Common Information Enhanced Reconstruction for Accelerated High-resolution Multi-shot Diffusion Imaging
| Autor: | Feng Huang, Hua Guo, Yuhsuan Wu, Xiaodong Ma |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Image quality Biomedical Engineering Biophysics Initialization Iterative reconstruction Signal-To-Noise Ratio Composite image filter 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Conjugate gradient method Image Processing Computer-Assisted Humans Radiology Nuclear Medicine and imaging Computer vision Diffusion (business) Models Statistical business.industry Brain Reproducibility of Results Diffusion Magnetic Resonance Imaging Diffusion Tensor Imaging Undersampling Anisotropy Artificial intelligence business 030217 neurology & neurosurgery Algorithms Software Diffusion MRI |
| Zdroj: | Magnetic resonance imaging. 62 |
| ISSN: | 1873-5894 |
| Popis: | Purpose Multi-shot technique can effectively achieve high-resolution diffusion weighted images, but the acquisition time of multi-shot technique is prolonged, especially for multiple direction diffusion encoding. Thus, increasing acquisition efficiency is highly desirable for high-resolution diffusion tensor imaging (DTI). In this study, based on the assumption that different diffusion directions share the common information, image ratio constrained reconstruction (IRCR) combined with iterative self-consistent parallel imaging reconstruction (SPIRiT) is proposed to improve data sampling efficiency and image reconstruction fidelity for high-resolution DTI. Theory and methods The proposed reconstruction framework is named Common Information Enhanced Reconstruction (CIER). Inter-image correlation among different direction diffusion-weighted images is used through common information, which is an isotropic component and structure, for improving the performance of reconstruction. The framework consists of three steps. (i) Pre-processing: three intermediate multi-shot images, low-resolution composite image, high-resolution composite image and low-resolution diffusion weighted image, are generated based on the SPIRiT method. (ii) IRCR: the initial high–resolution diffusion weighted image is calculated from the images in step (i) based on that the ratio map between high-resolution images is approximated by the ratio map between the corresponding low-resolution images. (iii) Final SPIRiT reconstruction: the final image is generated with the image from IRCR as initialization by considering data consistency only in the SPIRiT calculation. A specific implementation based on multishot variable density spiral (VDS) DTI is used to demonstrate the method. Results The proposed CIER method was compared with the traditional reconstruction methods, conjugate gradient SENSE (CG-SENSE), L1-regularized SPIRiT (L1-SPIRiT), and anisotropic-sparsity SPIRiT (AS-SPIRiT) in brain DTI at acceleration factors of 3 to 7. CIER provided better diffusion image quality than other methods shown by both qualitative and quantitative results, especially at higher undersampling acceleration factors. Conclusion CIER offers better diffusion image quality at higher undersampling acceleration factors for high-resolution DTI. Both qualitative and quantitative results prove that common information can be used to improve sampling efficiency and maintain the image quality of diffusion-weighted images. |
| Databáze: | OpenAIRE |
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