2011
DOI: 10.1118/1.3671934
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High-quality multiple T2(*) contrast MR images from low-quality multi-echo images using temporal-domain denoising methods

Abstract: This study demonstrated that denoising methods in the temporal-domain can effectively suppress noise in the spatial domain, and increase signal-to-noise ratio (SNR) for each image of different T(2)(*) weights at multiple time points, resulting in multiple high-quality T(2)(*) contrast images.

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Cited by 6 publications
(5 citation statements)
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“…To further improve the image quality, we applied a model‐based temporal domain denoising method to both the magnitude and phase image . This denoising procedure was especially needed for the late echo data because the late echo images have low SNR.…”
Section: Methodsmentioning
confidence: 99%
“…To further improve the image quality, we applied a model‐based temporal domain denoising method to both the magnitude and phase image . This denoising procedure was especially needed for the late echo data because the late echo images have low SNR.…”
Section: Methodsmentioning
confidence: 99%
“…Three different regularization methods have been compared using three main criteria (visual quality, CoV and ability to distinguish between MS patients and normals), and it has been observed that the proposed nlsrNNLS algorithm compares favorably with the other methods. Future work would include further optimization of algorithmic parameters, validation beyond a single scanner, longitudinal/ROIs studies (including lesions), scan-to-scan variability, and investigation of how well nlsrNNLS would work in combination with denoising strategies applied to the multi-echo images [4,5].…”
Section: Resultsmentioning
confidence: 99%
“…This causes the technique to work well only on data of high SNR; unfortunately, this is often not a practical assumption as T 2 relaxation data tend to be noisy. To improve the robustness of MWF computation, applying various denoising methods to the multi-echo images have been studied [2,3,4,5]. Regularization approaches for NNLS have been also studied, ranging from the conventional Tikhonov regularization [6,7], which is non-spatial, to smoothing methods that work by averaging the neighboring spectra to constrain the NNLS of a given voxel [8].…”
Section: Introductionmentioning
confidence: 99%
“…A healthy brain was scanned with a multi-echo gradient-recalled-echo (MGRE) sequence [1] using a 3T Siemens MRI system (Erlangen, Germany). Images were acquired at 16 echoes.…”
Section: Methodsmentioning
confidence: 99%