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Question 1

what is Image registration

Answer 1

the process of transforming the different sets of data into one coordinate system. Image registration is trying to maximise the amount of shared information in two images. Can be thought as reducing the information in the combined image. necessary to compare or integrate the medical data obtained from different
time, modalities or subjects.

Question 2

what is image fusion

Answer 2

registration + combination into a single representation

e.g. pet + ct

Question 3

what are the advantages of image fusion

Answer 3

Improved system performance, detection, tracking, situation assessment and awareness, robustness, spatial and temporal coverage

Question 4

what is Monomodal image registration

Answer 4

to register serial scans from the same imaging technique in temporal studies to evaluate disease progression, assess treatment response, detect changes, and monitor growth and development.

Question 5

what is Multimodal image registration

Answer 5

to combine complementary information from different

imaging modalities and to correlate anatomical structure with functional information, e.g., CT-PET, CT-MRI, MRI-PET

Question 6

describe the process of Registration Optimisation

Answer 6

a searching strategy to find the best registration solution i.e. the solution that produces the most similar spacial correspondance.

Question 7

what is Powell optimisation

Answer 7

performs a succession of one-dimensional optimisations, finding in turn the best solution along each freedom degree.

The algorithm stops when it is unable to find a new solution with a significant improvement to the current solution.

Question 8

describe the process of Registration Interpolation

Answer 8

Interpolation is required when an image needs to undergo transformations.

projective mapping can yield noninteger values for pixels. Because the target image is digital, its pixel values are defined only at integer coordinates. Thus it is necessary to infer the gray-level values at those locations; Gray-level interpolation is the technique that determines the gray-level value at noninteger coordinates.

Question 9

what are the most frequently used interpolation methods (in biomedical imaging)

Answer 9

nearest neighbor, linear, bilinear, trilinear, cubic, bicubic, tricubic, quadrilinear, cubic spline, sinc function.

Question 10

what is registration transformation

Answer 10

to relate the pixels of moving/study/floating images to the corresponding pixels of a fixed/reference/target image.

pk = T(qk) where pk and qk are corresponding pixels

Question 11

what are the three categories of registration transformation

Answer 11

Rigid transformation

Affine Transformation

Deformable/Elastic/Nonlinear Transformation

Question 12

what is Rigid Transformation

Answer 12

rotation, reflection, and translation (sliding)

preserves lengths/distances and angle measures, is often used to correct translation and rotation displacements.

Question 13

what is Affine Transformation

Answer 13

maps parallel lines into parallel lines, can be used to correct skewing distortion introduced by e.g., a tilted gantry in CT

Question 14

what is Deformable/Elastic/Nonlinear Transformation

Answer 14

more complex and is mainly used to correct dramatic deformations caused by changes of tissue structures, differences of volume and shape of organs.

Relies on extracted features (point, curves, surfaces). Interpolation necessary. Transformation is mostly relevant in the neighborhood of the homologous features (local not global).

Question 15

in what situations is rigid transformation appropriate and not appropriate to use in

Answer 15

appropriate in intra-subject registration

Fails in: matching atlas to patients, inter-subject registration, presence of deformations (tumor, craniotomy)

Question 16

what are the three different techniques used to perform registration

Answer 16

Feature-based

Intensity-based

Hybrid

Question 17

what is Feature-based registration

Answer 17

using features to align the two images, such as: Point/landmark
Contour
Surface

Question 18

what is Intensity-based registration

Answer 18

The registration transformation is determined by iteratively optimizing a certain similarity measure calculated from pixel values. Similarity measures include:
Minimizing the intensity difference,
Correlation techniques,
Variance of Intensity Ratio (VIR) by R.Woods,
Information theoretic techniques (Maximization of Mutual Information)

Question 19

what is Hybrid registration

Answer 19

Combining feature- and intensity-based registration techniques.

Question 20

what is the procedure for Feature-based registration

Answer 20

Preprocessing (Segmentation): feature extraction

Registering: Computation of the optimal transformations to register features in B onto features in A.

Verifying: Usually through visual check, comparing against phantoms

Question 21

what are phantoms

Answer 21

a specially designed object that is scanned or imaged in the field of medical imaging to evaluate, analyze, and tune the performance of various imaging devices.

Question 22

what are the advantages and disadvantages of feature-based registration

Answer 22

Advantage is that the transformation can be formulated
in an analytic form with efficient computational schemes.

However, a preprocessing step is needed and the results are highly dependent on the result of the feature extraction. Complicated by the problem of local minima.

Question 23

describe the process of Point-based feature-based registration

Answer 23

involves identifying corresponding points (homologous landmarks) in the images to be registered, registering the points (Iterative Closest Point (ICP) or Thinplate
Spline technique), and inferring the image transformation.

Question 24

In Point/landmark-based registration, what are the qualities of good landmarks

Answer 24

Landmarks should:
Have uniquely defined positions.
Carry substantial image information.
Be well-suited for user-interaction.
Facilitate efficient registration.
Have salient, prominent characteristics.
Be scattered over the image.

Question 25

describe the Thinplate Spline technique

Answer 25

Spline is referred to as a long flexible strip of metal, which takes a least bent shape along the spline. This bent spline can be used to model the surface of deformed objects. can produce a smooth spline interpolation, has high computation speed, and can correct the local elastic deformations by mapping the study landmark points to the corresponding points in the reference image.

Question 26

what are Fiducial Markers (point based feature)

Answer 26

an object placed in the field of view of an imaging system which appears in the image produced, for use as a point of reference or a measure. Non-invasive: mould, frame, dental adapter, skin markers Invasive: screw markers, stereotactic frames.

Question 27

describe the process of Surface feature-based registration (“Hat and Head” method)

Answer 27

Two surface models are generated: “hat” and “head.” The “hat” surface is a skin surface from PET scan with low resolution. The “head” surface is a stack of skin contour from CT or MR scans with high resolution. The two segmented surfaces are visualised in 3D computer graphic systems and aligned by minimising the mean square distance between them.

Question 28

what is the disadvantage of surface registration

Answer 28

Unfortunately, this algorithm may be prone to finding the wrong solution and tend to fail when the surfaces show symmetries when rotated.

Question 29

what is the Simplest scheme for Intensity-Based Registration

Answer 29

minimising the intensity difference. methods include: Sum of Squared Differences (SSD) and the Sum of Absolute Differences (SAD) (check equations). These are efficient to calculate.

Question 30

What is the drawback of using Sum of Squared Differences (SSD) and Sum of Absolute Differences (SAD) for registration

Answer 30

These methods are sensitive to intensity changes. If there are lots of intensity changes throughout the image then it may not work well. This limits their application to monomodality image registration only.

Question 31

what is Correlation coefficient-based registration techique (Intensity-Based Registration)

Answer 31

Estimates the relative translative offset between two similar images. Correlation based registration techniques were proposed to aim at multimodal biomedical image registration

Question 32

what is the drawback of correlation coefficient-based registration

Answer 32

Because the geometric deformations of the image modalities are not likely to be linear, these correlation methods, which require a linear dependence between the intensity of the images, cannot achieve reliable registration results.

Question 33

what is entropy

Answer 33

Entropy measures the homogeneity (purity). describes the average information supplied by a set of values, given their probability. The lower the entropy, the greater the purity of the set. When all the variables have the same probability, the entropy achieves its maximum value, and when the probability of one variable is equal to one and the probabilities of other variables are equal to zero, then the entropy is minimum (equal to zero). In registration, the goal is to get minimum entropy.

Question 34

What is the Information Theoretic definition of a histogram

Answer 34

Gives the distribution of probabilities. A way of viewing the entropy of an image. Suppose image I has N pixels, then the histogram is the probability that a pixel has intensity i

Question 35

what is joint entropy

Answer 35

Joint entropy measures the amount of information in a combined image. If the two images are totally unrelated, the joint entropy will arrive at its maximum value and be the sum of the entropies of the individual images. the more similar the images are, the lower the joint entropy.

Question 36

what is a joint histogram

Answer 36

The Joint Histogram is a measure of the correlation between the intensities in the two images. The values for each pixel represents the probability of the value pairs occurring together.

Question 37

what does an ideal registration histogram look like

Answer 37

the joint histogram appears sharper and has tight clusters. i.e. the distribution is low.

Question 38

what is Mutual Information

Answer 38

Measures degree of statistical dependence between two random variables. A measure of how well one image explains the other one.

Question 39

what is mutual information registration

Answer 39

Involves finding the transformation that makes one image the best possible predictor for the second image. Mutual information will be maximum at the optimal registration. Maximises the clustering of the joint histogram. suitable for multimodal image registration. It is mainly used in rigid registration schemes.

Question 40

describe the hybrid registration process for MRI brain registration

Answer 40

Step 1 – landmark-based global registration. Step 2 – intensity-based registration for regions away from the landmarks. This is iterated until convergence criteria met.

Question 41

describe the hybrid registration process for retinal image registration

Answer 41

Step 1 – binary vascular trees are extracted based on local entropy-based thresholds. Step 2 – zero-order translation is estimated by using Mutual information technique. Step 3 – feature points are used to estimate higher-order transformation. (landmark based)

Question 42

how can image registration help with early diagnosis of neurodegenerative disorders

Answer 42

not all brains are the same. Build statistical models that capture patterns / characteristics of normal brains and/or specific brain diseases. Atlas's can be used to automatically segment and label. Registration is used to combine the test image with the atlas before segmentation.

Question 43

what are the current challenges of image registration

Answer 43

Multimodal elastic registration of deformable organs. Automatic approaches for the registration of heart, lung, and liver. (Hybrid methods, combining similarity measures with morphological information may provide possibilities for elastic registration) Efficiency, especially for multiple dimensional whole-body images. Validation of the registration performance. Although a wide variety of registration approaches have been proposed, objective validation of these methods is not well established. (Image databases may in the future provide a source for the objective comparison of different registration methods)