How AI Reconstructs Clearer MRI Scans Using Progressive Networks

This patent describes using a special type of artificial intelligence called a progressive generative adversarial network to reconstruct high-resolution magnetic resonance images, making them clearer and more detailed for doctors.

What it covers

This patent outlines a method for reconstructing magnetic resonance (MR) images from raw scan data. It uses an image processor to apply a 'regularizer' during reconstruction, which is a machine-learned progressive generator from a progressive generative adversarial network (PGAN). This generator is an 'image-to-image U-net type network' integrated into an 'unrolled iterative reconstruction algorithm' (Claim 1). The PGAN generator is trained by starting at a low resolution and progressively adding new layers to model finer details as training advances (Abstract, Claim 7). For example, after an MR system scans a patient and collects k-space measurements, this method reconstructs a high-quality image by iteratively refining it with the PGAN, then displays the clear MR image.

What it doesn't cover

• —Does not cover medical image reconstruction methods that do not specifically use a generative adversarial network (GAN) as a regularizer.
• —Does not cover GANs that are not progressively trained, meaning they don't start at low resolution and add layers for higher detail (Claim 7, 8).
• —Does not cover reconstruction algorithms that are not an 'unrolled iterative reconstruction' (Claim 1, 5).
• —Does not cover generators that are not an 'image-to-image U-net type network' (Claim 1).
• —Does not cover the reconstruction of images from medical modalities other than Magnetic Resonance (MR) imaging (Claim 1).

The clever bit

The innovation lies in using a 'progressive' training approach for the generative adversarial network. Instead of training a full-resolution network from scratch, it starts with low-resolution images and gradually adds layers to learn finer details. This makes the network more stable and better at handling the complex, high-resolution information needed for medical imaging.

Why it matters

Clearer and more detailed MR images are crucial for accurate medical diagnoses. This technology aims to improve image quality, potentially allowing doctors to detect subtle abnormalities more easily. It could also lead to faster scan times, making the MR experience more comfortable for patients and increasing patient throughput in clinics.

Real-world examples

• 1.Siemens Healthineers MR imaging systems
• 2.Advanced diagnostic MRI scanners
• 3.AI-powered medical image processing software
• 4.Clinical radiology workflows