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Ultrasound (US) imaging exhibits considerable difficulties for medical visual inspection and for development of automatic analysis methods due to speckle, which negatively affects the perception of tissue boundaries and the performance of automatic segmentation methods. With the aim of alleviating the effect of speckle, many filtering techniques are usually(More)
Recently, some methods have been proposed for filtering multi-coil MRI acquisitions with correlation between coils. Those methods are based on statistical models of noise to develop a Linear Minimum Mean Square Error (LMMSE) filter. The advantage of LMMSE-based filters stems from their simplicity and robustness. However, they exhibit some drawbacks: their(More)
In Magnetic Resonance (MR) image reconstruction, under-sampled data sets lead to ill-posed reconstruction problems. To regularize these problems, prior knowledge is commonly exploited. In this work, we introduce a new type of prior knowledge, partial discreteness, where part of the image is assumed to be homogeneous and can be well represented by a constant(More)
In quantitative MR T1 mapping, the spin-lattice relaxation time T1 of tissues is estimated from a series of T1 -weighted images. As the T1 estimation is a voxel-wise estimation procedure, correct spatial alignment of the T1 -weighted images is crucial. Conventionally, the T1 -weighted images are first registered based on a general-purpose registration(More)
An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness (PD), where a small number of regions in the image are assumed to be(More)
In magnetic resonance imaging the spin-lattice relaxation time (T<sub>1</sub>) of tissues is estimated from a set of T<sub>1</sub>-weighted images. As the T<sub>1</sub> estimation is a voxel-wise estimation, correct alignment of the T<sub>1</sub>-weighted images is crucial for T<sub>1</sub> mapping. Therefore, to correct for motion, the(More)
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