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 1-13C-ketoglutarate which can be used for imaging the conversion to hydroxyglutarate (HG) or Gln in cancer cells with an IDH1 mutations by hyperpolarized MRI. The ability to detect the status of IDH1 mutations is clinically prognostic for multiple cancers. These exciting observations are limited by two factors, the major one being that the natural abundance of 13C at position C5 overlaps with 1-13C-2-hydroxyglutarate peak, which limits the sensitivity of analysis and prevents simultaneous observations of HG and Gln formation.

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 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.

This technology includes the use of nanodiamonds to achieve background-free imaging. We present several techniques to reduce or eliminate background florescence by exploiting properties of the fluorescent nanodiamonds. In particular, magnetic field modulation of the fluorescence intensity offers a simple, robust, and easily adaptable method to obtain background free imaging in a variety of imaging modalities, i.e., fluorescence microscopy and wide field fluorescence animal imaging.

This technology includes a fully automated computer aided diagnosis system to quantify myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) pixel maps from the first-pass contrast-enhanced cardiac magnetic resonance (CMR) perfusion images. This system performs automated image registration, motion compensation, segmentation, and modeling to extract quantitative features from different myocardial regions of interest.

This technology includes an approach that dramatically lessens the effects of depth-dependent degradation in fluorescence microscopy images. First, we develop realistic ‘forward models’ of the depth dependent degradation and apply these forward models to shallow imaging planes that are expected to be relatively free of such degradation. In doing so, we create synthetic image planes that resemble the degradation found in deeper imaging planes. Second, we train neural networks to remove the effect of such degradation, using the shallow images as ground truth.