In this work, we have described a fast and easy-to-use method for quantifying SNR on coronary MRA images acquired with SENSE acceleration. This fast SNRnoRF method has been characterized and validated in phantom studies in comparison to a reference standard method SNRmult. Good agreement of measured noise statistics and SNR behavior between the two methods was observed both qualitatively and quantitatively. The capability of SNRnoRF in measuring spatially dependent SNR was also demonstrated with in vivo studies.
method consists of a noise image that is acquired without RF pulses and magnetic field gradients. This is enabled by the linearity of parallel imaging reconstruction methods. As a result, the reconstruction process can be regarded as an operator that acts on both signal and noise separately (15
). A noise image acquired with SNRnoRF
offers several remarkable advantages when compared to a noise image that is obtained through the acquisition of serial identical anatomical images. First, the noise scan can be acquired orders of magnitude faster, which makes it perfectly well suited for lengthy volumetric coronary MRA scans. In practice, a noise scan can be completed within 30s for a 3D coronary MRA scan that takes 10 minutes to acquire. In the current implementation, one order of magnitude faster scan duration was achieved by removing most of the magnetization preparation pulses and the waiting period between the acquisition windows. However, during each TR, only the time for signal sampling is theoretically required to fill the noise k
-space. This suggests that the 30s noise scan may be further abbreviated to 4s only. Secondly, without MR signal from the object, the noise images are free from artifacts induced by motion or physiological fluctuation. This important feature permits SNRnoRF
to be computed for quantitative cardiac and time resolved contrast enhanced imaging, which cannot easily be performed using the multiple acquisitions method. The SNR for each repetition of the anatomical scan is determined by the signal measured in that repetition and the noise extracted from the noise scan. Should the anatomical scan require breath holding as a mechanism to suppress respiratory motion artifacts, the breath-hold duration does not have to be prolonged by the acquisition of the noise scan since it may be acquired during free breathing as long as the coil loading remains similar. Moreover, the SNRmult
method may be adversely affected by misregistration originating from object motion during the time course of data acquisition. This can be avoided with the SNRnoRF
technique since a noise image is no longer contaminated by motion artifacts. In addition, the noise acquisition comes at no extra costs such as preparatory steps, extra setup time or offline processing. Finally, measurement of contrast-to-noise ratio (CNR) is based on local signal and noise measurements, and therefore can be obtained conveniently in the same manner as SNRnoRF
. All these favorable properties may make the SNRnoRF
method useful to be adopted in routine clinical scanning where quantitative analyses or systematic protocol optimization steps, and quantitative end-points related to image quality are essential.
An assumption underlying SNRnoRF is that all pixels in the user selected ROI have the same statistical noise distribution. SENSE reconstruction leads to inhomogeneous noise across the image. Since SNRnoRF is a ROI based measurement, its sensitivity to spatial variations is reduced when compared to SNRmult. However, the noise statistics change slowly as a function of the location and can be considered regionally homogeneous as long as the g-factor remains low. In our phantom study, mean g-factors were lower than 2 and their standard deviation were less than 0.5 as long as the SENSE acceleration factor remained below 5 (see ) with the current coil setup. For these moderate acceleration factors, the average relative deviations of SNRnoRF from SNRmult are below 10.1% (see ). However, when the SENSE acceleration factor is 5, more coherent geometrical structures of noise amplification were observed. These might be responsible for the relatively sharp increase in relative SNR deviations for higher R values, especially if larger ROIs are used. In practice, adequate SENSE acceleration factors are usually chosen which are well supported by the coil arrangement. A g-factor beyond 2 is most commonly avoided since severe image degradation due to incomplete SENSE unfolding may occur. Therefore, over a reasonable range of g-factors and SENSE acceleration factors, the SNRnoRF method is reliable and fast for the quantification of SNR from homogeneous regions.
The ROI position and size should also be considered to ensure that the noise distribution in the ROI is sufficiently homogeneous. ROI positions in areas of fold over, or other kinds of artifacts on the anatomical image should be avoided. In practice, the ROI size is also limited by the homogeneity of the target area on the anatomical image and the noise statistics at that same location. In homogeneous regions, larger ROI sizes contribute to measurement precision, because the error in NnoRF
is proportional to
, where nROI
is the number of independent pixels in the ROI. With SENSE acceleration factors 1 to 4, noise amplification and local variation are moderate. Thus enlarging ROI and including more samples contribute to reduce the relative error of SNRnoRF
relative to the SNRmult
method. Our studies have demonstrated that the SNRnoRF
method performed consistently well over a range of SNR levels.
In general, all previous knowledge about ROI based SNR measurement can be readily applied with the SNRnoRF
method. For example, the magnitude bias correction can be applied to reduce the overall error of the signal estimation at low SNR (28
Our measurements in vivo
confirm that SNRnoRF
is well-suited to measure spatially dependent SNR on MR images acquired with SENSE. The relative signals obtained in our study show that the myocardium has ~30–40% higher signal intensity than skeletal muscle. These results agree well with values in normal subjects in the literature (29
) and may be attributable to shorter T2 that has been reported of chest muscle. However, the noise power in the myocardium which is located more distant from the signal receive surface coils is elevated compared to the noise in the skeletal muscle which is much closer to the coils (). Therefore, despite the higher signal intensity of myocardium, the SNR of myocardium is reduced when compared to skeletal muscle. One may notice that SNR obtained with a SENSE acceleration factor of 2 is higher than that with factor of 1 for both cardiac and skeletal muscles. As previously reported, this is attributable to a longer TR, a lower receiver bandwidth and an improved RF transmit to receive duty cycle used in the sequence with a SENSE acceleration factor of 2 (26
In the current study, experiments were performed with a specific coil, pulse sequence and parallel imaging technique. However, by compacting the reconstruction processes into a “black box” as available on most commercial MRI systems, the SNRnoRF
method is expected to be applicable with other coils, signal acquisition techniques, k
-space sampling schemes or trajectories, image reconstruction methods, and linear processing associated with coronary MRA. Therefore, the combination with balanced steady state free precession (bSSFP) (12
) or spin echo pulse sequences (33
) seems straightforward but remains to be explored. For parallel imaging approaches that rely on calibrating k
-space lines that are acquired along with the normal image acquisition, such as generalized autocalibrating partially parallel acquisitions (GRAPPA) (8
) and modified SENSE (34
) reconstruction, the current method may not directly be applied and modifications of the reconstruction algorithm may be needed. However, if the “black-box” contains any nonlinear filtering, which may violate the condition of identical reconstruction for both the anatomical and the noise image, the proposed technique should not be used.
In conclusion, the SNRnoRF method demonstrated here is a practical and easy-to-use method to quantify SNR on coronary MRA when parallel imaging is employed. It allows SNR measurement in any user specified ROI on the image with very little extra cost in scanning time and no extra off-line image processing or analysis is required. The proposed method is also not jeopardized by misregistration in contrast to those approaches that depend on repeated image acquisitions. One limitation of SNRnoRF, like any other ROI based measurement, is its reduced sensitivity to spatial variation of noise when compared to the gold standard SNRmult. SNRnoRF quantification may not be well-suited for high acceleration factors and non-linear filters during reconstruction or post processing. In general, the proposed methodology will provide a useful tool for objective and quantitative evaluation of coronary MRA acquired with different pulse sequences, MRI platforms, coil arrays, and reconstruction methods and may also find applications outside of coronary imaging.