Member, Maternal & Child Health Research Institute (MCHRI)
Shreyas Vasanawala, Postdoctoral Faculty Sponsor
Accelerated multi-contrast reconstruction for synthetic MRI using joint parallel imaging and variable splitting networks.
PURPOSE: Synthetic magnetic resonance imaging (MRI) requires the acquisition of multi-contrast images to estimate quantitative parameter maps, such as T1 , T2 , and proton density (PD). The study aims to develop a multi-contrast reconstruction method based on joint parallel imaging (JPI) and joint deep learning (JDL) to enable further acceleration of synthetic MRI.METHODS: The JPI and JDL methods are extended and combined to improve reconstruction for better-quality, synthesized images. JPI is performed as a first step to estimate the missing k-space lines, and JDL is then performed to correct and refine the previous estimate with a trained neural network. For the JDL architecture, the original variable splitting network (VS-Net) is modified and extended to form a joint variable splitting network (JVS-Net) to apply to multi-contrast reconstructions. The proposed method is designed and tested for multi-dynamic multi-echo (MDME) images with Cartesian uniform under-sampling using acceleration factors between 4 and 8.RESULTS: It is demonstrated that the nRMSE is lower and the structural similarity index (SSIM) values are higher with the proposed method compared to both the JPI and JDL methods individually. The method also demonstrates the potential to produce a set of synthesized contrast-weighted images that closely resemble those from the fully sampled acquisition without erroneous artifacts.CONCLUSION: Combining JPI and JDL enables the reconstruction of highly accelerated synthetic MRIs.
View details for DOI 10.1002/mp.14848
View details for PubMedID 33733464
Improving phase-based conductivity reconstruction by means of deep learning-based denoising of
phase data for 3T MRI.
Magnetic resonance in medicine
To denoise B 1 + phase using a deep learning method for phase-based in vivo electrical conductivity reconstruction in a 3T MR system.For B 1 + phase deep-learning denoising, a convolutional neural network (U-net) was chosen. Training was performed on data sets from 10 healthy volunteers. Input data were the real and imaginary components of single averaged spin-echo data (SNR = 45), which was used to approximate the B 1 + phase. For label data, multiple signal-averaged spin-echo data (SNR = 128) were used. Testing was performed on in silico and in vivo data. Reconstructed conductivity maps were derived using phase-based conductivity reconstructions. Additionally, we investigated the usability of the network to various SNR levels, imaging contrasts, and anatomical sites (ie, T1 , T2 , and proton density-weighted brain images and proton density-weighted breast images. In addition, conductivity reconstructions from deep learning-based denoised data were compared with conventional image filters, which were used for data denoising in electrical properties tomography (ie, the Gaussian filtering and the Savitzky-Golay filtering).The proposed deep learning-based denoising approach showed improvement for B 1 + phase for both in silico and in vivo experiments with reduced quantitative error measures compared with other methods. Subsequently, this resulted in an improvement of reconstructed conductivity maps from the denoised B 1 + phase with deep learning.The results suggest that the proposed approach can be used as an alternative preprocessing method to denoise B 1 + maps for phase-based conductivity reconstruction without relying on image filters or signal averaging.
View details for DOI 10.1002/mrm.28826
View details for PubMedID 33949721