Mcgill University

Universitè Laval

Other

Simon Duchesne

D. Louis Collins

9346-6621 Québec Inc. D/b/a Positive Degree

Louis Collins

Nicolas Robitaille

Pierre Gravel

Fernando Valdivia

Burt Crépault

D Louis Collins

Dominique Duchesne

A computer-implemented method, an apparatus, and a system for estimating synthetic values of quantitative metrics are provided. They involve calculating new, more accurate boundaries using a classifier based on local intensity and spatial estimators, for the segmentation mask provided by a non-local means patch-based segmentation in a test image, and estimating for the pixels of interest at least one synthetic value of a quantitative metric using a given value of the quantitative metric assigned to the reference images and the boundaries. The method, apparatus, and system provide the advantage of generating synthetic values directly comparable against known values for given subjects or against predetermined scales for diagnostic or prognostic purposes. In the specific case of Alzheimer's disease, the invention stretches the predictive range up to two full decades, which constitutes a significant advance in the field of medical diagnostics.

System and method for estimating synthetic quantitative health values from medical images

Applicants have discovered a multi-layer quality control/quality assurance system that provides higher quality data, without human intervention, by rejecting images that do not achieve a pre-determined quality threshold. In some embodiments of the present invention, there is provided a new method of processing an image from a data set through a pipeline wherein quality control/assurance allows to determine a quality of the image processing, the method comprising; receiving a test image; pre-processing the test image; registering the test image to a reference image; calculating a test image quality using a correlation of image intensity values between corresponding locations of the test image and the reference image; providing a plurality of training images and calculating training image quality distribution statistics for the training images with respect to the reference image; and relating the test image quality to the training image quality distribution.

Quality control and assurance of images

Intensity standardization of MRI data sets aims at correcting scanner-dependent intensity variations. An automatic technique, called STI, which shares the simplicity and robustness of histogram-matching techniques, but also incorporates tissue spatial intensity information, has been discovered. The method comprises registering a medical image to a standard image; applying one or more masks to the medical and standard images for isolating certain specific image components; determining the most common intensity data pair between the medical and standard images for each isolated image component; calculating a formula that joins the most common intensity data pair of each image component; and interpolating an intensity data adjustment using the formula and applying it to the medical image data to generate a standardized version of the medical image.

System and method for medical image intensity standardization

There is provided a method for predicting a clinical state of a subject based on image data obtained from a Volume Of Interest in the subject. The method comprise the establishment of a predictive model that relates image features and the future evolution of a clinical state.

Systems and methods of clinical state prediction utilizing medical image data

A method of classifying a test subject comprises collecting imaging data for a plurality of training subjects, control subjects and a test subject. An intensity volume of interest (VOI) and a morphological VOI are selected from said imaging data. Training intensity data and morphological data are calculated for the intensity and spatial VOI. A statistical model can then be created based on the training intensity data and training spatial data to provide a universe of subjects. Control intensity data and spatial data are also calculated for the intensity and spatial VOI. A classifier can then be built dividing the universe into at least two regions. The test subject data can then be applied to the classifier to provide a determination of whether the test subject falls within the first region or the second region. The condition can be a neurological disease state such as temporal lobe epilepsy or Alzheimer's dementia.

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Simon Duchesne