Methods of Predicting Patient Treatment Response and Resistance via Single-Cell Transcriptomics of Their Tumors

Tailoring the best treatments to cancer patients remains a highly important endeavor in the oncology field. However, personalized treatment courses are challenging to determine, and technologies or methods that can successfully be employed for precision oncology are lacking.

Biomarker Analysis Software for High-Throughput Diagnostic Multiplex Data

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that are released from cells. EVs may contain proteins derived from their cells of origin with the potential as diagnostic biomarkers indicating the state of the cells when released. However, due to their small size (50-1000nm), the methods currently used to phenotype EVs have limited sensitivity and scale. A need exists for development of novel technologies improving EV detection and phenotyping.

DeePlexing – Extending Imaging Multiplexity Using Machine Learning

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.

A Machine Learning Strategy to Improve the Fidelity of Imaging Time-Varying Signals to Improve Clinical Imaging

This technology includes a new technique to improve the fidelity of time-varying signals acquired in the dynamic contrast enhanced (DCE) imaging. This technique enhances the time-varying signals in a given DCE image series through deep convolutional neural networks (CNN) to learn the relationship of signal versus contrast concentration from other series of different contrast doses.

Single Scan Bright-blood and Dark-blood Phase Sensitive Inversion Recovery (PSIR) Late Gadolinium Enhancement (LGE) for Cardiovascular Magnetic Resonance (CMR) Imaging

This technology includes a technique to improves detection of myocardial scar compared with conventional bright-blood late gadolinium enhancement (LGE) techniques. Dark-blood late gadolinium enhancement (DB-LGE) improves tissue delineation with signal suppression of the blood pool based on T2-preparation pulse that is relatively independent from the blood flow velocities and improves scar detection in patients with known or suspected coronary artery disease.

Compatible 3-D Intracardiac Echography Catheter and System for Interventional Cardiac Procedures

This technology includes a versatile intravascular 3D intracardiac echocardiography (ICE) catheter that can operate under conventional X-ray and MRI for use during interventional cardiac procedures. The 3D MRICE and custom, GPU-based, real-time imaging system are also included. Structural heart disease affects more than 2.9% of the US population, and common interventional procedures can be difficult because of limitations in catheter devices and inadequate image guidance.

Free Breathing Motion Corrected Pixel-wise MRI Myocardial T1 Parameter Mapping for Clinical Cardiac Imaging

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.

System for Automated Anatomical Structures Segmentation of Contrast-Enhanced Cardiac Computed Tomography Images

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.

Methods and Systems for Automatically Determining Magnetic Field Inversion Time of a Tissue Species

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.

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