M1.7 | Model-based reconstruction MRI

Funding period: Oct 2017 to Dec 2021

Researcher: Chompunuch Sarasaen



Keywords: dynamic MRI, prior knowledge, super-resolution, Deep Learning, MRI reconstruction

Magnetic Resonance Imaging (MRI) Is a promising tool for minimally invasive interventions. Nevertheless, achieving high temporal resolution may result in compromised spatial resolution, so-called a spatio-temporal trade-off. In fact, there is available prior knowledge such as high resolution planning scans or dynamic images containing temporal redundancy which could be useful but are typically ignored. 

To incorporate prior knowledge in model-based (super-resolution) MRI reconstruction to generate high spatial and high temporal resolution of abdominal dynamic MRI with less-than-complete data.

The study investigates the incorporation of prior information of MR data: FTSuperResDyn and DDoS-UNet. The FTSuperResDyn consists of three main steps: main training, fine-tuning with subject-specific scan, and inference. The patch-based super-resolution was utilized to tackle the lack of abdominal MR data. The main training performed with the publicly available dataset (T1 dual images). Then, the network was fine-tuned using high resolution plaining scan. After that, 3D dynamic images were reconstructed. Furthermore, the DDoS-UNet aims to include the additional temporal information to the network by means of dual channel training of static and dynamic images of the different time-points. The performance of both proposed methods were evaluated with different in-plane undersampled levels.

While reconstructing the super-resolved images, the FTSuperResDyn approach achieved the average SSIM value of the highest undersampling (6.25% of the k-space) before and after fine-tuning were 0.939 ± 0.008 and 0.957 ± 0.006, respectively. The DDoS-UNet model showed the ability to reconstruct even higher undersampling of 4% of the k-space with the average SSIM of 0.951 ± 0.017. It can be seen that both of the proposed methods could reconstruct high spatial resolution images while reducing scan-time. 

This research illustrates the potential of incorporating prior knowledge into dynamic MRI reconstruction to mitigate the spatio-temporal trade-off. 

This work aimed at exploiting prior information to incorporate it into the framework with different aspects to tackle the spatio-temporal trade-off in MRI.

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