Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint / 南方医科大学学报

OBJECTIVE@#To propose a diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio.@*METHODS@#The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method.@*RESULTS@#The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters.@*CONCLUSION@#The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.

Image Denoising for Metal MRI Exploiting Sparsity and Low Rank Priors

PURPOSE: The management of metal-induced field inhomogeneities is one of the major concerns of distortion-free magnetic resonance images near metallic implants. The recently proposed method called “Slice Encoding for Metal Artifact Correction (SEMAC)” is an effective spin echo pulse sequence of magnetic resonance imaging (MRI) near metallic implants. However, as SEMAC uses the noisy resolved data elements, SEMAC images can have a major problem for improving the signal-to-noise ratio (SNR) without compromising the correction of metal artifacts. To address that issue, this paper presents a novel reconstruction technique for providing an improvement of the SNR in SEMAC images without sacrificing the correction of metal artifacts. MATERIALS AND METHODS: Low-rank approximation in each coil image is first performed to suppress the noise in the slice direction, because the signal is highly correlated between SEMAC-encoded slices. Secondly, SEMAC images are reconstructed by the best linear unbiased estimator (BLUE), also known as Gauss-Markov or weighted least squares. Noise levels and correlation in the receiver channels are considered for the sake of SNR optimization. To this end, since distorted excitation profiles are sparse, l1 minimization performs well in recovering the sparse distorted excitation profiles and the sparse modeling of our approach offers excellent correction of metal-induced distortions. RESULTS: Three images reconstructed using SEMAC, SEMAC with the conventional two-step noise reduction, and the proposed image denoising for metal MRI exploiting sparsity and low rank approximation algorithm were compared. The proposed algorithm outperformed two methods and produced 119% SNR better than SEMAC and 89% SNR better than SEMAC with the conventional two-step noise reduction. CONCLUSION: We successfully demonstrated that the proposed, novel algorithm for SEMAC, if compared with conventional de-noising methods, substantially improves SNR and reduces artifacts.

Medical Image Denoising Based on Wavelet-Domain Hidden Markov Tree / 医疗卫生装备

Objective To denoise digital radiographic images well.Methods A technique was presented that used the Anscombe's transformation to adjust the original image to a Gaussian noise model based upon the wavelet denoising method and the wavelet-domain Hidden Markov Tree(HMT) model.Wavelet domain HMT models were used to determine the dependencies of multiscale wavelet coefficients through the state probabilities of the wavelet coefficients,whose sedistribution densities could be approximated by Gaussian mixture model.Results The proposed method could keep natural images edges from damaging and increase PSNR.Conclusion Quantitative and qualitative DR images assessment shows that the proposed algorithm outperforms the traditional Gaussian filter in terms of noise reduction,quality of details and bone sharpness.

Image Pre-processing of Gene Chips Based on MATLAB / 医疗卫生装备

Objective To design an image pre-processing system of low-density gene chips based on MATLAB, which can process the colored images by cy3 and cy5 fluorescence staining of low-density chips obtained by the array scanning system. It can filter out noise, enhance the contrast gradient of image, improve the quality of image, and implement the functions of image segmentation, edge detection and region identification. Methods The median filter method of the wavelet was used to implement the function of image denoising and improve the image quality. Edge detection was accomplished by wavelet, combining with edge operators. Image segmentation was developed by genetic algorithms. Results It could reduce the effect of spot, noise and other factors, improve the quality of image, and detect the periphery of image better and the region of sampling point more precisely. It can also effectively separate the valuable weak signal points and background or noise with the system. Conclusion The system can accomplish the functions of image pre-processing of low-density gene chips, and the adopted methods are feasible. It can provide relative accurate data information for future analysis.