Abstract: Techniques are disclosed for measuring the corpus callosum volume of a fetus using magnetic resonance imaging. A scanogram of a fetus is acquired, and a detection area is determined using the corpus callosum position of the fetus in the scanogram. Magnetic resonance scanning is performed on the detection area to obtain a diffusion weighted image, with a gradient direction that is orthogonal or normal to an extending direction of fiber bundles of the corpus callosum. A fetal head image is cropped in the diffusion weighted image, and a predetermined threshold is applied to obtain an image including pixels having a brightness value that is greater than the threshold. Image processing is performed on the binarized image, with the largest region therein being identified as the corpus callosum, and the sum of voxel dimensions associated with the signal of the largest region being calculated as the corpus callosum volume.
April 24, 2020
October 29, 2020
Siemens Healthineers Ltd., Shandong Medical Imaging Research Institute
Guangbin Wang, Tian Yi Qian, Xin Chen, Cong Sun
Abstract: The present disclosure related to techniques for implementing Chemical Exchange Saturation Transfer-Magnetic Resonance Imaging (CEST-MRI) sequence generation. The techniques include starting a CEST-MRI scanning process, and generating and transmitting a CEST pre-saturation pulse. When transmission of the CEST pre-saturation pulse has ended, the MRI device generates and transmits a fat-suppression pulse. When transmission of the fat-suppression pulse has ended, the MRI device generates and transmits an excitation pulse. When transmission of the excitation pulse has ended, the MRI device generates and transmits multiple non-slice-selective refocusing square-wave pulses. The present disclosure functions to increase the spatial coverage and MR signal acquisition speed of CEST-MRI imaging.
March 19, 2020
September 24, 2020
Siemens Healthineers Ltd., Zhejiang University
Abstract: A magnetic resonance diffusion tensor imaging method and corresponding device. The method includes acquiring omnidirectionally sampled diffusion weighted images of a plurality of training samples; performing diffusion tensor model fitting and undersampling for the omnidirectionally sampled diffusion weighted images of each training sample to obtain an omnidirectionally sampled diffusion tensor image and an undersampled diffusion weighted image; training a deep learning network, with the omnidirectionally sampled diffusion tensor images of the plurality of training samples as training targets and the undersampled diffusion weighted images as training data; acquiring undersampled diffusion weighted images of a target object; and inputting the undersampled diffusion weighted images of target objects into the trained deep learning network to obtain the predicted omnidirectionally sampled diffusion tensor images of the target objects. Also, a fiber tracking method and corresponding device.