IDF No 2297 Method for Image Reconstruction using Unsupervised Deep Learning and System thereof

Method for Image Reconstruction using Unsupervised Deep Learning and System thereof

Categories for this Invention

Technology: Image reconstruction using unsupervised deep learning techniques;

Industry & Application: Biomedical Engineering, Healthcare Industries, Magnetic Resonance Imaging(MRI) units, Medical Device;

Market: The global 3D reconstruction technology market is projected to grow at a CAGR of 11.6% during 2024-2029.

Image Gallery

Problem Statement

  • In the present era, various techniques like nuclear imaging, magnetic resonance imaging, computerized tomography scan which may be used to obtain images of internal structures of objects or patients.
  • However, these techniques subject to various trade-offs between speed, efficiency & quality of reconstruction.
  • Hence, there is a need to address said issues.

Technology

  • Present Invention explains about a system & method for image reconstruction using fully unsupervised deep learning techniques.
  • Further it explains that a system of one or more computers can be configured to perform particular operations or actions by virtue of having software, firmware, hardware, or a combination of them installed on the system that in operation causes or cause the system to perform the actions.. like a method for training a neural network for image reconstruction.
  • The method includes the following steps depicted in the smart chart hereinbelow:

STEP 1

  • Calculating loss function (L(X,X′)) & projection data error (X-X′) based on actual projection data (X) & reference reconstructed projection data (X′);

STEP 2

  • Updating one or more parameters of the neural network based on the calculated loss function (L(X,X′));

STEP 3

  • Reconstructing an image (Y′) by processing the actual projection data error (X-X′) using the one or more updated neural network parameters;

STEP 4

  • Transforming the reconstructed image (Y′) into new reference reconstructed projection data (X′);

STEP 5

  • Iteratively performing steps S1-S4 for a predefined number of cycle for calculating an optimum loss function (L(X,X′));

STEP 6

  • Generating one or more optimum parameters of the neural network using the optimum loss function (L(X,X′));

STEP 7

  • Updating the neural network with the one or more optimum parameters, & Transforming the reconstructed image (Y′) by processing the projection data (X) using a trained neural network.

Key Features / Value Proposition

Technical Perspective:

  • Facilitates a software framework for image reconstruction by combing the Deep Learning (DL) & the Iterative Reconstruction (IR) techniques.
  • Provide fast, fully unsupervised & robust image reconstruction technique.
  • Advantageous to reconstruct tomographic images without any noise/blur artifacts & allows reconstruction from the truncated data without the need for prior truncation correction.
  • The present techniques do not restrict the solution space by using regularization term in the loss function.

Industrial Perspective:

  • Efficient cost-effective solution and applicable in the medical imaging system to reconstruct the image.
  • Provide speedy solution.
  • Facilitates high quality of reconstructed image as shown in fig 2.
  • Easily installed on the system that in operation causes the system to perform the action of reconstruction of image.

Questions about this Technology?

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Contact For Licensing

sm-marketing@imail.iitm.ac.in
ipoffice2@iitm.ac.in

Research Lab

Prof. Balaji Srinivasan

Department of  Mechanical Engineering,

Prof. Ganapathy Krishnamurthi

Department of  Engineering Design

Intellectual Property

  • IITM IDF Ref. 2297

  • IN Patent No: 485152

Technology Readiness Level

TRL-4

Proof of Concept ready, tested in lab.

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