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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.

Disorders of adult beta-globin synthesis, which include sickle cell disease (SCD) and beta-thalassemia, are the most common monogenic disorders in the world. While the curative potential of bone marrow transplantation has been demonstrated, this approach is limited to a small fraction of affected patients due to the requirement for an HLA-matched donor, the highly specialized approach that requires critical infrastructure, and the high cost.

This technology includes the use of thymic stromal lymphopoetin (TSLP) for the treatment of MRSA. Our studies show that mouse neutrophils express the TSLP receptor, TSLPR, and that TSLP protein is increased during cutaneous MRSA infection. Using in vitro MRSA whole blood killing assays, we show that TSLP acts on mouse neutrophils to enhance MRSA killing. In an in vivo MRSA intradermal ear infection, TSLPR-deficient mice exhibit increased MRSA burden compared to wild-type mice.

This technology includes a device and method for maintaining access to a location in the body while reducing or eliminating the potential for pulling an access device (i.e., catheter) back through an opening, such as a cardiac procedure. An introducer sheath includes a distal indented portion and a balloon, so that once placed in a desired location through tissue, the balloon can be inflated to anchor the sheath against retraction.

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 fully automatic 3D image processing system to segment the heart as well as other organs from contrast-enhanced cardiac computed tomography (CCT) images. Our method detects four cardiac chambers including left ventricle, right ventricle, left atrium, right atrium, as well as the ascending aorta and left ventricular myocardium. It also classifies noncardiac tissue structures in the CCT images such as lung, chest wall, spine, descending aorta, and liver.

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 transgenic reporter mouse that expresses a fluorescent protein called mt-Keima, to be used to detect and quantify in vivo mitophagy. This fluorescent protein was originally described by a group in Japan and shown to be able to measure both the general process of autophagy and mitophagy. We extended these results by creating a living animal so that we could get a measurement for in vivo mitophagy. Our results demonstrate that our mt-Keima mouse allows for a straightforward and practical way to quantify mitophagy in vivo.

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.