The Medical College of Wisconsin, Inc.

New York Society for the Relief of the Ruptured and Crippled, Maintaining the Hospital for Special Surgery

New York Society for the Ruptured and Crippled Maintaining the Hospital for Special Surgery

Andrew S Nencka

Kevin Matthew Koch

James Stewart Hyde

Andrzej Jesmanowicz

Eric S Paulson

Matthew F Koff

Hollis Potter

Lutfi Tugan Muftuler

Daniel Shefchik

Ali Bakhshinejad

Robin A Karr

Juan Liu

Bradley J Swearingen

Systems and methods for training and implementing a machine learning algorithm to generate feature maps depicting spatial patterns of features associated with osteolysis, synovitis, or both. MRI data, including multispectral imaging data, are input to the trained machine learning algorithm to generate the feature maps, which may indicate features such as a location and probability of a pathology classification, a severity of synovitis, a type of synovitis, a synovial membrane thickness, and other features associated with osteolysis or synovitis. In some implementations, synovial anatomy are segmented in the MRI data before inputting the MRI data to the machine learning algorithm. These segmented MRI data may be generated using another trained machine learning algorithm.

Neural network classification of osteolysis and synovitis near metal implants

Aneurysms are classified and quantitatively analyzed based on medical image data acquired from a subject. In general, one or more algorithms are implemented to automatically classify, or otherwise diagnose, and measure aneurysms and their change over time. These algorithms make use of artificial intelligence and deep learning to develop quantitative analytics that can be consolidated into diagnostic reports.

Systems and methods for generating classifying and quantitative analysis reports of aneurysms from medical image data

Systems and methods for designing and fabricating three-dimensional objects with precisely computed material compositions for use in enhancing electromagnetic fields for magnetic resonance imaging ('MRI') are provided. As examples, the fabricated object can be designed to reduce magnetic field inhomogeneities in the main magnetic field of an MRI system, or to reduce inhomogeneities in a transmit radio frequency ('RF') field (i.e., a Bfield). As examples, the object can be a shim; a housing or other part of an RF coil; a medical device, such as a surgical implant; or component used in a medical device, such as a housing for an implantable medical device.

System and method for fabricating electromagnetic field enhancing objects for magnetic resonance imaging

Systems and methods for quantitative susceptibility mapping ('QSM') using magnetic resonance imaging ('MRI') are described. Localized magnetic field information is used when performing the inversion to compute quantitative susceptibility maps. The localized magnetic field information can include multi-resolution subvolumes obtained by segmenting, or dividing, a field shift map. In some instances, a trained machine learning algorithm, such as a trained neural network, can be implemented to convert the localized magnetic field information into quantitative susceptibility data. These local susceptibility maps can be combined to form a composite quantitative susceptibility map of the imaging volume.

Multi-resolution quantitative susceptibility mapping with magnetic resonance imaging

A magnetic resonance imaging (MRI) system can include a magnetic resonance imaging (MRI) scanner, having a plurality of radio frequency (RF) receivers, and a processor. The MRI scanner can perform a full field of view (fFOV) scan on an anatomy area including an implant to acquire first multi-spectral MRI data associated with a plurality of frequency bins. The processor can generate, for each pair of a single RF receiver and a single frequency bin, a respective spectral sensitivity map using at least a portion of the fFOV multi-spectral MRI data. The MRI scanner can perform a reduced FOV (rFOV) scan to acquire second multi-spectral MRI data associated with the plurality of frequency bins. The processor can reconstruct one or more MRI images according to the rFOV using the rFOV multi-spectral MRI data and the spectral sensitivity maps.

Spectral bin un-aliasing for reduced field-of-view magnetic resonance imaging near metal implants

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Greendale, WI, United States of America

Andrew S Nencka