A Survey of Medical Image Registration J B

A Survey of Medical Image Registration J. B. Maintz, M. A Viergever Medical Image Analysis, 1998

Medical Image § § SPECT (Single Photon Emission Computed Tomography) PET (Positron Emission Tomography) MRI (Magnetic Resonance Image) CT (Computed Tomography)

Image Modalities Anatomical Depicting primarily morphology (MRI, CT, X -ray) n Functional Depicting primarily information on the metabolism of the underlying anatomy (SPECT, PET) n

Medical Image Integration n n Registration Bring the modalities involved into spatial alignment Fusion Integrated display of the data involved Matching, Integration, Correlation, …

Registration procedure n n n Problem statement Registration paradigm Optimization procedure Pillars and criteria are heavily interwined and have many cross-influences

Classification of Registration Methods Nature of Dimensionality Registration basis Nature of transformation Domain of Interaction transformation Optimization procedure Modalities involved Object Subject

Dimensionality Spatial dimensions only n n 2 D/2 D 2 D/3 D 3 D/3 D Time series(more than two images), with spatial dimensions n n 2 D/2 D 2 D/3 D 3 D/3 D

Spatial registration methods n n 3 D/3 D registration of two images 2 D/2 D registration Less complex by an order of magnitude both where the number of parameters and the volume of the data are concerned. n 2 D/3 D registration Direct alignment of spatial data to projective data, or the alignment of a single tomographic slice to spatial data

Registration of time series Time series of images are required for various reasons n Monitoring of bone growth in children (long time interval) n Monitoring of tumor growth (medium interval) n Post-operative monitoring of healing (short interval) n Observing the passing of an injected bolus through a vessel tree (ultra-short interval) Two images need to be compared.

Nature of registration basis Image based n n Extrinsic based on foreign objects introduced into the imaged space n Intrinsic based on the image information as generated by the patient n Non-image based (calibrated coordinate systems)

Extrinsic registration methods n Advantage n n n registration is easy, fast, and can be automated. no need for complex optimization algorithms. Disadvantage n n n Prospective character must be made in the pre-acquisition phase. Often invasive character of the marker objects. Non-invasive markers can be used, but less accurate.

Extrinsic registration methods n n Invasive Stereotactic frame Fiducials (screw markers) Non-invasive Mould, frame, dental adapter, etc Fiducials (skin markers)

Extrinsic registration methods n n The registration transformation is often restricted to be rigid (translations and rotations only) Rigid transformation constraint, and various practical considerations, use of extrinsic 3 D/3 D methods are limited to brain and orthopedic imaging

Intrinsic registration methods n n n Landmark based Segmentation based Voxel property based

Landmark based registration n Anatomical salient and accurately locatable points of the morphology of the visible anatomy, usually identified by the user n Geometrical points at the locus of the optimum of some geometric property, e. g. , local curvature extrema, corners, etc, generally localized in an automatic fashion.

Landmark based registration n The set of registration points is sparse ---fast optimization procedures n Optimize Measures n n Average distance between each landmark Closest counterpart (Procrustean Metric) Iterated minimal landmark distances Algorithm n n n Iterative closest point (ICP) Procrustean optimum Quasi-exhaustive searches, graph matching and dynamic programming approaches

Segmentation based registration n Rigid model based Anatomically the same structures(mostly surfaces) are extracted from both images to be registered, and used as the sole input for the alignment procedure. n Deformable model based An extracted structure (also mostly surfaces, and curves) from one image is elastically deformed to fit the second image.

Rigid model based n “head-hat” method rely on the segmentation of the skin surface from CT, MR, and PET images of the head n Chamfer matching alignment of binary structures by means of a distance transform

Deformable model based n Deformable curves Snakes, active contours, nets(3 D) n Data structure Local functions, i. e. , splines n Deformable model approach Template model defined in one image template is deformed to match second image n n segmented structure unsegmented

Voxel property based registration n n Operate directly on the image grey values Two approaches: n n Immediately reduce the image grey value content to a representative set of scalars and orientations Use the full image content throughout the registration process

Principal axes and moments based n n Image center of gravity and its principal orientations (principal axes) are computed from the image zeroth and first order moment Align the center of gravity and the principal orientations n n Principal axes : Easy implementation, no high accuracy Moment based: require pre-segmentation

Full image content based n n Use all of the available information throughout the registration process. Automatic methods presented

Paradigms reported n n Cross-correlation Fourier domain based. . Minimization of variance of grey values within segmentation Minimization of the histogram entropy of difference images n n Histogram clustering and minimization of histogram dispersion Maximization of mutual information Minimization of the absolute or squared intensity differences …

Non-image based registration Calibrated coordinate system n If the imaging coordinate systems of the two scanners involved are somehow calibrated to each other, which necessitates the scanners to be brought in to he same physical location n Registering the position of surgical tools mounted on a robot arm to images

Nature of Transformation n n Rigid Affine Projective Curved

Domain of transformation n Global Apply to entire image n Local Subsections have their own

Rigid case equation n Rigid or affine 3 D transformation equation

Rotation matrix n rotates the image around axis i by an angle

Transformation n Many methods require a pre-registration (initialization) using a rigid or affine transformation Global rigid transformation is used most frequently in registration applications Application: Human head

Interaction n Interactive Semi-automatic Automatic Minimal interaction and speed, accuracy, or robustness

Interaction n Extrinsic methods n n n Automated Semi-automatic Intrinsic methods n Semi-automatic n n n Anatomical landmark Segmentation based Automated n n Geometrical landmark Voxel property based

Optimization procedure Parameters for registration transformation n Parameters computed n Parameters searched for

Optimization techniques n n n Powell’s method Downhill simplex method Levenberg-Marquardt optimization Simulated annealing Genetic methods Quasi-exhaustive searching

Optimization techniques n Frequent additions: Multi-resolution and multi-scale approaches n More than one techniques Fast & coarse one followed by accurate & slow one

Modalities involved n n Monomodal Multimodal Modality to model Patient to modality

Subject n n n Intrasubject Intersubject Atlas

Object n Different areas of the body

Related issues n How to use the registration n Registration & visualization Registration & segmentation Validation of the registration Accuracy, …