Perspectum Limited

Siemens Medical Solutions Usa, Inc.

Isis Innovation Limited

Michael J Brady

Matthew David Kelly

Matthew Robson

Jérôme Marie Joseph Declerck

Chloe Hutton

Benjamin J Irving

Olivier Noterdaeme

Michael L Gyngell

Alexis Roche

Olivier Corolleur

Alexandre Bagur

Marija Haramija

Ben Irving

A method of analysing the magnitude of Magnetic Resonance Imaging (MRI) data is described. The method comprising: using the magnitude only of the multi-echo MRI data of images from the subject, where images are acquired at arbitrarily timed echoes including at least one echo time where water and fat are not substantially in-phase; fitting the magnitude of said multi-echo MRI data to a single signal model to produce a plurality of potential solutions for the relative signal contributions for each of the at least two species from the model, by using a plurality of different starting conditions to generate a particular cost function value for each of the plurality of starting conditions, where said cost function values are independent of a field map term for the MRI data; analysing said cost function values to calculate relative signal separation contribution for each species at each voxel of the images.

Method of analysing magnetic resonance imaging images

A method of providing a quantitative volumetric assessment of organ health or a quantitative map of an organ. The method comprises obtaining a volumetric map of organ health comprising information defining a state of tissue health across at least part of an organ, receiving an input defining at least one organ section, determining an assessment organ volume based at least partly on the at least one defined organ section, calculating an organ-viability measure for the assessment organ volume based at least partly on information within the volumetric map defining the state of tissue health across the organ volume, and outputting an indication of the organ-viability measure.

Method and apparatus for providing a quantitative volumetric map of an organ or an assessment of organ health

Intensity inhomogeneities can obscure areas of interest and are problematic for MR image segmentation algorithms. An efficient approach to estimating these inhomogeneities by computing a calibration factor that is a function of an estimated bias field from a series of calibration scans is disclosed. This enables correction of TW and TW images by reducing inhomogeneities without the need to map Tand T. Because of interest in the shape of the intensity inhomogeneities a limited number of flip angles yield satisfactory performance. Additionally, an RF transmit field Bcan be estimated and inhomogeneities resulting from the Bfield reduced from the MR image using the estimated Bfield.

Systems and methods for correction of inhomogeneities in magnetic resonance images

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.

Similarity measures

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Oxford, United Kingdom

Michael J Brady