IMAGE ANALYSIS OF BRAIN IMAGE DATA

• US 20100260394A1
• Filed: 12/05/2008
• Published: 10/14/2010
• Est. Priority Date: 12/14/2007
• Status: Abandoned Application

• Current Assignee: Koninklijke Philips N.V.
• Original Assignee: Koninklijke Philips Electronics N.V. (Koninklijke Philips N.V.)
• Inventors: Meetz, Kirsten, Buelow, Thomas
• Application Number:
  US12/746,933
• Publication Number:
  US 20100260394A1
• Time in Patent Office: Days
• US Class Current: 382/128
• CPC Class Codes: G06T 19/00 Manipulating 3D models or images for computer graphics
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10116 X-ray image
• CPC Class Codes: G06T 2207/10132 Ultrasound image
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 2210/41 Medical
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

The present invention relates to analysis of image data, e.g. brain image data, where regions of interest are identified in patient specific image data based on non-image data. The brain image data is analyzed by correlating non-image data (20) in the form of data indicative of a neurological deficit and an object model (21) to identify one or more regions of interest (22) in the brain model, mapping the brain model to patient specific brain image data to obtain target image data (24), and identifying the one or more regions of interest in the target image data.

Claims

1. An image analysis system comprising:

an input unit (42) for receiving data (20) indicative of a deficit and for receiving image data (23) describing at least part of an object;

a storage unit (43) for storing an object model (21), where each voxel or group of voxels is associated with one or more labels (26), the one or more labels comprising an anatomical label and a deficit label;

a correlating unit (44) for correlating the data indicative of the deficit and the object model to identify one or more regions of interest (22) in the object model;

a mapping unit (49) for mapping the object model to the image data to obtain target image data (24); and

an identifying unit (400) for identifying one or more regions of interest (22) in the target image data (24).

2. The image analysis system according to claim 1, wherein the image data is brain image data, the deficit is a neurological deficit and the object is a brain.

3. The image analysis system according to claim 1, further comprising a visualization unit (402) to visualize the identified one or more regions of interest in the target image data.

4. The image analysis system according to claim 1, further being arranged for automatically performing image-based computations on at least the part of the image data pertaining to the one or more regions of interest.

5. The image analysis system according to claim 4, wherein the automatic image-based computation is customized to the image modality and/or acquisition protocol used to obtain the image data.

6. The image analysis system according to claim 4, wherein the automatic image-based computation is customized to the identified one or more regions of interest.

7. The image analysis system according to claim 1, wherein the one or more labels further comprise a functional label.

8. The image analysis system according to claim 1, further comprising a decision support system (401), wherein the decision support system receives the data indicative of the deficit, the image data, and any identified region of interest.

10. A medical image acquisition apparatus according to claim 1, further comprising an acquisition unit for acquiring image data in the form of one or more data sets.

11. A computer program product having a set of instructions for use on a computer, the instructions being arranged to cause the computer to perform the steps of claim 1.

9. A method of analyzing image data, comprising:

receiving (1) data indicative of a deficit;

receiving (5) image data describing at least part of an object;

accessing (2) an object model where each voxel or group of voxels is associated with one or more labels, the one or more labels comprising an anatomical label and a deficit label; and

correlating (3) the data indicative of the deficit and the object model to identify one or more regions of interest in the object model;

mapping (6) the object model to the image data to obtain target image data;

identifying (7) the one or more regions of interest in the target image data.