Healthcare
❯
Image analysis
❯Medical Assistants
❯Generation of computer tomography scans from magnetic resonance images
Generation of computer tomography scans from magnetic resonance images
For:
Oncology hospitals, oncologists
Scope:
Train a model that generates CT images from MRI scans. Synthetic CT images may be used for radiation dose calculation in radiation therapy.
Goal:
Improved Employee Efficiency
Accelerated acquisition of magnetic resonance images
For:
Radiology departments, MRI vendors
Scope:
Innovations in MRI image formation
Goal:
Improved Employee Efficiency
AI platform for chest CT-scan analysis (early stage lung cancer detection)
For:
Healthcare authorities
Scope:
Detecting malignant neoplasms (lungs) on chest CT-scans
Goal:
Anticipate Risks, Improved Employee Efficiency
AI solution for end-to-end processing of cell microscopy images
For:
Biochemical, metabolomics and imaging branches of biomedicine
Scope:
Restoration of naturally distorted microscopy images for following visualization and analysis of meaningful patterns of protein formation inside living cells.
Goal:
Improved Product Development / R&D
Computer-aided diagnosis in medical imaging based on machine learning
Scope:
Detecting image anomalies.
Goal:
Improve Operation Efficiency
Generation of computer tomography scans from magnetic resonance images
For:
Oncology hospitals, oncologistsGoal:
Improved Employee EfficiencyProblem addressed
Generation of a CT image from a given MRI image.
Scope of use case
Train a model that generates CT images from MRI scans. Synthetic CT images may be used for radiation dose calculation in radiation therapy.
Description
In this project, we investigate approaches to generating
synthetic computed tomography (CT) images from the real
magnetic resonance imaging (MRI) data. Generating
radiological scans has grown in popularity in recent years
due to its promise to enable single-modality radiotherapy
planning in clinical oncology, where the co-registration of
the radiological modalities is cumbersome. We rely on
generative adversarial network (GAN) models with cycle
consistency, which permit unpaired image-to-image
translation between the modalities. We also introduce the
perceptual loss function term and the coordinate
convolutional layer to further enhance the quality of
translated images. The Unsharp masking and the super-
resolution GAN (SRGAN) were considered to improve the
quality of synthetic images. The proposed architectures were
trained on unpaired MRI-CT data and then evaluated on a
paired brain dataset. The resulting CT scans were generated
with a mean absolute error (MAE), a peak signal-to-noise
ratio (PSNR) and structural similarity (SSIM) scores of 60,83
HU, 17,21 dB, and 0,8, respectively. DualGAN, with
perceptual loss function term and coordinated convolutional
layer, proved to perform best. The MRI-CT translation
approach holds the potential to eliminate the need for the
patients to undergo both examinations and to be clinically
accepted as a new tool for radiotherapy planning.
Machine Learning
AI: Understand