Koninklijke Philips Corporation N.v.

Giuseppe Valvano

Elwin De Weerdt

Silke Hey

Jean-Pierre Franciscus Alexander Maria Ermes

Pierre Ermes

Disclosed herein is a medical system () comprising a memory () storing machine executable instructions (). The medical system further comprises a computational system (). Execution of the machine executable instructions causes the computational system to: receive () initial pulse sequence commands (), wherein the initial pulse sequence commands are configured for controlling a magnetic resonance imaging system () to acquire k-space data () following a non-Cartesian k-space sampling pattern ('), wherein the initial pulse sequence commands are configured for controlling the magnetic resonance imaging system to sample the non-Cartesian k-space sampling pattern by repeatedly sampling a Cartesian k-space sampling pattern () that is rotated for each acquisition, wherein the non-Cartesian k-space sampling pattern has an effective water-fat shift direction (′); receive () a chosen water-fat shift direction (); and construct () modified pulse sequence commands by rotating the non-Cartesian k-space sampling pattern such that the effective water-fat shift direction is aligned with the water-fat shift direction.

Adaptive water-fat shift in non-Cartesian magnetic resonance imaging

The invention provides for a medical imaging system (). The execution of the machine executable instructions () causes a processor () to: receive () multiple diffusion weighted images () of a subject (), wherein the multiple diffusion weighted images each have an assigned b-value, wherein the multiple diffusion weighted images each have an assigned diffusion weighting direction, wherein for a region of interest () there is at least one corresponding voxel () in each of the multiple diffusion weighted images; construct () a set of equations () for each of the at least one corresponding voxel, wherein the set of equations is constructed from an apparent diffusion equation for the assigned diffusion weighting direction of each of the multiple diffusion weighted images; solve () the set of equations for each voxel for the bvalue as an optimization; and construct () a bimage using the bvalue for each voxel.

Calculation of a B0 image multiple diffusion weighted MR images

Disclosed is a medical imaging system (). The execution of machine executable instructions () causes a processor () to: receive () measured magnetic resonance imaging data () descriptive of a first region of interest () of a subject (); receive () a B0 map (), a T1 map (), a T2 map (), and a magnetization map () each descriptive of a second region of interest () of the subject; receive () pulse sequence commands (); calculate () a simulated magnetic resonance image () of an overlapping region of interest () using at least the B0 map, the T1 map, the T2 map, the magnetization map, and the pulse sequence commands as input to a Bloch equation model (); and reconstruct () a corrected magnetic resonance image from the measured magnetic resonance imaging data for the overlapping region of interest by solving an inverse problem. The inverse problem comprises an optimization of a cost function and a regularization term formed from the simulated magnetic resonance image.

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Giuseppe Valvano