Rationale and Objectives
To evaluate objective image quality parameters for contrast-enhanced magnetic resonance angiography (CE-MRA), contrast-to-noise (CNR), and signal-to-noise ratio (SNR) calculations based on signal intensity (SI) and standard deviation (SD) measurements of the vessel, the surrounding tissue (eg, muscle), and the background noise outside the body are commonly used. However, modern magnetic resonance scanners often use dedicated software algorithms such as Constant LEvel AppeaRance (CLEAR) to improve image quality, which may affect the established methods of SNR and CNR calculation. The purpose of this study was to intraindividually evaluate the feasibility of conventional techniques used for SNR and CNR calculation of MRA data sets that have been reconstructed with both, a standard (non-CLEAR) and a CLEAR algorithm.
Methods
Supra-aortic high-resolution CE-MRA of 11 patients with headache symptoms was performed at 1.5 T using reconstruction algorithms generating both, non-CLEAR and CLEAR-corrected images from the acquired data set. A qualitative analysis with regard to image quality and contrast level was performed by two radiologists applying a score system. For quantitative analysis, distribution of SI values was measured in regions of interest in the common carotid artery (CCA) and the C1 segment of the internal carotid artery in identical positions of both data sets for intraindividual comparison of SNR and CNR calculations. For that purpose, three different equations were used for background noise assessment by determining the SD of SIs measured in the air outside the body (Eq. A), the soft tissue adjacent to the analyzed vessel segment (Eq. B), and in a contrast-medium filled tube (reference standard), which was placed around the patient’s neck (Eq. C).
Results
The qualitative analysis documented an improved image quality and a higher contrast level for CLEAR-based data sets. SNR and CNR calculations of the CCA and the C1 segment were significantly different for both reconstruction algorithms when using the background noise outside the body for image noise assessment ( P < .05 [CCA]; P < .05 [C1]). SNR and CNR calculations based on the soft tissue adjacent to the analyzed segment or a reference standard were comparable.
Conclusions
For comparative analysis of CE-MRA data sets, SNR and CNR calculations based on SD determination of the background noise signal measured outside the body are not applicable for CE-MRA data sets reconstructed with a CLEAR-based algorithm. Therefore, noise should rather be assessed in the perivascular tissue to enable proper comparative analysis of CLEAR-enhanced CE-MRA data sets.
Contrast-enhanced magnetic resonance angiography (CE-MRA) is widely used for the detection of vessel pathologies in clinical routine settings. The advantages of CE-MRA (eg, the lower nephrotoxicity of contrast agents in approved doses, compared iodinated contrast agent, and the lack of x-ray exposure) have been described elsewhere ( ).
Recently, the image quality of CE-MRA has been improved by technical innovations such as faster gradients and parallel imaging techniques, enabling high-resolution MRA with voxel sizes <1 μL ( ). Additionally, new software algorithms for data processing have been developed to further improve image quality. One of these data modification algorithms is called Constant LEvel AppeaRance (CLEAR; Philips Medical Systems, Best, The Netherlands); it was initially developed to reduce signal inhomogeneities due to the use of surface coil arrays for parallel acquisition techniques (eg, SENSitivity Encoding [SENSE]) ( ). Nevertheless, this algorithm can also be used to improve image quality of data sets acquired with phased-array coils without parallel imaging. Visually, CLEAR corrected data sets can be identified by a thin shadow-like area parallel to the outline of the body surface and a homogenous dark background ( Fig. 1 ).
Open full size image
Figure 1
Region of interest positioning projected on a total volume maximum intension projection. Signal intensity was measured in the common carotid artery, the C1 segment of the internal carotid artery, the soft tissue adjacent to the analyzed segment, the background noise outside the body, and the standard (contrast medium filled plastic tube) on corresponding slice positions of the Constant LEvel AppeaRance (CLEAR) and non-CLEAR reconstructed data sets for both sides separately.
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Materials and methods
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Qualitative Analysis
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Quantitative Analysis
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Table 1
Mean Signal Intensity (average over patients and sides) of the Common Carotid Artery (CCA) and the C1 Segment of the Internal Carotid Artery, the Perivascular Tissue, the Reference Standard, and the Air Outside the Body for Both Image Reconstructions (Constant LEvel AppeaRance [CLEAR] and Non-CLEAR)
Signal Intensity Values (mean ± SD over all patients) CLEAR Non-CLEAR CCA Vessel 2,332.5 ± 411.5 2,241.5 ± 448.1 Tissue 250.4 ± 59.7 270.9 ± 60.2 Reference standard 1,369.9 ± 307.8 1,121.1 ± 262.4 Air 25.4 ± 23.8 144.5 ± 29.6 C1 Vessel 2,267.5 ± 414.1 1,999.6 ± 325.1 Tissue 251.5 ± 54.8 232.5 ± 33.1 Reference standard 1,363.5 ± 277.1 1,121.3 ± 256.8 Air 30.9 ± 30.6 138.5 ± 27.9
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Table 2
Signal-to-Noise Ratio (SNR) and Contrast-to-Noise (CNR) Calculation
Reference SNR CNR (A) SI Vessel /SD Air (SI Vessel − SI Tissue )/SD Air (B) SI Vessel /SD Tissue (SI Vessel − SI Tissue )/SD Tissue (C) SI Vessel /SD Reference standard (SI Vessel − SI Tissue )/SD Reference standard
Mean signal intensity (SI) and its standard deviation (SD) were used to calculate SNR and CNR of the CCA and C1 segment of the internal carotid artery, separately for each patient and side with three equations, using different representation of noise: (A) background noise outside the body (air), (B) SI variation (SD) of soft tissue adjacent to the analyzed segment (tissue), and (C) SI variation (SD) of the reference standard.
Table 3
Signal-to-Noise Ratio (SNR) and Contrast-to-Noise (CNR) (mean ± standard deviation, averaged over patients and sides) of the Common Coronary Artery (CCA) and the C1 Segment of the Internal Carotid Artery for Both Data Sets (Constant LEvel AppeaRance [CLEAR] and Non-CLEAR), Using Different Representations of Noise
Vessel Reference SNR CNR CLEAR Non-CLEAR CLEAR Non-CLEAR CCA A 167.5 ± 164.3 35.2 ± 8.9 161.8 ± 153.9 25.9 ± 8.6 B 18.9 ± 5.4 20.5 ± 6.9 15.9 ± 5.6 17.8 ± 6.6 C 21.4 ± 7.8 27.5 ± 9.8 17.0 ± 7.2 21.7 ± 9.7 C1 A 155.9 ± 143.1 27.1 ± 10.1 129.9 ± 123.9 24.9 ± 5,5 B 18.7 ± 7.3 18.2 ± 4.5 15.2 ± 6.2 14.7 ± 5.0 C 19.5 ± 9.3 20.2 ± 11.5 16.2 ± 7.7 17.5 ± 9.6
(A) Background noise outside the body, (B) signal intensity variation (SD) of soft tissue adjacent to the analyzed segment, and (C) signal intensity variation (SD) of reference standard.
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Statistical Analysis
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Results
Qualitative Analysis
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![Figure 2, Intraindividual comparison of total volume maximum intension projections of contrast-enhanced magnetic resonance angiography data sets of the supra-aortic arteries with a standard (non-Constant LEvel AppeaRance [CLEAR]) ( a ) and a CLEAR ( b ) based reconstruction algorithm. The maximum intension projection of the CLEAR-based reconstructed data set shows a typical faint silhouette line parallel to the body surface, indicating that the algorithm increases the inner body contrast level. For standardization, identical contrast medium–filled plastic lines (gadobutrol/saline: 1:400) were positioned around the neck and served as a reference standard.](https://storage.googleapis.com/dl.dentistrykey.com/clinical/QualitativeandQuantitativeAnalysisofRoutinelyPostprocessedCLEARCEMRADataSets/0_1s20S1076633208002158.jpg)
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Quantitative Analysis
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Discussion
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Conclusion
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