APPARATUS AND METHOD FOR MEDICAL IMAGE RECONSTRUCTION USING DEEP LEARNING TO IMPROVE IMAGE QUALITY IN POSITRON EMISSION TOMOGRAPHY (PET)

    公开(公告)号:US20220110600A1

    公开(公告)日:2022-04-14

    申请号:US17554019

    申请日:2021-12-17

    Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produce uniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

    APPARATUS AND METHOD FOR MEDICAL IMAGE RECONSTRUCTION USING DEEP LEARNING TO IMPROVE IMAGE QUALITY IN POSITRON EMISSION TOMOGRAPHY (PET)

    公开(公告)号:US20220104787A1

    公开(公告)日:2022-04-07

    申请号:US17554032

    申请日:2021-12-17

    Abstract: A deep learning (DL) convolution neural network (CNN) reduces noise in positron emission tomography (PET) images, and is trained using a range of noise levels for the low-quality images having high noise in the training dataset to produceuniform high-quality images having low noise, independently of the noise level of the input image. The DL-CNN network can be implemented by slicing a three-dimensional (3D) PET image into 2D slices along transaxial, coronal, and sagittal planes, using three separate 2D CNN networks for each respective plane, and averaging the outputs from these three separate 2D CNN networks. Feature-oriented training can be implemented by segmenting each training image into lesion and background regions, and, in the loss function, applying greater weights to voxels in the lesion region. Other medical images (e.g. MRI and CT) can be used to enhance resolution of the PET images and provide partial volume corrections.

    NEURAL NETWORK FOR IMPROVED PERFORMANCE OF MEDICAL IMAGING SYSTEMS

    公开(公告)号:US20220327665A1

    公开(公告)日:2022-10-13

    申请号:US17225672

    申请日:2021-04-08

    Abstract: Existing, low quality images can be restored using reconstruction or a combination of post-reconstruction techniques to generate a real patient phantom. The real patient phantom (RPP) can then be simulated in Monte Carlo simulations of a higher performance system and a lower performance system. Alternatively, the RPP can be simulated in the higher performance system, and a real scan can be performed by an existing, lower performance system. The higher performance system can be differentiated from the lower performance system in a variety of ways, including a higher resolution time of flight measurement capability, a greater sensitivity, smaller detector crystals, or less scattering. A neural network can be trained using the images produce by the higher performance system as the target, and the images produced by the lower performance system as the input. After training, the trained neural network can be used to output input images taken in a lower performance system with higher performance system characteristics.

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