{
  "patent_number": "US 11422217",
  "country": "US",
  "title": "How AI Reconstructs Clearer MRI Scans Using Progressive Networks",
  "original_title": "Progressive generative adversarial network in medical image reconstruction",
  "summary": "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_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)."
  ],
  "filed": "2020-06-10",
  "granted": "2022-08-23",
  "expires": "2040-06-10",
  "status": "active",
  "holder": "Siemens Healthcare",
  "holder_url": "https://patentbrief.org/company/siemens-healthcare",
  "inventors": [
    {
      "name": "Simon Arberet",
      "url": "https://patentbrief.org/inventor/simon-arberet"
    },
    {
      "name": "Boris Mailhe",
      "url": "https://patentbrief.org/inventor/boris-mailhe"
    },
    {
      "name": "Mariappan S. Nadar",
      "url": "https://patentbrief.org/inventor/mariappan-s-nadar"
    },
    {
      "name": "Anatole Louis Jerome Moreau",
      "url": "https://patentbrief.org/inventor/anatole-louis-jerome-moreau"
    }
  ],
  "times_cited": 1,
  "tags": [
    "medical_imaging",
    "ai_ml",
    "software",
    "telecommunications",
    "consumer_electronics"
  ],
  "abstract": "For reconstruction in medical imaging, such as reconstruction in MR imaging, a high-resolution image is reconstructed using a generator of a progressive generative adversarial network (PGAN or progressive GAN). In machine training the network, both the generator and discriminator of the GAN are grown progressively: starting from a low resolution, new layers are added that model finer details as training progresses. The resulting generator may be better able to handle high-resolution information than a generator of a GAN.",
  "url": "https://patentbrief.org/patent/us/11422217/progressive-generative-adversarial-network-in-medical-image-reconstruction",
  "markdown_url": "https://patentbrief.org/patent/us/11422217/progressive-generative-adversarial-network-in-medical-image-reconstruction/md",
  "google_patents_url": "https://patents.google.com/patent/US11422217",
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}