The Physiological Noise Contribution to Temporal Signal-to-Noise Increases with Decreasing Resolution and Acceleration in Quantitative CMR

Terrence Jao^{1} and Krishna Nayak^{2}

**Data
Acquisition: ** Four healthy
volunteers were scanned on a clinical 3T scanner (GE Signa Excite HD) with an 8
channel cardiac coil. Images were acquired using snapshot b-SSFP during
mid-diastole at a single mid short-axis slice. For each unique set of image acquisition
parameters, twenty images were acquired in two breath-holds. A cardiac CINE was
acquired in each subject to determine the exact timing of mid-diastole as well
as a noise image without RF to calculate the noise covariance matrix. Data were
acquired at various matrix sizes (96/192 x 96/128/192) and accelerations
(1/1.33/1.6/2) with GRAPPA and partial Fourier imaging using TR/TE/FA = 3.5
ms/1.5 ms/50^{0}, BW = 125 Hz, FOV = 300 mm^{2} and a slice
thickness of 10 mm. In a single volunteer, images were acquired at different
inversion times (125, 275, 875, 1725, 3625 ms) to determine the impact of
magnetization preparation on PN and tSNR.

**Noise
Analysis:** Our
analysis of PN and tSNR follow the work of Triantafyllou et. al. in the brain.^{2}
The total noise in a CMR image series is modeled as the
independent sum of TN and PN. Physiological
noise scales proportionally with the MR signal level and tSNR is defined as the
ratio of the MR signal to the total noise. By measuring image SNR and tSNR, the
ratio of PN and TN can be determined with the following relationship:

$$ \frac{PN}{TN}=\sqrt{\left(\frac{SNR}{tSNR}\right)^2-1} $$

**Image Reconstruction:** Images were coil
combined using an optimum B1 weighted combination^{3} from which SNR
maps were calculated. tSNR was measured as the mean intensity of a pixel or ROI
divided by the temporal standard deviation. The Fourier transform scale factor,
effective noise bandwidth, and parallel imaging g-factor (max 1.40) were used
to correct the SNR maps to make them directly comparable with tSNR.^{2,4}
The GRAPPA g-factor was computed using a direct method from the kernel weights
themselves.^{5} The ratio of physiological noise to thermal noise was
subsequently determined from the equation above.

Figure 1 contains a representative SNR,
tSNR, and PN/TN map at a 1.6x1.6 mm^{2} in plane resolution. Figure 2 contains
plots of average SNR, tSNR, and PN/TN within the left ventricular myocardium ROI
as a function of the image matrix size and acceleration factor. SNR
consistently decreased as the image resolution (matrix size) and acceleration
factor (R) increased except in the case of partial Fourier acquisitions (R=1.3,2), which arrive at the k-space center earlier. In contrast, there was no
significant change in tSNR with either the acceleration factor or matrix
size. This caused low resolution images
(96x96) to be PN dominant (PN/TN > 1) regardless of acceleration factor.
However, high resolution images (192x128/192) became SNR starved with
increasing acceleration, which lead to TN dominance (PN/TN<1).

Figure 3 shows the signal level, SNR, tSNR, and PN/TN at different post inversion delays in a single volunteer. Both SNR and tSNR decrease as the signal approaches the null point of myocardium and rise afterwards. Near the signal null at 1 sec, tSNR approaches SNR and the images become thermal noise dominant (PN/TN < 1) because PN is proportional to signal strength.

[1] Salerno et. al. JACC. 2013; 6(7):806-22.

[2] Triantafyllou et. al. Neuroimage 2011; 55(2):597-606

[3] Roemer et. al. MRM. 1990; 16:192-225

[4] Kellman et. al. MRM 2005;54:1439–47

[5] Breuer et. al. MRM 2009; 62(3):739-46

[6] Do. et. al. JCMR 2014; 16(15)

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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