Lecture 7- Advanced Image Quality

Question 1

quantitative physical measurement

Answer 1

spatial resolution, noise, contrast, CNR, SNR, NEQ,DQE

Question 2

phantom-based observer assessment human or algorithm

Answer 2

performance on some phantom base taste e.g. object dtectibility, low contrast resolution

Question 3

patient based observer assessment

Answer 3

radiologist acceptance

Question 4

quantitative measurement of diagnostic/detective value for a specific diagnostic task

Answer 4

ROC study to determine sensitivity and specificity

Question 5

signal to noise ratio

Answer 5

measure of a signal strength relative to the background noise level In the X-ray imaging, signal provided by number of x-ray photons, subject attenuation, and imaging system. Noise also contributed by number of x-ray photons, subject attenuation, and by imaging system

Question 6

what is the formula for SNR

Answer 6

SNR= Nbar/signma=suqareroot of Nbar

Question 7

what does SNR depend on

Answer 7

number of photons

Question 8

what is the noise equivalent quanta

Answer 8

the number of Poisson-distributed quanta that would produce the same SNR with an ideal detector at a given spatial frequency. NEQ is essentially a spatial frequency domain descriptor of SNR^2, assuming LSI system

Question 9

what is the absolute measure of image quality:

Answer 9

of x-ray quanta that an image is worth at each spatial frequency

Question 10

why is the task-based approaches for image quality evaluation

Answer 10

ultimate goal of imaging is to provide useful images for given diagnostic tasks. System optimization has to improve the diagnostic outcome.

Question 11

individual physical properties of image quality

Answer 11

contrast, resolution, noise. do not characterize the overall image quality

Question 12

what does the NEQ represent

Answer 12

absolute and overall image quality, but may not be able to predict or correlate with the diagnostic performance for a given task

Question 13

what are the four components of a task based approach

Answer 13

task, objects and images, observer (decesion maker), figure of merit

Question 14

what goes into classification and detection

Answer 14

the image is to be classified into one of the many possible alternatives, finite number of hypotheses like detection or classification of a tumor

Question 15

what does into estimation or quantification

Answer 15

estimating one or more numerical parameters for the image, infinite number of hypotheses, typically involve a numerical algorithm, rather than a computation by a human. like tumor volume

Question 16

what goes into hybrid estimation-classification

Answer 16

estimate one or more parameters that will then be used as input to subsequent decision making operations

Question 17

what goes into the observer role, what is being decided

Answer 17

if an image is there or not. so presence or absence. can be human or algorithm

Question 18

what are estimation tasks

Answer 18

accuracy, variance, MSE or ML estimator, cramer rao lower bound

Question 19

what are classification/detection tasks

Answer 19

accuracy, sensitivity and specificity pairs, positive and negative predictive value, area under the curve ROC, SNR detectibility index and cost + utility

Question 20

what is accuracy as a figure of merit

Answer 20

accuracy is the simplest FOM to quntiy the binary classification system from. Fraction of cases for which the decision is correct compared with the truth

Question 21

what are the limits using accuracy for FOM

Answer 21

because accuracy is highly dependent on the prevalence of the underlying hypothesis.

Question 22

what is the sensitivity and specificity and what does it mean and formula

Answer 22

sensitivity is the probability of the correct decision when presented with positive cases. Sp the probability of the correct devision when presented with negative cases.

Sn= TP/(TP+FN)
Sp= TN/(TN+FP)

Question 23

what are the PPV and NPV and the formulas

Answer 23

PPV= TP/(TP+FP)- the probability that the case is actually positive, when the observer says the case is positive.

NPV= TN/(TN+FN)- the probability that the case is actually negative when the observer says it is negative

Question 24

draw a curve with lines representing true positive and true negative and threshold values

Answer 24

draw it

Question 25

what is an ROC and what does it look like

Answer 25

draw and it is a receiver operative characteristic curve

Question 26

what is the area under the ROC curve mean

Answer 26

it is a meaningful measure of overall performance for classification/detection tasks.

Question 27

higher area under the curve means what

Answer 27

higher performance

Question 28

what is the SNR

Answer 28

it is the signal to noise ratio and it is degree of overall for the two distributions of the two decision variable to determine the operability of the two classes determines the detectability. AUC is one measure of the overlap and another is SNR

Question 29

what is the index of detectability

Answer 29

it is the SNR derived from the AUC. Normal assumption is appproriate for many experimental ROC from human data

Question 30

what is the yes no experiment

Answer 30

it is where someone picks yes or no or positive vs. negative

Question 31

what is the rating experiment scale

Answer 31

for each trial, the observer is asked to indicate the confidence level that the trial belongs to the two hypotheses. At leas five rating scales. this rating data can be used to to make an ROC by sweeping the decision threshold between rating scales

Question 32

go through homework of how to turn data into a ROC

Answer 32

draw it

Question 33

what are the issues with human observers in ROC studies

Answer 33

human oberver and ROC analysis can be considered as gold standard method to measure image quality for a specific visual task. ROC studies with human observers are time consuming and difficult to control. Tests of statical observation are hard to tell.

Question 34

mathematical model observers mean what

Answer 34

algorithms to quantify objectively or to predict human observer performacnce for a given visual task. Ideal is bayesian observer

Question 35

what is the ideal observer and likelihood ratio

Answer 35

any observe that uses the likelihood ratio as the decision variable is referred to as an ideal observer. points on ROC curve for ideal observer are generated by comparing A(g) to different thresholds

Question 36

propertites of ideal observer

Answer 36

it maximizes the area under the Roc curve, maximizes true positive fraction at any false positive fraction. An observer than uses a monotonically trasnformed version of the linelihood ratio is still an ideal observer. The same post processing is used

Question 37

what are the difficulties with an ideal observer

Answer 37

requires immense amounts of prior knowledge. Usually a nonlinearr function of the imaging data and it is difficult to compute except for textbook cases. Not obviously related to humans or other practical observers