University of Minnesota

Siemens Healthcare Gmbh

The United States of America, As Represented by the Secretary, Department of Health and Human Services

Siemens Healthineers Ag

Gregory John Metzger

Kamil Ugurbil

Dingxin Wang

Xiufeng Li

Stephen C Schmechel

Joseph S Koopmeiners

Jonathan Henriksen

Ethan Yize Leng

Steen Moeller

Vibhas S Deshpande

Essa Yacoub

Christopher A Warlick

Stephen Dankbar

Chaitanya Kalavagunta

David Henry Porter

A method for generating a perfusion weighted image using arterial spin labeling (ASL) with segmented acquisitions includes dividing an anatomical area of interest into a plurality of slices and performing a multi-band (MB) echo planar imaging (EPI) acquisition process using a magnetic resonance imaging (MRI) system to acquire a control image dataset representative of the plurality of slices using a central-to-peripheral or peripheral-to-central slice acquisition order. An ASL preparation process is performed using the MRI system to magnetically label protons in arterial blood water in an area upstream from the anatomical area of interest. Following a post-labeling delay time period, the MB EPI acquisition process is performed to a labeled image dataset corresponding to the slices using the central-to-peripheral or peripheral-to-central slice acquisition order. A perfusion weighted image of the anatomical area is generated by subtracting the labeled image dataset from the control image dataset.

Slice ordering for MB-EPI ASL imaging

Medical imaging analysis systems are configured to perform automatic image registration algorithms that perform three-dimensional (3D), affine, and/or intensity-based co-registration of magnetic resonance imaging (MRI) data, such as multiparametric MRI (mpMRT) data, using mutual information (MI) as a similarity metric. An apparatus comprises a computer-readable storage medium storing a plurality of imaging series of magnetic resonance imaging (MRI) data for imaged tissue of a patient; and a processor coupled to the computer-readable storage medium. The processor is configured to receive the imaging series of MRI data; identify a volume of interest (VOI) of each image of the imaging series of MRI data; compute registration parameters for the VOIs through the maximization of mutual information of the corrected VOIs; and register the VOIs using the computed registration parameters.

Automated co-registration of prostate MRI data

Automated, machine learning-based systems are described for the analysis and annotation (i.e., detection or delineation) of prostate cancer (PCa) on histologically-stained pathology slides of prostatectomy specimens. A technical framework is described for automating the annotation of predicted PCa that is based on, for example, automated spatial alignment and colorimetric analysis of both H&E and IHC whole-slide images (WSIs). The WSIs may, as one example, be stained with a particular triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and &#x3b1;-methylacyl CoA racemase (AMACR).

Automated detection and annotation of prostate cancer on histopathology slides

Embodiments can provide a method for multi-banded RF-pulse enhanced magnetization imaging, the method comprising determining, by a processor, a frequency offset against a central frequency by specifying an offset frequency for one or more RF coils close to a frequency peak of mobile water; and simultaneously applying, by one or more RF coils, one or more bands of Gaussian RF pulses around the central frequency to a patient from a medical imaging device; wherein the one or more bands of Gaussian RF pulses are symmetrically applied having a distance from the central frequency equal to the frequency offset.

Multi-banded RF-pulse enhanced magnetization imaging

A magnetic resonance method and system are provided for providing improved multi-band (MB) magnetic resonance imaging. The adaptive MB imaging can be achieved by providing one or more modified multi-band excitation pulse sequences that include at least either one nullified 'dummy' slice within a slab that is not excited simultaneously with the other slices during a single multislice acquisition sequence, or one excitation slice group that utilizes a non-uniform slice spacing between simultaneously excited slices. Adaptive GRAPPA or slice-GRAPPA kernel sizes can also be used during image reconstruction to improve speed without excessive point spread blurring or MB reconstruction failure. A total leakage factor (TLF) can also be determined based on test images using modified MB excitation sequences, and used to improve the adaptive MB procedure.

Apparatus and method for multi-band MR imaging

A method for generating a perfusion weighted image using ASL with segmented acquisitions includes dividing an anatomical area of interest into slices and performing an EPI acquisition process using an MRI system to acquire a control image dataset representative of the slices. An ASL preparation process is performed using the MRI system to magnetically label protons in arterial blood water upstream from the anatomical area of interest. Following a first time period, a multi-band EPI acquisition process is performed using the MRI system to acquire a first labeled image dataset representative of a first subset of the slices. Following a second time period, another multi-band EPI acquisition process is performed using the MRI system to acquire a second labeled image dataset representative of a second subset of the slices. A perfusion weighted image is generated by subtracting the first and second labeled image dataset from the control image dataset.

Time efficient ASL imaging with segmented multiband acquisition

A user-independent, quantitative, multiparametric MRI model is developed and validated on co-registered correlative histopathology, yielding improved performance for cancer detection over single parameter estimators. A computing device may be configured to receive a first parametric map that maps imaged tissue of a patient using values of a first parameter, and a second parametric map that maps the imaged tissue using values of a second parameter, wherein the parametric maps are generated from medical imaging data for the imaged tissue. The computing device may be further configured to apply a multiparametric model to the maps to generate at least one Composite Biomarker Score for the tissue, the model being a function of the first parameter and the second parameter. The function may be determined based on co-registered histopathology data. The computing device may be further configured to generate an indication of whether the tissue includes predicted cancer, and output the indication.

Medical imaging device rendering predictive prostate cancer visualizations using quantitative multiparametric MRI models

A computerized method for immunohistochemistry analysis of tissue utilizes digital images of multiple adjacent tissue sections aligned within a computerized software and processed with an algorithm to quantify a two-dimensional IHC signature score for each respective slide image. In various embodiments, the IHC score is performed over several adjacent sections and further processed to produce a three-dimensional IHC quantification referred to as an IHC signature map.

Growing community of inventors

Lake Elmo, MN, United States of America

Gregory John Metzger