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.
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.
Authors: D R Hadizadeh; G M Kukuk; D T Steck; J Gieseke; H Urbach; H J Tschampa; S Greschus; A Kovàcs; M Möhlenbruch; A Bostroem; H H Schild; W A Willinek Journal: AJNR Am J Neuroradiol Date: 2012-02-02 Impact factor: 3.825
Authors: Michael Jeserich; Bela Merkely; Pascal Schlosser; Simone Kimmel; Gabor Pavlik; Stephan Achenbach Journal: MAGMA Date: 2017-01-16 Impact factor: 2.310
Authors: Matthias C Burg; Alexander C Bunck; Harald Seifarth; Boris Buerke; Harald Kugel; Volker Hesselmann; Michael Köhler; Walter Heindel; David Maintz Journal: Radiol Res Pract Date: 2010-07-27