Venous signal suppression in 3D dynamic Gd‐enhanced carotid artery imaging using the eigenimage filter

Kaandorp, DW Dave; Kopinga, K.; Wijn, Pff Pieter

doi:10.1002/(sici)1522-2594(199908)42:2<307::aid-mrm13>3.0.co;2-#

Cited by 10 publications

(12 citation statements)

References 17 publications

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“…Double subtraction improves the CNR of venograms, but still cannot completely resolve this problem. Other techniques, such as eigenimage filtering, factor analysis, and correlation analysis, can generate higher‐quality venograms (16, 17); however, these techniques require a longer postprocessing time. In addition, they generate only a single venous image without contrast dynamics in the venous system.…”

Section: Discussionmentioning

confidence: 99%

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Dynamic MR venography: An intrinsic benefit of time‐resolved MR angiography

Thornton

Mistretta

et al. 2006

Magnetic Resonance Imaging

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Purpose:To investigate the possibility of obtaining dynamic contrast-enhanced magnetic resonance venography (DCE-MRV) images of the lower extremities. Materials and Methods:Peripheral contrast-enhanced magnetic resonance angiography (CE-MRA) was performed on 20 patients using a time-resolved sequence that combined undersampled projection reconstruction (PR) inplane and Cartesian slice encoding through-plane. The contrast dynamics of distal vessels were depicted. An automated segmentation algorithm based on a contrast arrival time (CAT) threshold was used to generate contrast dynamics in the venous system. The signal difference between the vein and artery was measured to evaluate the effectiveness of this technique in isolating the venous contrast dynamics. Results:The automatically generated image series depicted the contrast dynamics of both the arterial and venous systems, including asymmetric venous enhancement and background tissue enhancement. Quantitative measurement showed a mean venous/arterial signal ratio increase from 1.58 to 4.82 for the peak venous frame after arterial signal suppression. Conclusion: DCE-MRV is a minimally invasive techniquefor evaluating the venous side of the systemic vascular anatomy. Time-resolved MRA has the potential clinical benefit of enabling both arterial and venous disease to be detected in patients undergoing CE-MRA.

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Section: Discussionmentioning

confidence: 99%

“…Higher temporal resolution is desirable to better depict the contrast dynamics in both the arterial and venous systems. This may also help with image processing, such as eigenimage filtering and correlation analysis (16, 17).…”

mentioning

confidence: 99%

Dynamic MR venography: An intrinsic benefit of time‐resolved MR angiography

Thornton

Mistretta

et al. 2006

Magnetic Resonance Imaging

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“…Various postprocessing methods for removing arterial signal from venous images (and venous signal from arterial images) have been proposed. These fall into two categories: dynamic techniques that are based on differences in the time at which arterial and venous vessels enhance (1–5) and, more recently, static techniques where image processing methods are used to track the vessels in the image volume (6, 7). The advantage of the static techniques is that they can be applied to a single image volume; however, they also have several drawbacks.…”

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confidence: 99%

Separating arterial and venous components from 3D dynamic contrast‐enhanced MRI studies using factor analysis

Martel¹,

Fraser²,

Delay³

et al. 2003

Magnetic Resonance in Med

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Dynamic contrast-enhanced MRI has been used extensively for angiography but in order to generate separate arterial and venous images some form of postprocessing is required. This typically involves the subtraction of one image in a dynamic sequence from another in order to suppress unwanted signal; however, this also has the effect of decreasing the signal-to-noise ratio (SNR) of the image. In this study, factor analysis, a technique related to eigenimage filtering, is used to separate arterial and venous components from dynamic contrast-enhanced images of the legs acquired with a temporal resolution of 30 sec. The SNR of the venous and arterial images extracted from a series of 20 patients using conventional single subtraction, a double subtraction method, and factor analysis were compared. Results show that the use of factor analysis improved the SNR in the venous images by a factor of 2.3 compared with the use of simple subtraction. A subjective comparison of the maximum intensity projection images generated from the venous images was also carried out and showed a significant preference for those generated using factor analysis over those generated using other subtraction methods. Magn Reson Med 49:928 -933, 2003.

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“…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).…”

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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

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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 ...

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Venous signal suppression in 3D dynamic Gd‐enhanced carotid artery imaging using the eigenimage filter

Cited by 10 publications

References 17 publications

Dynamic MR venography: An intrinsic benefit of time‐resolved MR angiography

Dynamic MR venography: An intrinsic benefit of time‐resolved MR angiography

Separating arterial and venous components from 3D dynamic contrast‐enhanced MRI studies using factor analysis

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

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