Image quantification is the extraction of quantitative measures from patient images and the relation of the quantitative measures to patient outcomes. For an image quantification to be most effective, it should meet four requirements of (1) relevance, (2) objectivity, (3) robustness, and (4) implementability. A relevant quantification assigns a number to an observable biological or clinical phenomenon. An objective quantification measures the phenomenon accurately. A robust quantification measures the phenomenon precisely. An implementable quantification is one which can be implemented in a timely fashion in a clinical environment and workflow. Relevance can be assessed by studying how the quantification metrics correlate with clinical patient outcomes. Objectivity and robustness can be assessed using phantoms and biological models to test how different feature extraction workflows lead to different biases and variabilities in the feature measurements. The implementation of quantification in a clinical environment should be such that it is automated, intuitive, and efficient. CT-based quantification has the potential to improve consistency and quality of patient care if it is applied using these principles.
KeywordsQuantification Computed tomography Ground truth Objectivity Robustness
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