Spatial proteomics and transcriptomics are fast-emerging fields with the potential to revolutionize various branches of biology. In the last five years, various multiplex immunofluorescence and immunohistochemistry imaging methods have been developed to stain 5-60 different protein markers in a given tissue. Nonetheless, most of these techniques are iterative and can image a maximum of 3-8 markers in a single cycle, resulting in processing time of several hours to days.
This technology includes a novel method to train deep learning convolution neural network model to improve the signal-noise-ratio for the magnetic resonance (MR) imaging. The novelty lies on the fact that actual MR imaging physics information is used in the deep learning training. The resulting model achieves significant signal-to-noise ratio (SNR) improved for different acceleration factors in MR imaging. The resulting model can be used for many body anatomies (e.g., brain, heart, liver, spine, etc.) to significantly improve the SNR.
This technology includes a method for performing cardiac imaging without the need for the patient to hold their breath. Free breathing pixel-wise myocardial T1 parameter mapping includes performing a free-breathing scan of a cardiac region at a plurality of varying saturation recovery times to acquire a k-space dataset; generating an image dataset based on the k-space dataset; and performing a respiratory motion correction process on the image dataset.
This technology includes a computer-implemented method for determining magnetic field inversion time of a tissue species using a T1-mapping image, information about the region of interest, and a tissue classification algorithm. This method includes T1-mapping image comprising a plurality of T1 values within an expected range of T1 values for the tissue of interest. An image mask is created based on predetermined identification information about the tissue of interest. Next, an updated image mask is created based on a largest connected region in the image mask.
This technology includes a method to reduce scanning time while retaining high image quality during MRI scans. A reconstructed image is rendered from a set of MRI data by first estimating an image with an area which does not contain artifacts or has an artifact with a relatively small magnitude. Corresponding data elements in the estimated image and a trial image are processed, for instance by multiplication, to generate an intermediate data set.
This technology includes a novel Principal Component Analysis (PCA) based approach to correct motion related B0 changes in PRF thermometry. Proton Resonance Frequency (PRF) thermometry is a widely used Magnetic Resonance Imaging (MRI) based technique to monitor changes in tissue temperature in response to thermal therapy. The use of PRF thermometry with thermal therapy procedures is indispensable to ensure delivery of desired thermal dose to the target tissue, and to minimize unintended damage to the normal tissue.
This technology includes the incorporation of a magnetic field gradient waveform (consisting of two or more pulses) between excitation and encoding to eliminate signal from moving fluid for imaging applications. Proton Resonance Frequency (PRF) thermometry is a widely used Magnetic Resonance Imaging (MRI) based technique to monitor changes in tissue temperature in response to thermal therapy. The use of PRF thermometry with thermal therapy procedures is indispensable to ensure delivery of desired thermal dose to the target tissue, and to minimize unintended damage to the normal tissue.
This technology includes the determination of temperature dependent temporal deviations of the real from the intended gradient waveforms and k-space trajectories during MRI image acquisition, and the use of appropriate temperature dependent pre-compensations to avoid or reduce the image distortion caused by these temporal deviations on the gradient waveforms and k-space trajectories, which will improve imaging quality.
This technology includes a method of correcting the motion during T1 mapping using cardiovascular magnetic resonance imaging (MRI). Ischemic heart disease is the leading cause of death in the United States. The lack of blood supply among myocardial tissue, especially for scar regions, changes the T1 relaxation value of heart muscles. The non-invasive quantification of T1 value of myocardium (T1 mapping) is therefore of great importance for the diagnosis and treatment of cardiovascular disease.