1,171 research outputs found
Joint Total Variation ESTATICS for Robust Multi-Parameter Mapping
Quantitative magnetic resonance imaging (qMRI) derives tissue-specific
parameters -- such as the apparent transverse relaxation rate R2*, the
longitudinal relaxation rate R1 and the magnetisation transfer saturation --
that can be compared across sites and scanners and carry important information
about the underlying microstructure. The multi-parameter mapping (MPM) protocol
takes advantage of multi-echo acquisitions with variable flip angles to extract
these parameters in a clinically acceptable scan time. In this context,
ESTATICS performs a joint loglinear fit of multiple echo series to extract R2*
and multiple extrapolated intercepts, thereby improving robustness to motion
and decreasing the variance of the estimators. In this paper, we extend this
model in two ways: (1) by introducing a joint total variation (JTV) prior on
the intercepts and decay, and (2) by deriving a nonlinear maximum \emph{a
posteriori} estimate. We evaluated the proposed algorithm by predicting
left-out echoes in a rich single-subject dataset. In this validation, we
outperformed other state-of-the-art methods and additionally showed that the
proposed approach greatly reduces the variance of the estimated maps, without
introducing bias.Comment: 11 pages, 2 figures, 1 table, conference paper, accepted at MICCAI
202
Patch-based anisotropic diffusion scheme for fluorescence diffuse optical tomography-part 1: technical principles
Fluorescence diffuse optical tomography (fDOT) provides 3D images of fluorescence distributions in biological tissue, which represent molecular and cellular processes. The image reconstruction problem is highly ill-posed and requires regularisation techniques to stabilise and find meaningful solutions. Quadratic regularisation tends to either oversmooth or generate very noisy reconstructions, depending on the regularisation strength. Edge preserving methods, such as anisotropic diffusion regularisation (AD), can preserve important features in the fluorescence image and smooth out noise. However, AD has limited ability to distinguish an edge from noise. In this two-part paper, we propose a patch-based anisotropic diffusion regularisation (PAD), where regularisation strength is determined by a weighted average according to the similarity between patches around voxels within a search window, instead of a simple local neighbourhood strategy. However, this method has higher computational complexity and, hence, we wavelet compress the patches (PAD-WT) to speed it up, while simultaneously taking advantage of the denoising properties of wavelet thresholding. The proposed method combines the nonlocal means (NLM), AD and wavelet shrinkage methods, which are image processing methods. Therefore, in this first paper, we used a denoising test problem to analyse the performance of the new method. Our results show that the proposed PAD-WT method provides better results than the AD or NLM methods alone. The efficacy of the method for fDOT image reconstruction problem is evaluated in part 2
Techniques for enhancing digital images
The images obtain from either research studies or optical instruments are
often corrupted with noise. Image denoising involves the manipulation of image
data to produce a visually high quality image. This thesis reviews the existing
denoising algorithms and the filtering approaches available for enhancing images
and/or data transmission.
Spatial-domain and Transform-domain digital image filtering algorithms
have been used in the past to suppress different noise models. The different noise
models can be either additive or multiplicative. Selection of the denoising algorithm
is application dependent. It is necessary to have knowledge about the noise present
in the image so as to select the appropriated denoising algorithm. Noise models
may include Gaussian noise, Salt and Pepper noise, Speckle noise and Brownian
noise. The Wavelet Transform is similar to the Fourier transform with a completely
different merit function. The main difference between Wavelet transform and
Fourier transform is that, in the Wavelet Transform, Wavelets are localized in both
time and frequency. In the standard Fourier Transform, Wavelets are only localized
in frequency. Wavelet analysis consists of breaking up the signal into shifted and
scales versions of the original (or mother) Wavelet. The Wiener Filter (mean
squared estimation error) finds implementations as a LMS filter (least mean
squares), RLS filter (recursive least squares), or Kalman filter.
Quantitative measure (metrics) of the comparison of the denoising algorithms
is provided by calculating the Peak Signal to Noise Ratio (PSNR), the Mean Square
Error (MSE) value and the Mean Absolute Error (MAE) evaluation factors. A
combination of metrics including the PSNR, MSE, and MAE are often required to
clearly assess the model performance
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