Kinahan, Paul E.

PurposeA Positron Emission Tomography/Computed Tomography quality assurance program is necessary to ensure that patients receive optimal imaging and care. We summarize the AAPM Task Group (TG) 126 report on acceptance and quality assurance (QA) testing of PET/CT systems.MethodsTG 126 was charged with developing PET/CT acceptance testing and QA procedures. The TG aimed to develop procedures that would allow for standardized evaluation of existing short‐axis cylindrical‐bore PET/CT systems in the spirit of NEMA NU 2 standards without requiring specialized phantoms or proprietary software tools.ResultsWe outline eight performance evaluations using common phantoms and freely available software whereby the clinical physicist monitors each PET/CT system by comparing periodic Follow‐Up Measurements to Baseline Measurements acquired during acceptance testing. For each of the eight evaluations, we also summarize the expected testing time and materials necessary and the recommended pass/fail criteria.ConclusionOur report provides a guideline for periodic evaluations of most clinical PET/CT systems that simplifies procedures and requirements outlined by other agencies and will facilitate performance comparisons across vendors, models, and institutions.

PurposePhysical and digital phantoms play a key role in the development and testing of nuclear medicine instrumentation and processing algorithms for clinical and research applications, including neuroimaging using positron emission tomography (PET). We have developed and tested a digital reference object (DRO) version of the original segmented magnetic resonance imaging (MRI) data used for the three‐dimensional (3D) PET brain phantom developed by Hoffman et al., which is used as the basis of a commercially available physical test phantom.MethodsThe DRO was constructed by subdividing the MRI image planes the original phantom was based on to create equal‐thickness slices and re‐labeling voxels. The digital data was then embedded in a PET Digital Imaging and Communications in Medicine format and tested for compliance.ResultsWe then tested the DRO by comparing it to computed tomography (CT) images of the physical phantom summed to form composite slices with axial extent similar to the DRO, but with a factor of two better in‐slice resolution. For composite slices, 91% of voxels were labeled in full agreement, 5% of the voxels were 50–75% accurate, and the remaining 4% of voxels had 25% or less agreement.ConclusionsThis DRO can be used as an input for PET scanner simulation studies or for comparing simulations to measured Hoffman phantom images.

Abstract With mean survival from glioblastoma multiforme (GBM) barely edging over 15 months, it become imperative to develop novel tools informing on both progression and treatment efficacy. As the aggressive growth of GBM outstrips available resources, regions of hypoxia develop. Hypoxia can be clinically assessed through [18F]-flouromisonidazole (FMISO) PET. However, PET imaging suffers from low resolution and reconstruction artifact. We present the comparative and predictive results of a multi-stage model utilizing the mathematical biology of cancer and the physics of PET imaging. Our mathematical model of GBM characterizes cancer cells into the interacting phenotypes of normoxic, hypoxic, and necrotic cells supported by vasculature. The pharmacokinetic activity of FMISO is induced in the simulated tissue then acquired and reconstructed by an analytic simulator of the PET process. This multi-stage process creates a patient-specific virtual PET images with characteristic of the clinical PET scan. Extending previous work, we model the full three-dimensional dynamics of tumor progression, pharmacokinetic activity, and FMISO PET for the tumor kinetics of six GBM patients. Virtual and clinical FMISO PET images are compared. Overall hypoxic burden and spatial distribution show strong correspondence between virtual and clinical FMISO PET images for all six patients. Simulated FMISO PET dynamics, with tumor kinetics derived from routine clinical MRI scans, provide a unique and evolving tool giving insight into the biological connections between magnetic resonance and molecular imaging. Citation Format: Joshua Jacobs, Andrea Hawkins-Darrud, Robert Harrison, Sandra Johnston, Paul Kinahan, Kristin Swanson. Correlating magnetic resonance and molecular imaging using three dimensional untreated virtual control. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A10.

Abstract As anticancer therapies designed to target specific molecular pathways have been developed, it has become critical to develop methods to assess the response induced by such agents. Although traditional, anatomic CT, and MRI examinations are useful in many settings, increasing evidence suggests that these methods cannot answer the fundamental biologic and physiologic questions essential for assessment and, eventually, prediction of treatment response in the clinical trial setting, especially in the critical period soon after treatment is initiated. To optimally apply advances in quantitative imaging methods to trials of targeted cancer therapy, new infrastructure improvements are needed that incorporate these emerging techniques into the settings where they are most likely to have impact. In this review, we first elucidate the needs for therapeutic response assessment in the era of molecularly targeted therapy and describe how quantitative imaging can most effectively provide scientifically and clinically relevant data. We then describe the tools and methods required to apply quantitative imaging and provide concrete examples of work making these advances practically available for routine application in clinical trials. We conclude by proposing strategies to surmount barriers to wider incorporation of these quantitative imaging methods into clinical trials and, eventually, clinical practice. Our goal is to encourage and guide the oncology community to deploy standardized quantitative imaging techniques in clinical trials to further personalize care for cancer patients and to provide a more efficient path for the development of improved targeted therapies. Clin Cancer Res; 22(2); 284–90. ©2016 AACR.