Dr. Lanell Peterson Profile

Dr. Lanell Peterson

at Univ. of Washington

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Publications (2)

Proceedings Article | 28 May 2019 Paper

Virtual clinical trials using 3D PET imaging

Paul Kinahan, Darrin Byrd, Kristen Wangerin, Mark Muzi, Lanell Peterson, Brenda Kurland, Jennifer Specht, Hannah Linden

Proceedings Volume 11072, 1107238 (2019) https://doi.org/10.1117/12.2535582

KEYWORDS: Positron emission tomography, Clinical trials, Signal to noise ratio, Scanners, Therapeutics, Reconstruction algorithms, Cancer, Image restoration, Image processing, 3D image processing

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Positron emission tomography (PET) imaging has emerged as a standard component for cancer diagnosis treatment and has increasing use in clinical trials of new therapies for cancer and other diseases. The use of PET imaging to assess response to therapy and its ability to measure change in radiotracer uptake is motivated by its potential for quantitative accuracy and high sensitivity. However, the effectiveness depends upon a number of factors, including both the bias and variance in the pre- and post-therapy reconstructed images. Despite all the attention paid to image reconstruction algorithms, little attention has been paid to the impact on task performance of the choice of algorithm or its parameters, even for FBP or OSEM. We have developed a method, called a 'virtual clinical trial', to evaluate the ability of PET imaging to measure response to cancer therapy in a clinical trial setting. Here our goal is to determine the impact of a fully-3D PET reconstruction algorithm and parameters on clinical trial power. Methods: We performed a virtual clinical trial by generating 90 independent and identically distributed PET imaging study realizations for each of 22 original dynamic 18F-FDG breast cancer patient studies pre- and post- therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, specifically biological variability and quantum noise determined by the PET scanner sensitivity and/or imaging time, as well as the trade-offs introduced by the reconstruction algorithm in bias versus variance. Results: For high quantum noise levels, due to lower PET scanner sensitivity or shorter scan times, quantum noise has a measurable effect on signal to noise ratio (SNR) and study power. However, for studies with moderate to low levels of quantum noise, biological variability and other sources of variance determine SNR and study power. In other words, the choice of the fully-3D PET reconstruction algorithm and parameters has minimal impact on task performance. Conclusions: For many clinical trials, the variance aspects of 3D PET and reconstruction method and parameters have minimal to no impact. Variance for other factors, and bias introduced by changes in 3D PET reconstruction between scans can dramatically impact the utility of clinical trials that rely on quantitative accuracy.

SPIE Journal Paper | 23 November 2017 Open Access

Assessment of a statistical AIF extraction method for dynamic PET studies with ¹⁵O water and ¹⁸F fluorodeoxyglucose in locally advanced breast cancer patients

Finbarr O’Sullivan, Janet O'Sullivan, Jian Huang, Robert Doot, Mark Muzi, Erin Schubert, Lanell Peterson, Lisa Dunnwald, David Mankoff

JMI, Vol. 5, Issue 01, 011010, (November 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011010

KEYWORDS: Blood, Positron emission tomography, Breast cancer, Statistical analysis, Data modeling, Tissues, Diagnostics, Lung, Statistical modeling, Tumors

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