1 June 2017 Method for detecting voxelwise changes in fluorodeoxyglucose-positron emission tomography brain images via background adjustment in cancer clinical trials
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Abstract
An important challenge to using fluorodeoxyglucose-positron emission tomography (FDG-PET) in clinical trials of brain tumor patients is to identify malignant regions whose metabolic activity shows significant changes between pretreatment and a posttreatment scans in the presence of high normal brain background metabolism. This paper describes a semiautomated processing and analysis pipeline that is able to detect such changes objectively with a given false detection rate. Image registration and voxelwise comparison of the pre- and posttreatment images were performed. A key step is adjustment of the observed difference by the estimated background change at each voxel, thereby overcoming the confounding effect of spatially heterogeneous metabolic activity in the brain. Components of the proposed method were validated via phantom experiments and computer simulations. It achieves a false response volume accuracy of 0.4% at a significance threshold of 3 standard deviations. It is shown that the proposed methodology can detect lesion response with 100% accuracy with a tumor-to-background-ratio as low as 1.5, and it is not affected by the background brain glucose metabolism change. We also applied the method to FDG-PET patient images from a clinical trial to assess treatment effects of lapatinib, which demonstrated significant changes in metabolism corresponding to tumor regions.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Lei Qin, Lei Qin, Armin Schwartzman, Armin Schwartzman, Keisha McCall, Keisha McCall, Nezamoddin N. Kachouie, Nezamoddin N. Kachouie, Jeffrey T. Yap, Jeffrey T. Yap, } "Method for detecting voxelwise changes in fluorodeoxyglucose-positron emission tomography brain images via background adjustment in cancer clinical trials," Journal of Medical Imaging 4(2), 024006 (1 June 2017). https://doi.org/10.1117/1.JMI.4.2.024006 . Submission: Received: 30 December 2016; Accepted: 10 May 2017
Received: 30 December 2016; Accepted: 10 May 2017; Published: 1 June 2017
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