Method for automated MR imaging and analysis of the neuroaxis

• US 8,457,377 B2
• Filed: 07/25/2011
• Issued: 06/04/2013
• Est. Priority Date: 03/11/2004
• Status: Active Grant

• Current Assignee: ABSIST, LLC
• Original Assignee: ABSIST, LLC
• Inventors: Weiss, Kenneth L.
• Primary Examiner(s): Mehta, Bhavesh
• Assistant Examiner(s): ANSARI, TAHMINA N
• Application Number:
  US13/136,165
• Publication Number:
  US 20120020538A1
• Time in Patent Office: 680 Days
• Field of Search: None
• US Class Current: 382/128
• CPC Class Codes: A61B 5/0036 including treatment, e.g., using an implantable medical device, ablating, ventilating
• CPC Class Codes: A61B 5/0042 for the brain
• CPC Class Codes: A61B 5/0263 using NMR
• CPC Class Codes: B60R 25/00 Fittings or systems for preventing or indicating unauthorised use or theft of vehicles locks for vehicles E05B77/00-E05B85/00
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10072 Tomographic images
• CPC Class Codes: G06T 2207/10081 Computed x-ray tomography [CT]
• CPC Class Codes: G06T 2207/10088 Magnetic resonance imaging [MRI]
• CPC Class Codes: G06T 2207/20101 Interactive definition of point of interest, landmark or seed
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 2207/30004 Biomedical image processing
• CPC Class Codes: G06T 2207/30012 Spine; Backbone
• CPC Class Codes: G06T 2207/30016 Brain
• CPC Class Codes: G06T 7/0012 Biomedical image inspection
• CPC Class Codes: G06T 7/11 Region-based segmentation
• CPC Class Codes: G06V 2201/033 of skeletal patterns
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 30/40 for processing medical images, e.g. editing
• CPC Class Codes: G16H 70/60 relating to pathologies
• CPC Class Codes: H04L 2012/40273 the transportation system being a vehicle
• CPC Class Codes: Y10T 428/24 : Structurally defined web or sheet [e.g., overall dimension, etc.]
• CPC Class Codes: Y10T 428/24744 : Longitudinal or transverse tubular cavity or cell

Abstract

Automated spine localizing, numbering and autoprescription system enhances correct location or diseased or injured tissue, even allow multi-spectral diagnosis. Externally located this tissue is facilitated by an integrated self adhesive spatial reference and skin marking system that is designed for a variety of modalities to include MRI, CT, SPECT, PET, planar nuclear imaging, radiography, XRT, thermography, optical imaging and 3D space tracking. The device ranges from a point localizer to a more multifunctional and complex grid/phantom system. The specially designed spatial reference(s) is affixed to an adhesive strip with corresponding markings so that after applying the unit to the skin/surface and imaging, the reference can be removed leaving the skin appropriately marked. The localizer itself can also directly adhere to the skin after being detached from the underlying strip. A spine autoprescription process performs image analysis that is able to identify vertebrae and discs even in the presence of abnormalities.

Claims

1. A method comprising:

(a) acquiring a plurality of medical diagnostic images of a neuro-axis of a patient with fast sagittal MRI opposed-phase fast gradient recalled echo sequences having a total acquisition time of less than one minute, wherein the parameters of the sequences are selected so that discs have the local intensity maxima amongst spinal structures; and

(b) executing a program, which is stored in a computer memory, by a processor for;

(i) generating a composite midline sagittal image volume of the neuro-axis by combining two or more medical diagnostic images from the plurality of medical diagnostic images, wherein the generated composite midline sagittal image volume includes at least a portion of all interspaces and vertebrae between the patient'"'"'s axis (C-2 vertebra) and sacrum; and

,

(ii) automatically identifying a plurality of spinal structures in the composite midline sagittal image volume by executing an algorithm which identifies spinal structures based on signal intensity in the composite midline sagittal image volume.

2. A method comprising:

(a) acquiring a plurality of medical diagnostic images of a neuro-axis of a patient using multi-parametric derivable MRI sequences;

(b) executing a program, which is stored in a computer memory, by a processor for;

generating a composite midline sagittal image volume of the neuro-axis by combining two or more medical diagnostic images from the plurality of medical diagnostic images, wherein the generated composite midline sagittal image volume includes at least a portion of all interspaces and vertebrae between the patient'"'"'s axis (C-2 vertebra) and sacrum; and

,

(ii) automatically identifying a plurality of spinal structures in the composite midline sagittal image volume using an iterative process; and

(iii) using the identification of the plurality of spinal structures, and at least one parameter map derived from the plurality of medical diagnostic images, automatically assess the patient for at least one neuro-axis pathology.

3. The method of claim 2, wherein the at least one neuro-axis pathology comprises a pathology selected from the list consisting of:

(a) fractures;

(b) osteoporosis;

(c) metastatic disease; and

(d) degenerative disc disease;

and wherein the at least one parameter map is taken from the list consisting of;

(i) a T2 map describing transverse signal relaxation times effecting image contrast;

(ii) a T2* map describing signal relaxation caused by magnetic field inhomogeneities;

(iii) a T2^†

map describing cancellous bone-induced intravoxel dephasinq;

(iv) a fat map; and

(v) a water map.