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Quantitative CT Imaging For Response Assessment When Using Dose Reduction Methods
Michael McNitt-Gray, Ph.D.
University of California - Los Angeles

Despite widespread concerns over radiation dose, CT continues to be widely used for assessing response to therapy in many clinical trials settings. There have been significant developments which allow the reduction of radiation dose from CT, including advances in iterative reconstruction techniques, detector technologies and others that promise significant dose reductions (50-60%) to patients, while maintaining clinical image quality.  While these technologies should be investigated wherever possible in a clinical environment, their effects on quantitative measures extracted from CT images are unclear and need to be investigated before they are deployed in clinical trials. Simply reducing tube current time product (mAs) will increase image noise, which may increase variability in quantitative measures. Size measures may be affected differently depending on the anatomic region; lung lesions (typically high contrast objects) may be affected differently from liver lesions (typically lower contrast). Peak values measured when contrast enhanced studies are used may also respond to dose reductions differently. In addition, because new iterative reconstruction methods reduce noise, they often also smooth the image somewhat, which may affect size and density (e.g. average HU) measures.  Therefore, this application proposes to systematically investigate the effects of radiation dose reduction methods on quantitative metrics used in clinical trials. The goal is to determine how far dose levels can be decreased under different conditions before increase variance reaches unacceptable levels in the context of clinical trials using quantitative measures to assess response to therapy.  The UCLA team has proposed three specific aims to carry out this research. In the first aim, they propose to create a collection of cases that represent a range of low dose acquisition and reconstruction scenarios in specific  quantitative imaging tasks. This will be accomplished using a calibrated dose reduction simulation method (noise insertion tool) and then reconstructing images under a wide variety of dose reduction levels and reconstruction methods. The second specific aim will be to extract quantitative imaging measures from these reconstructed image data sets and analyze variance of quantitative measures across dose levels and reconstruction methods. The third will use the results of the second aim’s analysis to evaluate reduced dose imaging effects in a prospective clinical trial. The overall goal is to provide guidance to the QIN, and clinical trials in general, regarding the use of both standardized protocols and the use of dose reduction methods, with the ultimate goal of determining the levels of dose reduction that yield acceptable levels of measurement variance in several assessment tasks/environments.