Method and system of computer-aided quantitative and qualitative analysis of medical images

• US 8,014,576 B2
• Filed: 11/22/2006
• Issued: 09/06/2011
• Est. Priority Date: 11/23/2005
• Status: Active Grant

• Current Assignee: Salient Imaging, Inc.
• Original Assignee: The Medipattern Corp.
• Inventors: Saghatelyan, Karen, Stapleton, Sandra, Lachmann, Frederic, Collins, Jeffrey
• Primary Examiner(s): Wu; Jingge
• Assistant Examiner(s): ANSARI, TAHMINA N
• Application Number:
  US11/603,236
• Publication Number:
  US 20070133852A1
• Time in Patent Office: 1,749 Days
• Field of Search: None
• US Class Current: 382/128
• CPC Class Codes: A61B 5/055 involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging
• CPC Class Codes: A61B 6/03 Computed tomography [CT] echo-tomography A61B8/14
• CPC Class Codes: A61B 6/5247 combining images from an ionising-radiation diagnostic technique and a non-ionising radiation diagnostic technique, e.g. X-ray and ultrasound
• CPC Class Codes: A61B 8/08 Detecting organic movements or changes, e.g. tumours, cysts, swellings A61B8/02 - A61B8/06 take precedence
• CPC Class Codes: G01R 33/5601 involving use of a contrast agent for contrast manipulation, e.g. a paramagnetic, super-paramagnetic, ferromagnetic or hyperpolarised contrast agent
• CPC Class Codes: G01R 33/5608 Data processing and visualization specially adapted for MR, e.g. for feature analysis and pattern recognition on the basis of measured MR data, segmentation of measured MR data, edge contour detection on the basis of measured MR data, for enhancing measured MR data in terms of signal-to-noise ratio by means of noise filtering or apodization, for enhancing measured MR data in terms of resolution by means for deblurring, windowing, zero filling, or generation of gray-scaled images, colour-coded images or images displaying vectors instead of pixels image data processing or generation, in general G06T
• CPC Class Codes: G01R 33/56366 Perfusion imaging
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/20036 Morphological image processing
• CPC Class Codes: G06T 2207/30068 Mammography; Breast
• CPC Class Codes: G06T 5/50 using two or more images, e.g. averaging or subtraction
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06V 10/764 using classification, e.g. of video objects
• CPC Class Codes: G06V 2201/032 of protuberances, polyps nodules, etc.

Abstract

A system and method of computer aided analysis of medical images and detection of malignant lesions is described. Medical images obtained from multiple modalities are analyzed. Morphological features as well as temporal, i.e., kinetics features, are combined to compute a consolidated assessment of a possible lesion detected in the medical images. The system includes at least one kinetics module, which is capable of extracting kinetics features from a time sequence of MRI images or MRS data taken after administering a contrast enhancement agent to a patient. The consolidated assessment is presented to a user for confirmation or modification.

Claims

1. A method of acquiring and analyzing MRS medical image data from a region in an anatomy of a patient, the method comprising the steps of:

obtaining a plurality of medical image data of the region;

identifying from the plurality of medical image data a set of data points representing a possible lesion in said region;

extracting from the plurality of medical image data features associated with said possible lesion;

computing an initial diagnostic assessment of said possible lesion from said features; and

upon said initial diagnostic assessment satisfying a pre-selected criteria, completing the steps of;

acquiring the MRS medical image data from a candidate region, selection of said candidate region being based on said initial diagnostic assessment and said candidate region being selected to include said possible lesion;

extracting biochemical characteristics from the MRS medical image data;

re-computing a consolidated assessment of said possible lesion further incorporating said biochemical characteristics in said re-computation; and

providing said consolidated assessment to a user for evaluation and modification.

2. The method of claim 1, wherein at least part of the plurality of medical image data contains temporal information responsive to administering of a contrast enhancement agent to the patient and said features includes at least one of a set of morphological features and a set of kinetics characteristics of the temporal information.

3. The method of claim 2, further comprising the step of obtaining a plurality of rules associating the set of morphological features, the set of kinetics characteristics of the temporal information, and the set of spectral characteristics with possible diagnosis, said initial assessment and said re-computed assessment being computed from said plurality of rules.

4. The method of claim 3, further comprising the steps of:

receiving from the user a modification to at least one of said morphological features, said set of kinetics characteristics and said set of spectral characteristics from the user, and

re-computing a modified assessment, said modified assessment being computed further incorporating said modification; and

providing said modified assessment to the user for further evaluation and modification.

5. The method of claim 2, wherein the kinetics characteristics are extracted from a contrast variation curve corresponding to time-dependent local contrast variation in a subset of said set of data points.

6. The method of claim 5, wherein the kinetics characteristics include a classification of the contrast variation curve into one of continued enhancement, plateau and washout types.

7. The method of claim 5, wherein the step of extracting the kinetics characteristics includes identifying an envelope enclosing the possible lesion and wherein the contrast variation curve is extracted by identifying said time-dependent local contrast variation in data points enclosed within said envelope.

8. The method of claim 1, wherein the plurality of medical image data includes medical image data acquired from at least two modalities, said re-computation further comprising the steps of:

selecting at least one modality from said at least two modalities,

for each modality of said at least one modality, computing a modality decision from features extracted from medical image data of said each modality,

computing an MRS decision from said biochemical characteristics;

correlating said MRS and said modality decisions in said re-computation.

9. The method of claim 1, wherein the biochemical characteristics include at least a concentration distribution of a marker chemical.

10. The method of claim 1, wherein the biochemical characteristics include at least relative strength of two or more marker chemicals obtained from a spectroscopic analysis.

11. The method of claim 1, wherein the plurality of medical image data includes at least a set of image data selected from the group of X-ray image data, ultrasound image data, MRI image data, CT image data, PET image data, PET/CT image data, digital tomosynthesis image data, and nuclear image data.