Dr. Mark Muzi Profile

Dr. Mark Muzi

Director of Image Analysis at Univ of Washington Medical Ctr

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Publications (4)

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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SPIE Journal Paper | 30 October 2017 Open Access

Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies

Dariya Malyarenko, Andriy Fedorov, Laura Bell, Melissa Prah, Stefanie Hectors, Lori Arlinghaus, Mark Muzi, Meiyappan Solaiyappan, Michael Jacobs, Maggie Fung, Amita Shukla-Dave, Kevin McManus, Michael Boss, Bachir Taouli, Thomas Yankeelov, Christopher Chad Quarles, Kathleen Schmainda, Thomas Chenevert, David Newitt

JMI, Vol. 5, Issue 01, 011006, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011006

KEYWORDS: Diffusion weighted imaging, Diffusion, Digital imaging, Medicine, Data modeling, Data communications, MATLAB, Scanners, Standards development, Magnetic resonance imaging

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SPIE Journal Paper | 10 October 2017 Open Access

Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network

David Newitt, Dariya Malyarenko, Thomas Chenevert, C. Chad Quarles, Laura Bell, Andriy Fedorov, Fiona Fennessy, Michael Jacobs, Meiyappan Solaiyappan, Stefanie Hectors, Bachir Taouli, Mark Muzi, Paul Kinahan, Kathleen Schmainda, Melissa Prah, Erin Taber, Christopher Kroenke, Wei Huang, Lori Arlinghaus, Thomas Yankeelov, Yue Cao, Madhava Aryal, Yi-Fen Yen, Jayashree Kalpathy-Cramer, Amita Shukla-Dave, Maggie Fung, Jiachao Liang, Michael Boss, Nola Hylton

JMI, Vol. 5, Issue 01, 011003, (October 2017) https://doi.org/10.1117/12.10.1117/1.JMI.5.1.011003

KEYWORDS: Diffusion, In vivo imaging, Diffusion weighted imaging, Breast, MATLAB, Artificial intelligence, Magnetic resonance imaging, Radiology, Statistical analysis, Medicine

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