Systems and methods for automated detection in magnetic resonance images

Abstract

Some aspects include a method of detecting change in degree of midline shift in a brain of a patient. While the patient remains positioned within the low-field magnetic resonance imaging device, acquiring first magnetic resonance (MR) image data and second MR image data of the patient's brain; providing the first and second MR data as input to a trained statistical classifier to obtain corresponding first and second output, identifying, from the first output, at least one initial location of at least one landmark associated with at least one midline structure of the patient's brain; identifying, from the second output, at least one updated location of the at least one landmark; and determining a degree of change in the midline shift using the at least one initial location of the at least one landmark and the at least one updated location of the at least one landmark.

Description

[0001] Cross References to Related Applications [0002] This application claims the benefit under 35 U.S.C. §119(e) of U.S. Provisional Application Serial No. 62 / 425,569, filed November 22, 2016, entitled "CHANGEDETECTION METHODS AND APPARATUS," which is incorporated by reference in its The entire contents are incorporated herein. Background technique [0003] Magnetic resonance imaging (MRI) provides an important imaging modality for many applications and is widely used in clinical and research settings to produce images of the interior of the human body. MRI is based on detecting magnetic resonance (MR) signals, which are electromagnetic waves emitted by atoms in response to state changes caused by applied electromagnetic fields. For example, nuclear magnetic resonance (NMR) techniques involve the detection of MR signals emitted from the nuclei of excited atoms upon rearrangement or relaxation of the nuclear spins of atoms in an object being imaged (e.g., atoms in human ti...

AI Technical Summary

Problems solved by technology

However, conventional MRI techniques suffer from a number of disadvantages, which for a given imaging application can include relatively high cost of equipment, limited availability (e.g., difficulty and expense of obtaining access to a clinical MRI scanner), image acquisition process the length of

The high cost and substantial space requirements of high-field MRI systems lead to limited availability of MRI scanners

Consequently, there are often clinical situations where an MRI scan would be beneficial, but is impractical or impossible due to the above limitations and as discussed in further detail below

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