Zhejiang University

The General Hospital Corporation

Jinmin Xu

Huafeng Liu

Kawin Setsompop

Jason Stockmann

Huihui Ye

Lipid suppression in magnetic resonance imaging ('MRI') is provided on a slice-by-slice basis using tailored local field control that is configured for lipid control for each slice in a planned slice prescription. Only those lipid voxels that fall within the bandwidth of the concurrent RF excitation pulse are targeted. Switched Boffset fields are used to improve lipid suppression pulse performance by pushing water and lipids apart in the frequency domain. Multi-coil Bshim arrays with rapidly switchable output currents that can be turned on during the lipid suppression pulse may be used. A convex optimization may be used to jointly solve for the shim currents and the lipid suppression pulse center frequency and bandwidth to optimize lipid suppression while minimizing water signal loss.

Lipid suppression in magnetic resonance imaging using multi-coil local Bfield control

The present invention discloses a magnetic resonance fingerprinting imaging method with variable number of echoes, in addition to conventional MRF coding such as changing the excitation pulse angle, the method also introduces the change of the number of echoes, so that quantitative maps of B, B+, Tand T* can be obtained in a single scan. Further, if the echo time corresponding to the in-phase, opposed-phase and in-phase of water and fat is set for three consecutive echoes, the present invention can also image water and fat, and achieve the accurate quantification of B, B+, T, T, [T*]and [T*]. Through in vivo experiments and simulations, the effectiveness of the present invention has been proved. Therefore, the present invention can provide multiple information representations for common brain diseases (glioma) and fatty diseases (such as lipoma, fatty liver, etc.), which is conducive to clinical diagnosis and treatment.

Magnetic resonance fingerprinting imaging method with variable number of echoes

This present invention discloses a dynamic dual-tracer PET reconstruction method based on a hybrid-loss 3D CNN, which selects a corresponding 3D convolution kernel for a 3D format of dual-tracer PET data, and performs feature extraction in a stereoscopic receptive field (down-sampling) and the reconstruction (up-sampling) process, which accurately reconstructs the three-dimensional concentration distributions of two different tracers from the dynamic sinogram. The method of the invention can better reconstruct the simultaneous-injection single-acquisition dual-tracer sinogram without any model constraints. The scanning time required for dual-tracer PET can be minimized based on the method of the present invention. Using this method, the raw sinogram data of dual tracers can be reconstructed into two volumetric individual images in a short time.

Dynamic dual-tracer PET reconstruction method based on hybrid-loss 3D convolutional neural networks

The present invention discloses a DBN based separation method of a mixture of dual-tracer single-acquisition PET signals labelled with the same isotope. It predicts the two separate PET signals by establishing a complex mapping relationship between the dynamic mixed concentration distribution of the same isotope-labeled dual-tracer pairs and the two single radiotracer concentration images. Based on the compartment models and the Monte Carlo simulation, the present invention selects three sets of the same radionuclide-labeled tracer pairs as the objects and simulates the entire PET process from injection to scanning to generate enough training sets and testing sets. When inputting the testing sets into the constructed universal deep belief network trained by the training sets, the prediction results show that the two individual PET signals can been reconstructed well, which verifies the effectiveness of using the deep belief network to separate the dual-tracer PET signals labelled with the same isotope.

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Hangzhou, China

Jinmin Xu