Abstract: An atlas-free magnetic resonance imaging method images at least one part of a brain. An MRI sequence configured for acquiring two image volumes of the part at different inversion times within a single acquisition is combined to a fat-water separation method for acquiring a fat-water separated image. For each echo time two image volumes are acquired, respectively a first image volume and a second image volume at the first echo time, and a first image volume and a second image volume at the second echo time, and combined to a uniform image. The acquired images are combined to form a final uniform image, a final fat-water separated image, and a final second image volume that are fed into a multichannel image segmentation algorithm using a Markov random field model for segmenting the part into multiple classes of cranial tissues, in order to obtain a segmented image of said part.

Abstract: A simple method to denoise ratio images in magnetic resonance imaging, includes generating a MRI sequence provided for acquiring data from an object to be imaged, wherein the MRI sequence is configured for generating at least two different standard images, respectively a first standard image and a second standard image, acquiring the two different standard images, and combining the two different standard images in a ratio image. The ratio image is obtained by calculating a ratio of the first standard image and the second standard image that is tunable by a parameter ? wherein the parameter ? is automatically chosen for maximizing the negentropy of the ratio image.

Abstract: An atlas-free magnetic resonance imaging method images at least one part of a brain. An MRI sequence configured for acquiring two image volumes of the part at different inversion times within a single acquisition is combined to a fat-water separation method for acquiring a fat-water separated image. For each echo time two image volumes are acquired, respectively a first image volume and a second image volume at the first echo time, and a first image volume and a second image volume at the second echo time, and combined to a uniform image. The acquired images are combined to form a final uniform image, a final fat-water separated image, and a final second image volume that are fed into a multichannel image segmentation algorithm using a Markov random field model for segmenting the part into multiple classes of cranial tissues, in order to obtain a segmented image of said part.

Abstract: A simple method to denoise ratio images in magnetic resonance imaging, includes generating a MRI sequence provided for acquiring data from an object to be imaged, wherein the MRI sequence is configured for generating at least two different standard images, respectively a first standard image and a second standard image, acquiring the two different standard images, and combining the two different standard images in a ratio image. The ratio image is obtained by calculating a ratio of the first standard image and the second standard image that is tunable by a parameter ? wherein the parameter ? is automatically chosen for maximizing the negentropy of the ratio image.

Abstract: A method for performing magnetic resonance diffusion imaging, comprising: (a) acquiring a sequence of magnetic resonance images of a target body (BS) using diffusion-encoding gradient pulses applied along a set of non-collinear orientations ( ) sampling a three-dimensional orientation space (AR); (b) estimating, from said sequence of images, a set of space- and orientation-dependent parameters representative of molecular diffusion within said target body; wherein said step of estimating a set of space- and orientation-dependent parameters representative of molecular diffusion within said target body is performed in real time by means of an incremental estimator such as a Kalman filter fed by data provided by said magnetic resonance images; characterized in that said three-dimensional orientation space (AR) selectively excludes a set of orientations (FR) for which magnetic resonance signal intensity is attenuated by diffusion below a threshold level.

Abstract: A method for performing magnetic resonance diffusion imaging, comprising: (a) acquiring a sequence of magnetic resonance images of a target body (BS) using diffusion-encoding gradient pulses applied along a set of non-collinear orientations ( ) sampling a three-dimensional orientation space (AR); (b) estimating, from said sequence of images, a set of space- and orientation-dependent parameters representative of molecular diffusion within said target body; wherein said step of estimating a set of space- and orientation-dependent parameters representative of molecular diffusion within said target body is performed in real time by means of an incremental estimator such as a Kalman filter fed by data provided by said magnetic resonance images; characterized in that said three-dimensional orientation space (AR) selectively excludes a set of orientations (FR) for which magnetic resonance signal intensity is attenuated by diffusion below a threshold level.

Abstract: A method of matching data sets, such as different images, by estimating a transformation which relates the two images. Different candidate transformations are scored by using a similarity measure calculated on the basis of probability density functions derived from the two data sets themselves. The probability density functions are based on local likelihood density estimation (LLDE). The technique is applicable to image registration, and also to data fusion.

Abstract: A method of matching data sets, such as different images, by estimating a transformation which relates the two images. Different candidate transformations are scored by using a similarity measure calculated on the basis of probability density functions derived from the two data sets themselves. The probability density functions are based on local likelihood density estimation (LLDE). The technique is applicable to image registration, and also to data fusion.

Abstract: An electronic data processing device receives first and second data sets representing first and second comparable digital images. It comprises registration means comprising a first module which calculates a main function representative of the correlation ratios between the data of the first and second sets, and a second module which determines a registration transformation between one of the images and the other from the main function.