Scoring Method for Imaging-Based Detection of Vulnerable Patients

• US 20100278405A1
• Filed: 11/13/2006
• Published: 11/04/2010
• Est. Priority Date: 11/11/2005
• Status: Abandoned Application

• Current Assignee: Ioannis A. Kakadiaris, Morteza Naghavi
• Original Assignee: Ioannis A. Kakadiaris, Morteza Naghavi
• Inventors: Kakadiaris, Ioannis A., Naghavi, Morteza
• Application Number:
  US12/084,757
• Publication Number:
  US 20100278405A1
• Time in Patent Office: Days
• US Class Current: 382/131
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

A new cardiac risk factors are disclosed along with method for deriving the components of the factors, for developing the factors and for using the factors. Methods for computing pericardial fat and abdominal fat are also disclosed as well as methods for motion compensation.

Claims

1. A method for ranking patients based on a scoring rate derived from imaging-based data comprising:

obtaining images of a patient,

obtaining data from the images, where the data includes fat data, plaque data, and/or data on other physiologically image-discernible structures selected from the group consisting of vasa vasorum and other micro-vascularizations or micro-structures,

determining a risk factor from the data based on the data.

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11. (canceled)

12. A scoring index given by the formula:

13. The index of claim 12, wherein the coronary calcium sub-score given by the formula:

14. The index of claim 12, wherein the non-coronary calcium sub-score given by the formula:

15. The index of claim 12, wherein the non-cardiac calcium sub-score given by the formula:

16. The index of claim 12, wherein the non-cardiac calcium sub-score given by the formula:

17. The index of claim 12, wherein the non-cardiac calcium sub-score given by the formula:

18. A scoring index given by the formula:

19. A scoring index given by the formula:

20. A method for motion compensation comprising the steps of:

determining translation and orientation parameters q_c and q_R;

estimating the global reference shape parameters q_e

estimating the motion parameters q_Ti.

21. A method for determining pericardial fat comprising the steps of:

estimating object-specific (fat, non-fat, background classes) distributions using a training data set by computing relevant intensity and texture features and computing the most discriminating features;

removing artifacts and find the contour outline of the human body;

removing equipment-related artifacts;

finding a outline of a human body automatically for processing data inside this contour;

using anatomical landmark information to locate upper and lower limits of a heart;

segmenting an inner-thoracic cavity using radial gradient sampling in each slice;

segmenting lungs in each slice;

segmenting the heart and descending aorta in each slice;

updating a 3D label map of the various organs in a volume;

computing a seed point for the fat region in the center slice;

computing statistics dynamically for a sample region around the seed point;

computing most discriminating features selected from Step 2;

computing a global object affinity using the Mahalanobis metric;

computing fat by thresholding the global object affinity image; and

using labels determined in Step 9 to quantify pericardial fat.

22. A method for determining pericardial fat comprising the steps of:

estimating tissue-specific distributions using a training data set by Step 1;

computing relevant intensity and texture features for pixels in labeled region of interest (ROI), Step 2;

normalizing individual features, Step 3;

ranking individual features according to their relevance to classification, Step 4;

selecting an optimal feature set, and Step 5;

computing model parameters for classifiers

removing artifacts by (Step

     6) removing equipment-related artifacts such as table and wires and determine the heart region using an ontology map, using determine upper and lower limits of heart, (Step

     7) finding an outline of an human body automatically in each slice, (Step

     8) segmenting an inner-thoracic cavity in each slice, (Step

     9) segmenting the lungs in each slice, (Step

     10) segmenting the heart and the descending aorta in each slice, (Step

     11) updating the ontology map for every tissue/organ in each slice using labels obtained by segmentation in steps 7-11,

segmenting fat by (Step

     12) computing a binary map using SVM classifiers for all tissue/organ class combinations, (Step

     13) computing an Error COrrecting Code (ECOC) [60] for each object, (Step

     14) computing output of all classifiers using Hamming distance, and (Step

     15) combining ontology map information (steps 5-11) and steps 12-14 to quantify pericardial fat.

23. A method for determining abdominal fat comprising the steps of:

estimating object-specific feature distributions using a training data set by;

Step 1 computing relevant features for domain specific objects, Step 2;

computing the most discriminant features, Step 3;

constructing a template (mean shape) using the landmark points in the training images for the seed region (Not applicable in all domains),

initializing the target object seed region by Step 4;

computing the target object seed pixel using do-main specific knowledge,

computing the fuzzy connectedness-based object by Step 5;

computing global class affinity for a given spel, Step 6;

If the spel is not determined to be a member of non-target objects, then compute local fuzzy affinity, Step 7;

computing the global object affinity, and Step 8;

computing the fuzzy extent of the target object.

24. A method for determining abdominal fat comprising the steps of

estimating object-specific (fat, non-fat, background classes) distributions using a training data set by Step 1:

computing relevant intensity and texture features, Step 2;

computing the most discriminant features;

constructing a template using training data set. Step 3;

Construct a subcutaneous fat template using the Active Shape Model (ASM) framework [1];

removing artifacts and initialize the target object seed region by Step 4;

removing equipment-related artifacts, and Step 5;

initializing the seed point automatically using the subcutaneous fat template;

computing the fuzzy affinity-based object by Step 6;

computing the most discriminating features selected from Step 2 of training, Step 7;

computing the global object affinity using the Mahalanobis metric, and Step 8;

computing the fat by thresholding the global object affinity image.

25. A scoring index derived from imaging data comprising:

coronary calcium data including;

overall coronary calcium density data,

coronary calcium distribution data,

coronary calcium location data,

coronary calcium shape data,

coronary calcium size data,

coronary calcium structural data and/or

coronary calcium pattern data, and

optionally

overall coronary plaque density,

coronary plaque distribution data,

coronary plaque location data,

coronary plaque shape data,

coronary plaque size data,

coronary plaque structural data and/or coronary plaque pattern data.

26. The index of claim 25, further comprising:

non-coronary data including;

non-coronary calcium data,

non-coronary plaque data,

heart structural data,

microvascularization data, and/or

pericardial fat data, and

optionally

overall non-coronary arterial and/or veinal calcium density data,

non-coronary arterial and/or veinal calcium distribution data,

non-coronary arterial and/or veinal calcium location data,

non-coronary arterial and/or veinal calcium shape data,

non-coronary arterial and/or veinal calcium size data,

non-coronary arterial and/or veinal calcium structural data, and/or

non-coronary calcium pattern data;

where the non-coronary plaque data comprises;

overall non-coronary arterial and/or veinal plaque density,

non-coronary arterial and/or veinal plaque distribution data,

non-coronary arterial and/or veinal plaque location data,

non-coronary arterial and/or veinal plaque shape data,

non-coronary arterial and/or veinal plaque size data,

non-coronary arterial and/or veinal plaque structural data and/or

non-coronary arterial and/or veinal plaque pattern data;

where the heart structural data comprises;

muscle thickness, and/or

valve structure;

where the microvascularization data comprises;

overall cardial microvascularization density,

cardial microvascularization distribution data,

cardial microvascularization location data,

cardial microvascularization shape data,

cardial microvascularization size data,

cardial microvascularization structural data and/or

cardial microvascularization pattern data;

where the pericardial fat data comprises;

overall pericardial fat density data,

pericardial fat distribution data,

pericardial fat location data,

pericardial fat shape data,

pericardial fat size data,

pericardial fat structural, and/or

pericardial fat data.

27. The index of claim 26, further comprising:

non-cardiac data including;

non-cardiac arterial and/or veinal calcium data,

non-cardiac arterial and/or veinal plaque data,

organ structural data,

non-cardiac microvascularization data, and/or

non-cardiac fat data,

where the non-cardiac arterial and/or veinal calcium data includes;

overall non-cardiac arterial and/or veinal calcium density data,

non-cardiac arterial and/or veinal calcium distribution data,

non-cardiac arterial and/or veinal calcium location data,

non-cardiac arterial and/or veinal calcium shape data,

non-cardiac arterial and/or veinal calcium size data,

non-cardiac arterial and/or veinal calcium structural data, and/or

non-cardiac arterial and/or veinal calcium pattern data,

where the non-cardiac arterial and/or veinal plaque data includes;

overall non-cardiac arterial and/or veinal plaque density,

non-cardiac arterial and/or veinal plaque distribution data,

non-cardiac arterial and/or veinal plaque location data,

non-cardiac arterial and/or veinal plaque shape data,

non-cardiac arterial and/or veinal plaque size data,

non-cardiac arterial and/or veinal plaque structural data and/or

non-cardiac arterial and/or veinal plaque pattern data,

where the organ structural data includes;

gross morphologic data,

histological data,

defect data, and/or

abnormality data,

where the non-cardiac microvascularization data includes;

overall non-cardiac microvascularization density,

non-cardiac microvascularization distribution data,

non-cardiac microvascularization location data,

non-cardiac microvascularization shape data,

non-cardiac microvascularization size data,

non-cardiac microvascularization structural data and/or

non-cardiac microvascularization pattern data, and

where the non-cardiac fat data includes;

overall epicardial, thoracic and/or visceral fat density data,

epicardial, thoracic and/or visceral fat distribution data,

epicardial, thoracic and/or visceral fat location data,

epicardial, thoracic and/or visceral fat shape data,

epicardial, thoracic and/or visceral fat size data,

epicardial, thoracic and/or visceral fat structural data, and/or

epicardial, thoracic and/or visceral fat data.

28. The index of claim 27, further comprising:

non-hip-to-lip data including;

overall muscle structure,

overall tendon and ligament structure,

bone density and structure,

neurological density and structure, and/or

cancer density, location and structure.

29. The scoring index of claim 28, further comprising:

general patient data including;

age, weight,

height,

medical history,

family medical history,

education,

eating habits,

diet, and/or

exercise.