Empirical noise mapping guides optimal region‐of‐interest selection for accurate noise estimation in prostate DWI

Vollständiger Abstract

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Abstract Background Signal‐to‐noise ratio (SNR) measurements are important for quality assurance, especially when quantitative MRI markers or new hardware are being developed or evaluated. Regions of low signal or assumed uniform signal are used for SNR estimation in a variety of MR image types, but this is unreliable due to spatial heterogeneity of noise arising from the organization of multiple‐detector arrays and sensitivity encoding (SENSE) reconstruction. Purpose We present a universally applicable method for empirical noise mapping by propagating simulated noise through the image‐generating reconstruction pipeline, to identify optimal regions of interest (ROIs) for determining noise levels in prostate diffusion‐weighted imaging (DWI). Methods Thirty‐six men were imaged at 3T with DWI. K‐space data were propagated through an off‐line reconstruction pipeline including sensitivity encoding (SENSE), to generate DW images. Simulated complex Gaussian noise generated from prescan per‐channel measurements was similarly processed to produce pure noise maps. Per‐voxel standard deviations (SD) of spatial distributions of noise, averaged over prostate, internal obturator muscle (IOM), low signal (anterior and posterior), rectum, and bladder ROIs were calculated and compared to evaluate suitability for SNR estimation in prostate DWI. Results IOM ROI noise levels, averaged bilaterally, were not significantly different from those in the prostate. Bladder ROI noise levels were comparable. Noise levels measured in low signal areas were highly dependent on SENSE algorithm regularization strength. Discussion The proposed method can be used—on any scanner—to accurately calculate noise levels in MR images, without repeated acquisition or interference from SENSE use. Conclusion Bladder and averaged IOM ROIs are appropriate for estimating DWI noise levels in the prostate. Low signal ROIs should not be used, due to the strong dependence on the SENSE regularization strength, and by extension, on the SENSE reconstruction algorithm. The presented method can be used instead to obtain accurate spatial maps of noise levels.