Synarc Inc

Cerebriu A/s

Nordic Bioscience Imaging A/s

University of Copenhagen

Biomediq A/s

Nordio Bioscience A/s

Mads Nielsen

Marleen De Bruijne

Claus Christiansen

Lars A Conrad-Hansen

Francois Bernard Lauze

Jakob Raundahl

Marco Loog

Erik B Dam

Lauge Sørensen

Paola Pettersen

Akshay Sadananda Uppinakudru Pai

Arish Qazi

Morten A Karsdal

Jenny Folkesson

Martin Lillholm

Dan Richter Jørgenson

Ole F Olsen

Christian Igel

Michael Lund

Robert Megraw Lauritzen

Paola C Petterson

Bin Zou

Sami Sebastian Brandt

Gopal Raghunath Karemore

François Lauze

Lars Arne Conrad-Hansen

Francois B Lauze

There is provided a method of performing a medical scan of a subject using a medical imaging system, the method comprising: a) initiating a medical scan session; b) performing, using the medical imaging system, a first image acquisition sequence during the medical scan session to obtain first image scan data; c) performing, using a computer system, image analysis on the first image scan data acquired during said first image acquisition sequence to identify one or more quantitative indicators of pathology and/or image quality; d) based on said identification of one or more quantitative indicators at step c), determining, using the computer system, whether any additional image acquisition sequences are required during said medical scan session; and, if so required: e) determining, using the computer system, a second image acquisition sequence based on the one or more quantitative indicators; and f) performing, using the medical imaging system, a second image acquisition sequence during the medical scan session to obtain second image scan data.

Medical scan protocol for in-scanner patient data acquisition analysis

The present invention provides a method of computer analysis of a data set representing an image to extract a texture based measure therefrom, said image including a multiplicative bias in intensity within the image of unknown magnitude, the method comprising applying to said data set a bank of texture extracting filters, such that said filters are chosen from filters that are invariant to the presence of a multiplicative bias field. By providing such a method, rather than attempting to correct the bias field before extraction of a texture based-biomarker, a texture-based biomarker that is bias field invariant is extracted. This makes correction of the bias field unnecessary.

Extraction of a bias field invariant biomarker from an image

Computer analysis of data sets representing images such as MRI images to achieve bias correction and image registration, each image including a bias in intensity within the image of unknown magnitude, is performed by: a) inputting a digital data set of a first image and a digital data set of a second image into a computer; b) calculating a deformation of said first image that transforms said first image into a transformed image that is an optimized approximation of said second image and c) simultaneously calculating and applying a bias correction which is applied to said first image and a bias correction which is applied to said transformed image such that each of the first image and the transformed image is individually corrected for bias therein. Generally, an average of the bias correction over the first image is equal and opposite to an average of the bias correction over said transformed image.

Bias correction in images

The invention provides methods for automated classification of a medical diagnostic image of a lung according to its deduced probability of relating to a lung of a patient who is suffering from a diffuse parenchymal lung disease such as chronic obstructive pulmonary disease (COPD), cystic fibrosis, or severe asthma, or to a class of patients characterized by the severity of such a condition, or to a class of patients characterized by a prognostic likelihood of developing such a condition or severity of condition.

Classification of medical diagnostic images

A statistical framework for identifying regions of discrimination between two groups of imaged objects involves determining a weight map defining the regions of discrimination by minimizing the sample size required to discriminate the two groups in a numerical optimization scheme.

Optimised region of interest selection

Mammogram images are processed by computer to derive automatically a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being a parameter that changes alongside changes in breast density and is hence useful in assessing cancer risk. The method comprises the steps of processing each image of at least part of a breast by: computing for pixels of the image a quotient value representative of the aspect ratio of tissue structures depicted in the image; using a trained classifier to classify said pixels according to their respective said quotient values and assigning a score to the respective pixels representing their classification with respect to at least two classes; deriving said parameter that changes alongside changes in breast density based on the aggregate pixel membership scores of said classes. The classifier may be trained either by unsupervised learning or by supervised learning.

Breast tissue density measure

A method for the analysis of three dimensional scan data representing an articular cartilage is provided to extract a quantitative parameter indicative of joint pathology. A measure representative of cartilage homogeneity is derived from this three dimensional image data. The measured value is compared with similar measured values previously established in respect of healthy joints and/or joints characterised by a pathology.

Pathology indicating measure related to cartilage structure and automatic quantification thereof

A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Saud features may include the 3-jet of said pixels.

A method for the analysis of three dimensional scan data representing an articular cartilage is provided to extract a quantitative parameter indicative of joint pathology. A measure of local curvature of the cartilage is determined within a region of interest. The value of the quantitative parameter of this joint derived from this measure is compared with the value of a similar quantitative parameter previously established in respect of healthy joints and/or joints characterized by a pathology.

Cartilage curvature

A method of processing a mammogram image to derive a value for a parameter useful in detecting differences in breast tissue in subsequent images of the same breast or relative to a control group of such images, said derived parameter being an aggregate probability score reflecting the probability of the image being a member of a predefined class of mammogram images, comprises computing for each of a multitude of pixels within a large region of interest within the image a pixel probability score assigned by a trained statistical classifier according to the probability of said pixel belonging to an image belonging to said class, said pixel probability being calculated on the basis of a selected plurality of features of said pixels, and computing said parameter by aggregating the pixel probability scores over said region of interest. Said features may include the 3-jet of said pixels.

A method of deriving an estimate of the extent of fracture in a vertebra shown in an image of part of A spine is provided. The images of at least two vertebrae are segmented to obtain data representative of the shape and size of each of the vertebrae. An approximation of the shape of a first of the vertebrae is reconstructed by comparing the data obtained for a second of the two vertebrae with a mathematical model of at least the same two vertebrae of an unfractured spine. The unfractured shape of the first vertebra is predicted to enable a comparison of the shape and size of the first vertebra as imaged with the predicted unfractured shape and size. The difference between the respective images is subsequently computed to obtain a result representative of the extent of fracture in the first vertebra.

Vertebral fracture quantification

A method of deriving a quantitative measure of a degree of calcification of a blood vessel such as an aorta by processing an image such as an X-ray image of at least a part of the blood vessel containing said calcification comprises: taking a starting set of digital data representative of an image of at least part of a blood vessel containing a calcification set against a background; estimating the boundary of the calcification; using inpainting to replace digital data in said starting set representing the calcification with data extrapolating the boundary of the background to extend over the area of calcification, and so generating an inpainted set of digital data; and computing the difference between the starting set of digital data and the inpainted set of digital data to obtain a quantitative measure of the degree of calcification of the blood vessel.

Method of deriving a quantitative measure of a degree of calcification of an aorta

A method of deriving a quantitative measure of a degree of calcification of a blood vessel such as an aorta by processing an image such as an X-ray image of at least a part of the blood vessel containing said calcification comprises:

A method of deriving a quantitative measure of a degree of calcification of a blood vessel such as an aorta by processing an image such as an X-ray image of at least a part of the blood vessel containing said calcification comprises: taking a starting set of digital data representative of an image of at least part of a blood vessel containing a calcification set against a background;

Growing community of inventors

Dragør, Denmark

Mads Nielsen