Leland Stanford Junior University

General Electric Company

Ge Precision Healthcare LLC

Greg Zaharchuk

John Mark Pauly

Enhao Gong

Suchandrima Banerjee

Roland Bammer

Reed Frederick Busse

Morteza Mardani Korani

Methods of performing experimental treatments on a cohort of subjects are provided. A predictive model can be utilized to predict progression of a medical disorder or relevant imaging biomarker. The predicted medical disorder progression can be utilized as a control to determine whether an experimental treatment has an effect on the progression of the medical disorder. In some instances, the enrollment of subjects within a control group for clinical experiment is eliminated or reduced.

Methods of predicting disorder progression for control arms within an experimental trial

A method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [] that has been trained using zero-contrast [] and low-contrast [] images as input to the DLN and full-contrast images [] as reference ground truth images. Prior to training, the images are pre-processed [] to co-register and normalize them. The trained DLN [] is then used to predict a synthesized full-dose contrast agent image [] from acquired zero-dose and low-dose images.

Contrast dose reduction for medical imaging using deep learning

Methods and systems for synthesizing contrast images from a quantitative acquisition are disclosed. An exemplary method includes performing a quantification scan, using a trained deep neural network to synthesize a contrast image from the quantification scan, and outputting the contrast image synthesized by the trained deep neural network. In another exemplary method, an operator can identify a target contrast type for the synthesized contrast image. A trained discriminator and classifier module determines whether the synthesized contrast image is of realistic image quality and whether the synthesized contrast image matches the target contrast type.

System and method for synthesizing magnetic resonance images

A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K×K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

Dose reduction for medical imaging using deep convolutional neural networks

A method of reducing radiation dose for radiology imaging modalities and nuclear medicine by using a convolutional network to generate a standard-dose nuclear medicine image from low-dose nuclear medicine image, where the network includes N convolution neural network (CNN) stages, where each stage includes M convolution layers having K&#xd7;K kernels, where the network further includes an encoder-decoder structure having symmetry concatenate connections between corresponding stages, downsampling using pooling and upsampling using bilinear interpolation between the stages, where the network extracts multi-scale and high-level features from the low-dose image to simulate a high-dose image, and adding concatenate connections to the low-dose image to preserve local information and resolution of the high-dose image, the high-dose image includes a dose reduction factor (DRF) equal to 1 of a radio tracer in a patient, the low-dose PET image includes a DRF of at least 4 of the radio tracer in the patient.

A method for diagnostic imaging with reduced contrast agent dose uses a deep learning network (DLN) [that has been trained using zero-contrast [and low-contrast [] images as input to the DLN and full-contrast images [as reference ground truth images. Prior to training, the images are pre-processed [to co-register and normalize them. The trained DLN [is then used to predict a synthesized full-dose contrast agent image [] from acquired zero-dose and low-dose images.

A method for diagnostic imaging includes measuring undersampled data y with a diagnostic imaging apparatus; linearly transforming the undersampled data y to obtain an initial image estimate {tilde over (x)}; applying the initial image estimate {tilde over (x)} as input to a generator network to obtain an aliasing artifact-reduced image x&#x306; as output of the generator network, where the aliasing artifact-reduced image x&#x306; is a projection onto a manifold of realistic images of the initial image estimate {tilde over (x)}; and performing an acquisition signal model projection of the aliasing artifact-reduced x&#x306; onto a space of consistent images to obtain a reconstructed image {circumflex over (x)} having suppressed image artifacts.

MRI reconstruction using deep learning, generative adversarial network and acquisition signal model

A system for magnetic resonance imaging an object via a stochastic optimization of a sampling function is provided. The system includes a magnet assembly and a controller. The magnet assembly is operative to acquire MR data from the object. The controller is operative to: acquire a first MR data set using the magnet assembly; select the sampling function from a plurality of sampling function candidates based at least in part on the stochastic optimization; and acquire a second MR data set from the object using the magnet assembly based at least in part on the sampling function.

System and method for magnetic resonance imaging an object via a stochastic optimization of a sampling function

A method of improving diagnostic and functional imaging is provided by obtaining at least two input images of a subject, using a medical imager, where each input image includes a different contrast, generating a plurality of copies of the input images using non-local mean (NLM) filtering, using an appropriately programmed computer, where each input image copy of the subject includes different spatial characteristics, obtaining at least one reference image of the subject, using the medical imager, where the reference image includes imaging characteristics that are different form the input images of the subject, training a deep network model, using data augmentation on the appropriately programmed computer, to adaptively tune model parameters to approximate the reference image from an initial set of the input and reference images, with the goal of outputting an improved quality image of other sets of low SNR low resolution images, for analysis by a physician.

Quality of medical images using multiple-contrast and deep learning

Quality of medical images using multi-contrast and deep learning

Arterial spin labeling MRI is used to provide a patient specific correction factor to correct a image provided by a non-ASL imaging modality (e.g., DSC MRI). More specifically, a first blood flow image is taken using the non-ASL imaging modality, and a corresponding second blood flow image is taken with ASL. Some or all of the voxels in the first image are selected according to a predetermined selection method. A correction factor (CF) is computed to be the ratio of second image BF to first image BF averaged over the selected voxels. Thus, CF is the average of ASL/non-ASL blood flow over the selected voxels. This correction factor is applied to all voxels of an image equally, but can differ from patient to patient. This correction can be applied to one or more non-ASL blood flow images.

Arterial spin labeling MRI-based correction factor for improving quantitative accuracy of blood flow and volume images

Methods and systems for generating T1-weighted images are provided. The method includes acquiring a pair of single-shot fast-spin-echo (SSFSE) images Sand S. The method further includes generating a T1-weighted image Sbased on Sand S.

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Stanford, CA, United States of America

Greg Zaharchuk