# AI Method for Sharpening Low-Dose PET Scans with Multi-Task Learning > 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. - **Patent:** US 11756161 - **Original title:** Method and system for generating multi-task learning-type generative adversarial network for low-dose PET reconstruction - **Owner:** Shenzhen Institutes of Advanced Technology - **Granted:** 2023 - **Status:** Active - **Times cited:** 0 - **Field:** healthcare, ai_ml, software, medical_devices ## 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). ## The clever bit The novelty lies in combining multiple "U-Net type picture generators" with "discriminators" into a "multi-task learning-type generative adversarial network," where these networks learn in parallel and share information. This parallel learning, guided by a specific "joint loss function" that considers both L1 and cross-entropy losses, allows the system to effectively reconstruct high-quality images from very low-dose PET data, leveraging additional MR image input. ## Real-world examples 1. Medical imaging software for PET scanners 2. AI-powered diagnostic tools in oncology 3. Radiology workstations 4. Research platforms for medical image enhancement ## Why it matters Positron Emission Tomography (PET) scans are crucial for diagnosing diseases like cancer, but they expose patients to radiation. This patent matters because it offers a way to get high-quality images from lower radiation doses, making PET scans safer and potentially more accessible. By improving image quality from low-dose data, it could lead to earlier and more accurate diagnoses with reduced patient risk. ## Frequently asked questions ### What does AI Method for Sharpening Low-Dose PET Scans with Multi-Task Learning cover? 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. ### Who owns patent US 11756161? Shenzhen Institutes of Advanced Technology owns this patent, granted in 2023. ### When does this patent expire? This patent is expected to expire on June 7, 2041, when the invention enters the public domain. ### What problem does this patent solve? Positron Emission Tomography (PET) scans are crucial for diagnosing diseases like cancer, but they expose patients to radiation. This patent matters because it offers a way to get high-quality images from lower radiation doses, making PET scans safer and potentially more accessible. By improving image quality from low-dose data, it could lead to earlier and more accurate diagnoses with reduced patient risk. ### What does this patent NOT cover? Does not cover methods for reconstructing PET images without using a "U-Net type picture generator" with "skip connections" (Claim 1). **Full plain-English explainer:** https://patentbrief.org/patent/us/11756161/method-and-system-for-generating-multi-task-learning-type-generative-adversarial **Original patent:** https://patents.google.com/patent/US11756161 --- _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: - [How AI Reconstructs Clearer MRI Scans Using Progressive Networks](https://patentbrief.org/patent/us/11422217/progressive-generative-adversarial-network-in-medical-image-reconstruction) — 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. - [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. - [How an AI Learns to Recognize Objects by Generating Its Own Examples](https://patentbrief.org/patent/us/12154036/enhanced-generative-adversarial-network-and-target-sample-recognition-method) — This patent describes an enhanced artificial intelligence system that uses two competing networks, a generator and a discriminator, with special 'capsule layers' to create and recognize specific types of data, like images, even with limited initial examples. - [AI Predicts Prostate Cancer Spread from Medical Scans](https://patentbrief.org/patent/us/12737884/systems-and-methods-for-ai-assisted-analysis-of-primary-tumor-images-for) — This patent describes how artificial intelligence can analyze medical scans of a prostate tumor to automatically identify suspect regions and predict if the cancer will spread to other parts of the body.