# 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. - **Patent:** US 11422217 - **Original title:** Progressive generative adversarial network in medical image reconstruction - **Owner:** Siemens Healthcare - **Granted:** 2022 - **Status:** Active - **Times cited:** 1 - **Field:** medical_imaging, ai_ml, software, telecommunications, consumer_electronics ## What it does 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 does NOT 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. ## 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 ## 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. ## Frequently asked questions ### What does How AI Reconstructs Clearer MRI Scans Using Progressive Networks cover? 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. ### Who owns patent US 11422217? Siemens Healthcare owns this patent, granted in 2022. ### When does this patent expire? This patent is expected to expire on June 10, 2040, when the invention enters the public domain. ### What is patent US 11422217 cited by? This patent has been cited by 1 later patents that build on its ideas. ### What problem does this patent solve? 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. ### What does this patent NOT cover? Does not cover medical image reconstruction methods that do not specifically use a generative adversarial network (GAN) as a regularizer. **Full plain-English explainer:** https://patentbrief.org/patent/us/11422217/progressive-generative-adversarial-network-in-medical-image-reconstruction **Original patent:** https://patents.google.com/patent/US11422217 --- _Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._ ## Related patents Semantically similar inventions in the PatentBrief corpus: - [AI Method for Sharpening Low-Dose PET Scans with Multi-Task Learning](https://patentbrief.org/patent/us/11756161/method-and-system-for-generating-multi-task-learning-type-generative-adversarial) — This patent describes an AI method using a special type of neural network called a multi-task learning generative adversarial network (GAN) to improve the quality of medical images from low-dose PET scans, making them look like standard-dose scans. - [Improving Medical Image Segmentation with AI and Fake Data](https://patentbrief.org/patent/us/11580646/medical-image-segmentation-method-based-on-u-net) — This patent describes a method to make AI better at finding specific things in medical images, like tumors, by using computer-generated fake images to train the AI and a special network design. - [How AI Generates Images Based on Style and Content Cues](https://patentbrief.org/patent/us/12141700/generative-adversarial-network-for-generating-images) — This patent describes an artificial intelligence system that creates new images by understanding both what the image should show (like a cat) and how it should look (like a high-quality photo), using a special internal layer to blend these instructions. - [Upscaling Images with AI and Sub-Pixel Information](https://patentbrief.org/patent/us/12737843/upsampling-an-image-using-one-or-more-neural-networks) — This patent describes how artificial intelligence, specifically neural networks, can make low-resolution images look sharper by cleverly guessing new pixel colors based on tiny details between existing pixels. - [Removing Radio Interference from Portable MRI Scans](https://patentbrief.org/patent/us/12189012/eva-suit) — A method to clean up MRI images by using external sensors to detect and subtract outside radio interference, allowing portable scanners to work outside shielded rooms.