Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks | Dongwook Lee et al. | ResearchPod

Transcript: Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks

Sam: Fast MRI scans come with a cost: the computer then spends minutes cleaning up the image. This work shows a neural network doing that cleanup in a few hundredths of a second. The trick is to treat an image's brightness and its phase as two separate problems, each with its own network.

Alex: Place it for me first. Whose work is this, and what kind of paper?

Sam: It's a method paper from Jong Chul Ye's group at KAIST in Korea, posted as an arXiv preprint in 2018. It proposes a network, tests it on brain scans, and compares it against two established reconstruction methods.

Alex: I don't work in MRI. Why should someone outside reconstruction care?

Sam: Scan time is one of the big limits on MRI. You shorten it by skipping measurements, and then you need software to fill in what you skipped. The best current software is slow, and it needs the raw scanner data. This approach is fast, and part of it works on ordinary images. So we'll cover what was slow before, the one idea that makes this work, and how far to trust the evidence.

Alex: Start with the skipping. What exactly gets left out?

Sam: An MRI scanner doesn't measure pixels directly. It measures k-space, which is the frequency data of the image. To go faster, you sample only some of k-space. If you just transform that back, you get aliasing artifacts, meaning ghost copies and streaks across the image. Here they skip enough to speed the scan up about three and a half times.

Alex: And the established fixes are what?

Sam: Two families. Parallel imaging, like GRAPPA, uses the different views from multiple receiver coils to fill in the gaps. Compressed sensing assumes the image is sparse in some transform and solves an iterative optimization. The authors' own earlier method, ALOHA, recasts the problem as completing a structured low-rank matrix in k-space. The authors say these give state-of-the-art quality but are computationally heavy. And they need raw k-space, so you can't use them to improve an image someone already saved.

Alex: People had already thrown neural networks at this, though. What's actually new here?

Sam: Right, earlier work used a network as a starting point or a regularizer inside compressed sensing, or unrolled the iterative algorithm into layers. The new piece is how they handle complex-valued data. An MR image has a magnitude, which is the brightness you see, and a phase. They train one network for each, separately. The network design itself is a dual frame U-net.

Alex: Give me a few words on that network.

Sam: A U-net is a multi-scale network. It shrinks the image in stages to see the big picture, then expands back, with shortcuts between matching scales. Shrinking down to a sixteen-by-sixteen grid gives it a large receptive field, which matters because undersampling artifacts spread across the whole image. The authors argue the standard U-net duplicates low-frequency content, and the dual frame version corrects that, based on their theory of deep convolutional framelets.

Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks

Dongwook Lee, Jaejun Yoo, Sungho Tak, Jong Chul Ye

Apr 2, 2018·8:24·pmc·Original Paper

Deep Residual LearningAccelerated MRICompressed Sensing (CS)Parallel Imaging (pMRI)Magnitude and Phase NetworksAccelerated MRI Reconstruction

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8 min

Summary

Research Question

Magnetic Resonance Imaging (MRI) is essential for diagnostics but constrained by long scan times. Accelerated acquisition techniques like parallel imaging and compressed sensing are used to shorten scans, but they are computationally expensive and sometimes struggle to fully remove coherent aliasing artifacts. The researchers investigate whether deep residual learning can provide faster, higher-quality image reconstruction by treating artifact removal as an image-restoration task.

Approach

The authors utilize a dual frame U-Net architecture, which uses a multi-scale approach to capture global artifact patterns. A novel aspect of this method is the separate training of magnitude and phase networks. This decomposition simplifies training and allows the magnitude network to act as a post-processing tool for images where only magnitude data is available. If phase information is present, the framework can perform an iterative k-space interpolation, using the learned network to replace handcrafted regularizers used in traditional compressed sensing algorithms. The networks were trained using a combination of public datasets (HCP) and in vivo clinical MR data.

Main Findings

The proposed model successfully removed strong, coherent aliasing artifacts from both single-coil and multi-channel MRI data, consistently outperforming traditional compressed sensing (ALOHA) and parallel imaging (GRAPPA) methods. A key advantage is the computational efficiency: once trained, the network reconstructs images in milliseconds, orders of magnitude faster than conventional iterative methods that take seconds or minutes to complete. The authors further show that pre-training the magnitude network on large datasets and using recursive, multi-scale network structures significantly enhances the final image quality.

Why It Matters

This research demonstrates that deep learning can overcome the computational bottleneck inherent in current accelerated MRI techniques. By generating accurate results in near real-time, this framework could enable faster clinical workflows, reducing patient discomfort while maintaining the diagnostic quality required for medical imaging. The modular design, allowing for the separate handling of magnitude and phase, offers flexibility for different clinical data types.

Key Terms and Definitions

Aliasing Artifacts — Unwanted patterns or ghosts in an image caused by undersampling k-space data below the Nyquist rate.
Dual Frame U-Net — A specialized neural network architecture that employs additional bypass connections to avoid the duplication of low-frequency content often seen in standard U-Nets.
Compressed Sensing (CS) MRI — An acquisition technique that allows for reconstruction from sparse or insufficient k-space data by exploiting signal sparsity.
k-space — The raw data domain in MRI, which represents spatial frequency information before being transformed into an image.
Parallel Imaging (pMRI) — An approach that uses multiple receiver coils to speed up acquisition by exploiting the spatial sensitivity differences between those coils.

Abstract

OBJECTIVE: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address this, we investigate deep residual learning networks to remove aliasing artifacts from artifact corrupted images. METHODS: The deep residual learning networks are composed of magnitude and phase networks that are separately trained. If both phase and magnitude information are available, the proposed algorithm can work as an iterative k-space interpolation algorithm using framelet representation. When only magnitude data are available, the proposed approach works as an image domain postprocessing algorithm. RESULTS: Even with strong coherent aliasing artifacts, the proposed network successfully learned and removed the aliasing artifacts, whereas current parallel and CS reconstruction methods were unable to remove these artifacts. CONCLUSION: Comparisons using single and multiple coil acquisition show that the proposed residual network provides good reconstruction results with orders of magnitude faster computational time than existing CS methods. SIGNIFICANCE: The proposed deep learning framework may have a great potential for accelerated MR reconstruction by generating accurate results immediately.

Deep Residual Learning for Accelerated MRI using Magnitude and Phase Networks: Research Paper Podcast