded comparison demonstrated that this automatic algorithm is as effective as the manual process.Magn Contrast-enhanced magnetic resonance angiography (CEMRA) has become a routine clinical tool for pretreatment mapping of vasculature (1). Among data acquisition techniques for CEMRA, the time-resolved strategy offers a very useful option for many situations because of the elimination of the cumbersome timing of imaging to the contrast bolus arrival (2-13). The time-resolved CEMRA generates time series images in a manner similar to fluoroscopic X-ray angiography, where image postprocessing has been used frequently to improve vasculature display (14,15). For example, from the time-series images all mask images and arterial phase images can be summoned into one image of greater vascular detail with high signal-tonoise ratio (SNR) that is particularly useful for presentation in a surgical operation room where video display may not be available. Linear filtering techniques such as the matched filters can be used to produce a summation image (15,16) and have been attempted in time-resolved or dynamic CEMRA to generate a summary arteriogram (17-21). In practice, the major challenge for summarizing time series images is to identify the contrast bolus arrival and to avoid motion-corrupted mask images and arterial phase images that propagate severe motion artifacts into the final summation image (21). So far, this avoidance of motioncorrupted images and selection of optimal arterial phase and mask images for summation have been performed through a tedious manual procedure (21).Here we report an algorithm that can fully automate the linear filtering process, i.e., to select the set of arterial phase images and the set of mask images such that the subtracted image from the former to the latter is of the best quality. We describe in detail 1) how to quantify the notion of "image quality," and 2) how to effectively select the mask and arterial phase images based on the quantified measure of quality. POSTPROCESSING TECHNIQUES Problem StatementOur approach is basically an automated version of linear filtering that selects a mask image set and an arterial phase image set to generate a linear filtered image. For a given time series of images (I n , n ϭ 1ϳ n max , n max ϭ 35 -40, the total number of images in the time series), indexed by x and y, we want to select the mask image set (M) and the arterial phase image set (A), such that the subtracted or filtered image (S h ) is of "best quality," where S h is given by:where ͉M͉ and ͉A͉ are the number of mask images and arterial phase images, respectively. Note that all the arithmetic ...

show abstract

“…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)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19). Song et al presented a method to reduce the noise in 3D phase-contrast MRA (10).…”

mentioning

confidence: 99%

Three‐dimensional adaptive filtering in magnetic resonance angiography

Westin

Wigström

Loock

et al. 2001

Magnetic Resonance Imaging

View full text Add to dashboard Cite

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...

show abstract

Generalized matched filtering for time‐resolved MR angiography of pulsatile flow

Cited by 16 publications

References 14 publications

SNR improvement for multiinjection time‐resolved high‐resolution CE‐MRA of the peripheral vasculature

SNR improvement for multiinjection time‐resolved high‐resolution CE‐MRA of the peripheral vasculature

Automatic selection of mask and arterial phase images for temporally resolved MR digital subtraction angiography

Three‐dimensional adaptive filtering in magnetic resonance angiography

Contact Info

Product

Resources

About