{
  "patent_number": "US 11756161",
  "country": "US",
  "title": "AI Method for Sharpening Low-Dose PET Scans with Multi-Task Learning",
  "original_title": "Method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction",
  "summary": "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.",
  "what_it_does": "The patent describes a method for creating an AI system that can take blurry, low-dose Positron Emission Tomography (PET) images and make them clearer, similar to standard-dose PET images. It starts by building a \"U-Net type picture generator\" by connecting an \"encoder\" and a \"decoder\" using \"skip connections\" (Claim 1). Then, it creates multiple \"generative adversarial networks\" (GANs) by pairing these picture generators with \"discriminators\" (Claim 1). These GANs learn in parallel, sharing basic information, forming a \"first multi-task learning-type generative adversarial network.\" The system is trained using a \"joint loss function\" that combines L1 type loss and cross entropy loss, comparing the AI's output to high-quality standard-dose PET images (Claims 1, 2). For example, if a patient receives a low-dose PET scan to reduce radiation exposure, this AI can process those images to produce diagnostic quality results.",
  "what_it_does_not_cover": [
    "Does not cover methods for reconstructing PET images without using a \"U-Net type picture generator\" with \"skip connections\" (Claim 1).",
    "Does not cover systems that use only a single generative adversarial network, as it specifies \"a group of generative adversarial networks\" and \"plurality of picture generators\" (Claim 1).",
    "Does not cover methods where the \"plurality of discriminators\" do not use an \"input modality,\" a \"tag image,\" and an \"output result\" as input (Claim 1).",
    "Does not cover methods that do not combine low-dose PET and MR images as input modalities for each GAN (Claim 1).",
    "Does not cover training methods that do not use a \"joint loss function 1\" combining L1 type loss and cross entropy loss functions (Claim 1, 2)."
  ],
  "filed": "2021-06-07",
  "granted": "2023-09-12",
  "expires": "2041-06-07",
  "status": "active",
  "holder": "Shenzhen Institutes of Advanced Technology",
  "holder_url": "https://patentbrief.org/company/shenzhen-institutes-of-advanced-technology",
  "inventors": [
    {
      "name": "Xin Liu",
      "url": "https://patentbrief.org/inventor/xin-liu"
    },
    {
      "name": "Na Zhang",
      "url": "https://patentbrief.org/inventor/na-zhang"
    },
    {
      "name": "Yongfeng YANG",
      "url": "https://patentbrief.org/inventor/yongfeng-yang"
    },
    {
      "name": "Dong Liang",
      "url": "https://patentbrief.org/inventor/dong-liang"
    },
    {
      "name": "Hairong Zheng",
      "url": "https://patentbrief.org/inventor/hairong-zheng"
    },
    {
      "name": "Zhanli HU",
      "url": "https://patentbrief.org/inventor/zhanli-hu"
    },
    {
      "name": "Hanyu Sun",
      "url": "https://patentbrief.org/inventor/hanyu-sun"
    }
  ],
  "times_cited": 0,
  "tags": [
    "healthcare",
    "ai_ml",
    "software",
    "medical_devices"
  ],
  "abstract": "The present application relates to a method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction, and relates to the field of deep learning. The method includes connecting layers of the encoder with layers of the decoder by skip connection to provide a U-Net type picture generator; generating a group of generative adversarial networks by matching a plurality of picture generators with a plurality of discriminators in one-to-one manner; obtaining a first multi-task learning-type generative adversarial network; designing a joint loss function 1 for improving image quality; and training the first multi-task learning-type generative adversarial network according to the joint loss function 1 in combination with an optimizer to provide a second multi-task learning-type generative adversarial network.",
  "url": "https://patentbrief.org/patent/us/11756161/method-and-system-for-generating-multi-task-learning-type-generative-adversarial",
  "markdown_url": "https://patentbrief.org/patent/us/11756161/method-and-system-for-generating-multi-task-learning-type-generative-adversarial/md",
  "google_patents_url": "https://patents.google.com/patent/US11756161",
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}