Arterys Inc.

Calgary Scientific Inc.

Torin Arni Taerum

John Axerio-Cilies

Monroe Milas Thomas

Kyle Dormer

Hussein Patni

Darryl Bidulock

Robert George Newton

Pierre Lemire

David Bruce McFadzean

Daniel Irving Golden

Glen Lehmann

Matthieu Le

Jesse Lieman-Sifry

Matt Stephure

Jonathan Neil Draper

Michael Beauregard

Derek Scherger

Albert Hsiao

Hok Kan Lau

Sean Patrick Sall

Carla Leibowitz

Fabien Rafael David Beckers

Matthew Joseph Didonato

Shek Bun Law

Fabien Beckers

Daniel Marc Raymond Beauchamp

Anitha Priya Krishnan

Angélique Sophie Calmon

Colin John Holmes

Sonny Chan

Faramarz Famil Samavati

Mario Costa Sousa

Ross Mitchell

Giovanni De Francesco

Tristan Jugdev

Matthew Stephure

David B Mcfadzean

Pierre Joseph Lemire

This disclosure relates to a system that synchronizes the presentation of medical images in multiple contexts and methods thereof. The system includes a variety of contexts that display medical images, and the contexts are connected by communication channels. When a user edits or otherwise interacts with one of the contexts, a message is sent to other contexts via the communication channels, and the other contexts can adjust their presentation of medical images to achieve synchronization.

Systems and methods for medical image presentation in multiple contexts

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

Content based image retrieval for lesion analysis

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.

Medical imaging and efficient sharing of medical imaging information

Techniques to leverage computer monitor and graphics card hardware capabilities, using DCI-P3 and rec2020 color spaces for medical imagery are provided. The technique involves downloading the original raw source pixel data from the medical image from the webserver to the browser. A window and level transform is then applied to the raw pixel data enabling it to be displayed using the web browser capable of leveraging the hardware capabilities of commodity monitors and video cards that are capable of high bit depth display.

Systems and methods for high bit depth rendering of medical images in a web browser

Systems and methods for automated segmentation of anatomical structures, such as the human heart. The systems and methods employ convolutional neural networks (CNNs) to autonomously segment various parts of an anatomical structure represented by image data, such as 3D MRI data. The convolutional neural network utilizes two paths, a contracting path which includes convolution/pooling layers, and an expanding path which includes upsampling/convolution layers. The loss function used to validate the CNN model may specifically account for missing data, which allows for use of a larger training set. The CNN model may utilize multi-dimensional kernels (e.g., 2D, 3D, 4D, 6D), and may include various channels which encode spatial data, time data, flow data, etc. The systems and methods of the present disclosure also utilize CNNs to provide automated detection and display of landmarks in images of anatomical structures.

Automated cardiac volume segmentation

Systems and methods for providing remote access to an application program. A server remote access program may cooperate with display data interception application to provide display data to a client computing device. The client computing device may connect to the application at a Uniform Resourced Locator (URL) using a client remote access application to receive the display data. The client remote access application may provide user inputs, received at the client computing device, to the application to affect the state of the application.

Remote access to an application program

Systems and methods for determining a centerline of a tubular structure from volumetric data of vessels where a contrast agent was injected into the blood stream to enhance the imagery for centerlining. Given a 3D array of scalar values and a first and second point, the system and methods iteratively find a path from the start position to the end position that lies in the center of a tubular structure. A user interface may be provided to visually present and manipulate a centerline of the tubular structure and the tubular structure itself.

Image display of a centerline of tubular structure

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: perform error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Apparatus, methods and articles for four dimensional (4D) flow magnetic resonance imaging

Apparatus, methods and articles for four dimensional (4D) flow magnetic resonance imaging using coherency identification for magnetic resonance imaging flow data

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Calgary, Canada

Torin Arni Taerum