Lecture 7- Advanced Image Quality Flashcards
quantitative physical measurement
spatial resolution, noise, contrast, CNR, SNR, NEQ,DQE
phantom-based observer assessment human or algorithm
performance on some phantom base taste e.g. object dtectibility, low contrast resolution
patient based observer assessment
radiologist acceptance
quantitative measurement of diagnostic/detective value for a specific diagnostic task
ROC study to determine sensitivity and specificity
signal to noise ratio
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
what is the formula for SNR
SNR= Nbar/signma=suqareroot of Nbar
what does SNR depend on
number of photons
what is the noise equivalent quanta
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
what is the absolute measure of image quality:
of x-ray quanta that an image is worth at each spatial frequency
why is the task-based approaches for image quality evaluation
ultimate goal of imaging is to provide useful images for given diagnostic tasks. System optimization has to improve the diagnostic outcome.
individual physical properties of image quality
contrast, resolution, noise. do not characterize the overall image quality
what does the NEQ represent
absolute and overall image quality, but may not be able to predict or correlate with the diagnostic performance for a given task
what are the four components of a task based approach
task, objects and images, observer (decesion maker), figure of merit
what goes into classification and detection
the image is to be classified into one of the many possible alternatives, finite number of hypotheses like detection or classification of a tumor
what does into estimation or quantification
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
what goes into hybrid estimation-classification
estimate one or more parameters that will then be used as input to subsequent decision making operations
what goes into the observer role, what is being decided
if an image is there or not. so presence or absence. can be human or algorithm
what are estimation tasks
accuracy, variance, MSE or ML estimator, cramer rao lower bound
what are classification/detection tasks
accuracy, sensitivity and specificity pairs, positive and negative predictive value, area under the curve ROC, SNR detectibility index and cost + utility
what is accuracy as a figure of merit
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
what are the limits using accuracy for FOM
because accuracy is highly dependent on the prevalence of the underlying hypothesis.
what is the sensitivity and specificity and what does it mean and formula
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)
what are the PPV and NPV and the formulas
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
draw a curve with lines representing true positive and true negative and threshold values
draw it
