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Imaging > Iterative Reconstruction > Flashcards

Iterative Reconstruction Flashcards

1
Q

what is a filtered back projection

A

it is a closed form formula. One step solution, computationally efficient. Exact reconstruction if applied to ideal projection data

  • point source
  • point detector
  • x-ray path as a thin line
  • monochromatic spectrum
  • complete data set
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2
Q

what is the size of xray source and detector in FBP

A

it is an finite size of source and detector

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3
Q

what is the spectrum for FBP

A

it is polychromatic spectrum, beam hardening

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4
Q

what is the noise for FBP

A

it is quantam noise, and electronic noise

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5
Q

what is the projection data like

A

it is an incomplete projection data

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6
Q

what is iterative reconstruction- what is the initial guess

A

it can be anything and it is most commonly a blank image or FBP images

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7
Q

what is the simulated projection in an iterative reconstruction

A 
add attenuation (CT number) together along x-ray path
for reconstructed image is correct, measured projection data should be the same as those calculated from reconstructed images
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8
Q

draw out how iterative reconstruction usually goes

A

slide 3 of the iterative reconstruction power point

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9
Q

what are the advantages of iterative reconstruction

A

accurate system modeling: geometry, spectrum
appropriate statistcal noise model
may improve statistical resolution and reduce image artifacts

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10
Q

what are the limitations of iterative reconstruction

A

computationally intensive, long reconstruction time, different image appearance

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11
Q

what is iterative reconstruction used for most of the time

A

it was used in PET, SPECT, IR in the early 70s, limited applications, reintroduced CT, so some people use it now in CT for IR

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12
Q

is iterative reconstruction used today and if so, how long does it take

A

it is used today, but reconstruction time is seconds to thirty minutes

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13
Q

do you use raw data or images for iterative reconstruction

A

it is better to se a picture

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14
Q

draw an iterative reconstruction for te approach

A

page 6 of the power point

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15
Q

what is algebraic reconstruction

A

images with n pixels have N degrees of freedom. A specific image is a single point in N dimension space, and single ray represents a hyperplane in this space. Image reconstruction is to find the intersectionpoint of all hyperplanes

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16
Q

what are the steps of iterative reconstruction

A 
initialization
forward projection
compare with measured projection
backproject the difference
repeat steps until the criteris is met
17
Q

when do you stop the criteria

A

when a fixed number of iterations is met, when the difference between the images in two iterations is smaller than a predetermined threshold

18
Q

what is a statistical reconstruction

A

consider measured projections (sinogram) as random variables. The statistics of these random variables are related to the imaging object (estimated images). An objective function is defined to relate measured projections to the estrimated images. IMage reconstruction is performed by iteratively maximized (minimized) the objective function. Stabalizing function in the form of a regularization prior may further reduce artfacts and noise

19
Q

what is are some of the models for statistical iterative reconstruction

A

Poisson noise model and maximum a posteriori

20
Q

what is data fidelity

A

it is a match measured and simulated projection data. You assign different weights to the different projection data:

  • lower weight to higher noise projection data
  • higher weight to lower noise projection data
21
Q

what is the regularization function

A

without the redularization term, the image estrimates are excessively noisy and unstable. Regularization enforces smoothnessin the reconstructed images by encouraging neighboring pixels to have similar values. Based on certain prior knowledge of the image. Tradeoff between noise and resolution

22
Q

what are the advantages of iterative reconstruction

A 
accurate geometry model
spectrum model
appropriate statistical noise model
object constraints (non-negativity)
artifact reduction (can reduce metal artifacts)
flexible for scanning configurations
potential to reconstruct images from sparse or incomplete data
23
Q

what is the spectrum model

A

it is for beam hardening correction by modeling beam spectrum into iterative reconstruction

24
Q

what is the difference between FBP and iterative reconstruction in a statistical model

A

FBP treats all data equally
iterative reconstruction- weight is measured data accordingly, assign lower weight for noisier data, potential reduction and dose reduction

25
Q

what is better for metal artifact reduction

A

it is best with iterative reconstruction

26
Q

is FBP or iterative based on geometry

A

FBP has an algorithm for each, but a new one has to be employed for each different geometry. Iterative reconstruction treats it all the same so its flexible for non-standard geometry

27
Q

does noise reduction improve detection

A

multiple studies have shown noise reduction does improve image quality and diagnositc confidence, however low dose images withou tnoise reduction show the same CT findings

28
Q

what is observer performance

A

multiple studies evaluate image quality of lower dose images with nise reduction, not observer performance, and comaprison with full dose images to determine what you are missing, and comparison with low dose images, to determine if you need to noise reduction to change diagnosis

29
Q

what is the lowest acceptable dose

A

Need an observer for it, and it is better be safe leaving a safety margin. Higher contrast tasks permit greater dose reduction, while low contrast tasks permit less.

30
Q

what is the ideal observer

A

Bayesian

31
Q

what is the ideal linear observer

A

Hotelling observer

32
Q

what is model observers that predict human observers that predict human observer perfromance

A

anthropomorphic

Imaging (19 decks)

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