Fast MRI using artificial intelligence

Our lab has a rich history on the development of fast MRI techniques that acquire less k-space data (undersampling) and reconstruct images without undersampling errors by exploiting image properties, such as compressibility, structure, similarity to other images, etc. Previous work was based on compressed sensing to exploit the natural compressibility of medical images to reduce k-space data and accelerate acquisition. Current work is focused on deep learning, where a reconstruction network is learned from multiple examples to map undersampled k-space data to images without undersampling errors. Our AI reconstruction work evolved from supervised to self-supervised learning and from image-to-image mapping to generative networks. 

  • High acceleration of 3D brain MRI using modular networks: Figure 1 for an example of 8-fold acceleration of brain MPRAGE to reduce the scan time to only 1 minute.   
Figure 1
Figure 1: Modular reconstruction network for highly-accelerated 3D MRI based on patch embedding and spatial mixing. The network enables to accelerate brain MPRAGE by factor of 8 and reduce scan time to 1 minute with similar information to conventional MPRAGE with 4-minute scan time.
  • Movienet reconstruction network for dynamic motion-resolved 4D MRI:  not only to reduce scan time but to significantly reduce the reconstruction time and enable 4D MRI in a clinical setting – see Figure 2 for an example of Movienet which replaces k-space consistency for motion consistency to reduce the reconstruction time to less than 1 second (compressed sensing 4D MRI reconstruction time was higher than 10 minutes).
Figure 2: Fast 4D MRI using auto-navigated radial acquisition and AI reconstruction (movienet). In addition to reducing scan time compared to compressed sensing, movienet enabled reconstruction times of only 2 seconds
Figure 2: Fast 4D MRI using auto-navigated radial acquisition and AI reconstruction (movienet). In addition to reducing scan time compared to compressed sensing, movienet enabled reconstruction times of only 2 seconds
  • Generative diffusion AI-based reconstruction for multi-sequence acceleration of brain imaging: joint acceleration of 4 sequences (MPRAGE-Pre, MPRAGE-Post, T2-FLAIR, and T2-Post) was performed using a diffusion network that learns a common image distribution model and generates images without aliasing artifacts from under sampled k-space data. Figure 3 shows the diffusion network workflow and application to a scan a patient with brain tumors in only 6 minutes, with comparable image quality to the standard scan of 12 minutes.
Figure 3: Generative diffusion reconstruction network to jointly accelerate 4 sequences in brain MRI with comparable image quality to the clinical standard in half the scan time.
Figure 3: Generative diffusion reconstruction network to jointly accelerate 4 sequences in brain MRI with comparable image quality to the clinical standard in half the scan time.
  • Generative diffusion bridge-based reconstruction for acceleration motion-compensated abdominal MRI in less than 20 seconds: a diffusion bridge model enables to transform between the distribution of two types of images  directly without converting images to noise and were able to highly accelerate and compensate for motion in abdominal MRI, as shown in Figure 4.
Top: Generative diffusion bridge approach. The forward bridge diffusion process gradually transforms the unaccelerated motion-free image to an accelerated motion-corrupted image by adding noise. The reverse bridge diffusion process uses a score function learned with a neural network. Bottom: Reconstruction of a testing case with 9-fold acceleration (100 spokes = 15 seconds scan time).
Figure 4: Top: Generative diffusion bridge approach. The forward bridge diffusion process gradually transforms the unaccelerated motion-free image to an accelerated motion-corrupted image by adding noise. The reverse bridge diffusion process uses a score function learned with a neural network. Bottom: Reconstruction of a testing case with 9-fold acceleration (100 spokes = 15 seconds scan time).