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540 Final > Kim review > Flashcards

Kim review Flashcards

1
Q

absolute path

A

the path of a file from the root location ex: /users/notcara/documents…..etc

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2
Q

relative path

A

the path of a file from current location: ex: ./MATLAB/etc.

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3
Q

how would you write a path that’s a folder above where you are

A

../jboudreau/etc.

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4
Q

terminal command: pwd

A

print current directory

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5
Q

terminal command: cd

A

change directory

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6
Q

terminal command: ls

A

list directory

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7
Q

terminal command: find

A

search files

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8
Q

terminal command: cp

A

copy files, use option ‘-r’ to copy folders

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9
Q

terminal command: mkdir

A

make directory

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10
Q

terminal command: mv

A

move/rename files

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11
Q

terminal command: rm

A

remove/delete files, use option ‘-r’ to remove folders

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12
Q

is there a recycle bin in Linux command line?

A

NO. it is possible to recover deleted files but it takes a lot of effort

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13
Q

terminal command: ln

A

create links/shortcuts to files, use option ‘-s’ to create symbolic links

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14
Q

files/directories are associated with three types of users:

A

owner (u), group (g), and others (o)

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15
Q

three kinds of permissions are assigned to each type of user

A

read (r), write (w), execute (x)

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16
Q

command: ls -l

A

view permissions

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17
Q

command: chmod

A

change permissions

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18
Q

command chown

A

change ownerships

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19
Q

MRI RF intensity inhomogeneities

A

when part of the image is shaded darker, there’s a gradient across the image going from darker to lighter

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20
Q

if r= distance from the origin of magnetic field, what would the graph with r as the x axis and 1/r^2 as the y axis look like?

A

steep drop down between 0 and 1 on the x axis tapering off for the rest of it

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21
Q

the slope of r by 1/r^2 graph would __ with higher field strength

A

increase

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22
Q

N3 inhomogeneity correction: end tolerance

A

smaller values lead to more stable results but take longer time

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23
Q

N3 inhomogeneity correction: field distance (mm)

A

smaller distance offers higher power but takes longer computationally.

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24
Q

the higher the magnetic field, the __ the field distance in N3 should be used

A

smaller

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25
Q

N3 inhomogeneity correction: subsampling factor:

A

the larger this is, the faster the algorithm runs but the coarser the results are

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26
Q

N3 inhomogeneity correction: Kernel fwhm

A

the larger the FWHM is, the faster the algorithm runs

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27
Q

N3 end tolerance: range

A

0.01 - 0.00001

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28
Q

N3 subsampling factor range

A

1.0-32.0

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29
Q

N3 Kernel fwhm range

A

0.05-0.50

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30
Q

normalization changes __, does not change __

A

the range of intensity distribution on the image using fixed min & max values; doesn’t change histogram shape

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31
Q

equalization requires __, changes __, and the min & max values __

A

a target image to match; the histogram; may not be fixed

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32
Q

output from brain extraction (2)

A

a mesh representation of brain boundary; a binary brain mask

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33
Q

a mesh representation of brain boundary is __ than the binary mask, because it __ but the binary is __

A

more accurate; can achieve sub-voxel accuracy; more convenient to use

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34
Q

BET option -f

A

“fractional intensity threshold”. the higher this local threshold, the smaller the Norm Force to expand the brain surface, the smaller the mask

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35
Q

BET option -R

A

recursively estimate the center of the brain; makes the result more stable especially when the image includes a large non-brain portion (it removes it)

36
Q

rigid registration

A

the most strict, only between the same subject. is a type of linear registration

37
Q

linear registration

A

use to adjust spacing difference, or for pre-registration for template construction

38
Q

affine registration

A

has 12 parameters, takes a long time - pre-registration for deformable registration and template construction — deformation

39
Q

slide 19

A

slide 19

40
Q

linear interpolation

A

takes weighted average voxel intensity values - overlaps exist between interpolation coverage and actual voxels

41
Q

(registration) similarity metrics (4)

A

sum of squared errors, correlation coefficients, mutual information, landmark matching. used for registration and transformation

42
Q

sum of squared errors

A

same modality, value similarity (registration of images from the same scanner and same modality)

43
Q

correlation coefficients

A

linear similarity (two images of same modality, possibly different scanners, or registration of subject image to atlas of same modality)

44
Q

mutual information

A

different modalities, non-linear similarity

45
Q

landmark matching

A

not intensity-based, identifies landmarks to match between images

46
Q

con of sum of squares errors method

A

very restricted assumption

47
Q

sum of squares errors method: assumption

A

same tissue showing with same intensity range in two images

48
Q

using sum of squared errors, what could be performed as its preprocessing

A

histogram-matching

49
Q

correlation coefficients: assumption

A

there is a linear relationship between voxel intensity of two images

50
Q

using correlation coefficients, what could be performed as its preprocessing

A

histogram-matching

51
Q

histogram-matching is not required for this similarity metric

A

mutual information

52
Q

mutual information assumption:

A

two images share similar structures, but may be expressed with voxel intensities in different ways

53
Q

mutual information pro

A

can handle non-linear relationships

54
Q

3 methods of tissue segmentation

A

K-means, Gaussian-mixture models-based clustering, Markov Random Fields (MRF)

55
Q

K-means clustering

A

takes the mean of the intensity values of clusters

56
Q

K-means clustering only works when

A

the size of each cluster is comparable equal

57
Q

“Mickey Mouse” ear issue

A

k-Means clustering results in segmentations that include the ear plus some of the face, whereas EM clustering is more accurate

58
Q

Gaussian mixture model (GMM) clustering

A

each cluster is specified by two parameters: location and standard deviation

59
Q

GMM clustering: location is __ standard deviation is __

A

the mean of intensity values in the cluster, the range of variance in intensity in the cluster

60
Q

GMM clustering: __ are mixed together in the histogram. to model the mixture, __

A

different tissues types; each tissue type is assigned a weight

61
Q

GMM clustering: weight:

A

mixture proportion relative to other tissue types

62
Q

Markov Random Fields

A

uses probability to determine which tissue type a voxel is more likely to be given the neighboring voxels

63
Q

HMRF (markov random field) does not mean __

A

protection of topology

64
Q

what will happen when the weight for HMRF is large? small?

A

..

65
Q

history of template construction (5)

A

Talairach atlas, MNI305 linear, MNI152 linear, MNI152 nonlinear (most popular), ADNI40 nonlinear

66
Q

what is the most popular template?

A

MNI152 nonlinear

67
Q

morphometry methods (3)

A

VBM, DBM, VBM + DBM (optimized VBM)

68
Q

voxel-based analysis of gray matter (6 steps)

A

subject images, tissue classification, then you have probabilistic map of gray matte, spatial transformation to an atlas space, atlas space, voxel-wise statistical analysis

69
Q

cons of VBM (3)

A

tissue probabilistic maps do not necessarily reflect the volume of a tissue at a voxel; relies on the accuracy of the tissue classification; mis-registration may lead to false positives

70
Q

deformation-based morphometry (DBM): by-product of __

A

nonlinear registration

71
Q

how does DMB overcome the criticism of VBM?

A

…

72
Q

optimized VBM: involves __

A

Jacobian modulation

73
Q

optimized VBM: steps (3)

A

gray matter map, times Jacobian determinant map, = modulated map

74
Q

how does the optimized VBM overcome the issue of VBM?

A

…

75
Q

Jacobian determinants:

A

how much a voxel changes after registration to the template

76
Q

atlas-based segmentation: involves __ (2)

A

registration to template, transformation (segmentation)

77
Q

nearest neighbor interpolation

A

assigns intensity value of the nearest voxel to the interpolation center

78
Q

interpolation of label maps can either be

A

linear, or nearest neighbor method

79
Q

evaluation of segmentation accuracy

A

…?

80
Q

linear transformation

A

does the same thing to each voxel, global

81
Q

nonlinear transformation

A

you can work with a specific part of the image; local

82
Q

what software do we use for rigid registration?

A

ANTS

83
Q

DBM

A

doesn’t rely on segmentation

84
Q

Jacobian determinant

A

how much a specific voxel changes in size during deformation

85
Q

two indices to evaluate segmentation accuracy

A

dice (more popular) and jaccard

540 Final (1 decks)

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