Techniques for 3-D elastic spatial registration of multiple modes of measuring a body

• US 8,031,211 B2
• Filed: 04/18/2011
• Issued: 10/04/2011
• Est. Priority Date: 08/30/2005
• Status: Active Grant

• Current Assignee: Cleveland Clinic Foundation, IGI Medical Technologies, LLC
• Original Assignee: Cleveland Clinic Foundation, University of Maryland Baltimore County (University of Maryland)
• Inventors: Shekhar, Raj, Walimbe, Vivek
• Primary Examiner(s): PERUNGAVOOR, SATHYANARAYA V
• Application Number:
  US13/088,871
• Publication Number:
  US 20110193882A1
• Time in Patent Office: 169 Days
• Field of Search: 382/128
• US Class Current: 345/648
• CPC Class Codes: G01T 1/1611 using both transmission and emission sources sequentially SPECT imaging G01T1/1642; PET imaging G01T1/2985; detecting prohibited goods, e.g. weapons, explosives, hazardous substances, contraband or smuggled objects G01V5/20
• CPC Class Codes: G01T 1/1647 Processing of scintigraphic data not related to a particular imaging system G01T1/2992

Abstract

Techniques for registration of multiple measurement modes of a body include receiving first and second data from different modes. Each includes measured values with coordinate values. For two mechanically aligned modes, any non-rigid registration is performed. For some modes, the non-rigid registration includes a coarse transformation and multiple fine scale transformations. The coarse transformation maximizes a coarse similarity measure. The second data is sub-divided into contiguous sub-regions. Fine transformations are determined between the sub-regions and corresponding portions of the first data to maximize a fine similarity measure. Sub-dividing and determining fine transformations repeats until stop conditions are satisfied. Transformations between the last-divided sub-regions are interpolated. Any of the fine similarity measure, a search region, interpolation method, sub-division location, and the use of rigid or non-rigid fine transformations are adaptive to properties of the first or second data so that the registration is automatic without human intervention.

Claims

1. A method for adaptive non-rigid registration of multiple measurement modes of a target body, comprising the steps of:

receiving first data comprising a first plurality of measured values each associated with a plurality of coordinate values for spatial or temporal dimensions based on a first measurement mode of a target body;

receiving second data comprising a second plurality of measured values each associated with a plurality of coordinate values for spatial or temporal dimensions based on a different second measurement mode of the target body;

determining a coarse scale transformation to maximize a coarse scale similarity measure between the first data and the second data;

sub-dividing the second data into a plurality of sub-regions each comprising a spatially or temporally contiguous subset of the second data;

determining an adaptive bin size for a mutual histogram based on a sample distribution for each sub-region of the plurality of sub-regions;

determining a fine scale similarity measure between the plurality of sub-regions and a plurality of corresponding portions of the first data based on the mutual histogram with the adaptive bin size;

determining a plurality of fine scale transformations to maximize the fine scale similarity measure; and

transforming the plurality of coordinate values for each measured value of the second data based on the plurality of fine scale transformations.

2. A method as recited in claim 1, wherein both the coarse scale transformation and the fine scale transformations are rigid-body translational and rotational transformations.

3. A method as recited in claim 1, wherein both the coarse scale similarity measure and the fine scale similarity measure are based on normalized mutual information.

4. A method as recited in claim 1, wherein the second measurement mode uses the same sensing system as the first measurement mode but at a different time.

5. A method as recited in claim 1, wherein the second measurement mode uses positron emission tomography (PET) and the first measurement mode uses X-ray computer tomography (CT).

6. A method as recited in claim 1, further comprising:

repeating said steps of sub-dividing the second data and determining a plurality of fine scale transformations until conditions for further sub-dividing the second data are no longer satisfied, wherein fine scale transformations for the last-formed sub-regions are each associated with a canonical position within a corresponding last-formed sub-region; and

interpolating fine scale transformations to positions between canonical positions.

7. A method as recited in claim 6, wherein:

the fine scale transformations include rotational transformations; and

said step of interpolating fine scale transformations further comprises

expressing rotational transformations as quaternion values, and

interpolating the quaternion values to positions between canonical positions.

8. A non-transitory computer-readable medium carrying one or more sequences of instructions for adaptive non-rigid registration of multiple measurement modes of a target body, wherein execution of the one or more sequences of instructions by one or more processors causes the one or more processors to perform the steps of:

receiving first data comprising a first plurality of measured values each associated with a plurality of coordinate values for spatial or temporal dimensions based on a first measurement mode of a target body;

receiving second data comprising a second plurality of measured values each associated with a plurality of coordinate values for spatial or temporal dimensions based on a different second measurement mode of the target body;

determining a coarse scale transformation to maximize a coarse scale similarity measure between the first data and the second data;

sub-dividing the second data into a plurality of sub-regions each comprising a spatially or temporally contiguous subset of the second data;

determining an adaptive bin size for a mutual histogram based on a sample distribution for each sub-region of the plurality of sub-regions;

determining a fine scale similarity measure between the plurality of sub-regions and a plurality of corresponding portions of the first data based on the mutual histogram with the adaptive bin size;

determining a plurality of fine scale transformations to maximize the fine scale similarity measure; and

transforming the plurality of coordinate values for each measured value of the second data based on the plurality of fine scale transformations.