Simultaneous Estimation of Bias and Resolution in PET Images with a Long-Lived “Pocket” Phantom System

Kinahan, Paul E.; Byrd, Darrin; Helba, Brian; Wangerin, Kristen A.; Liu, Huan; Levy, Joshua; Allberg, Keith C.; Krishnan, Karthik; Avila, Ricardo S.

doi:10.18383/j.tom.2018.00004

Cited by 2 publications

(3 citation statements)

References 30 publications

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“…For each detected roll of tape, the algorithm identified the tape’s core inner cylindrical ring region, making sure not to include a central plastic structure. A multi-pass optimization algorithm ( 13 ) determined the best fit values for the 3D position, 3D orientation, and 3D Gaussian PSF sigmas (σ x , σ y , σ z ) of the actual scotch tape scan data, where σ x = σ y . Use of one sigma value to represent the in-plane resolution of the acquisition system is common for CT calibration and reasonable considering that virtually all thoracic CT scans currently use helical scanning.…”

Section: Methodsmentioning

confidence: 99%

Calibration phantom-based prediction of CT lung nodule volume measurement performance

Avila,

Krishnan,

Obuchowski

et al. 2023

Quant Imaging Med Surg

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Background A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. Methods The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. Results The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. Conclusions This is the first report of a calibration phantom-based method’s ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

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Section: Methodsmentioning

confidence: 99%

Calibration phantom-based prediction of CT lung nodule volume measurement performance

Avila,

Krishnan,

Obuchowski

et al. 2023

Quant Imaging Med Surg

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“…This method minimizes the cost function by doing a line search for each scalar parameter in turn. We made this modification because Powell's method has shown better convergence properties than the Nelder–Mead simplex method ( 29 ), which we had used previously ( 24 ). The image analysis algorithm was implemented using the 3D Slicer platform ( 30 , 31 ).…”

Section: Methodsmentioning

confidence: 99%

“…In this project, we use a PET/CT pocket phantom and associated analysis tools for quality assurance for 2 key image characteristics: the reconstructed image resolution and PET/CT scanner calibration. In our previous publication we introduced the pocket phantom ( 24 ) and briefly described web deployment ( 25 ).…”

Section: Introductionmentioning

confidence: 99%

Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms

et al. 2018

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Multicenter clinical trials that use positron emission tomography (PET) imaging frequently rely on stable bias in imaging biomarkers to assess drug effectiveness. Many well-documented factors cause variability in PET intensity values. Two of the largest scanner-dependent errors are scanner calibration and reconstructed image resolution variations. For clinical trials, an increase in measurement error significantly increases the number of patient scans needed. We aim to provide a robust quality assurance system using portable PET/computed tomography “pocket” phantoms and automated image analysis algorithms with the goal of reducing PET measurement variability. A set of the “pocket” phantoms was scanned with patients, affixed to the underside of a patient bed. Our software analyzed the obtained images and estimated the image parameters. The analysis consisted of 2 steps, automated phantom detection and estimation of PET image resolution and global bias. Performance of the algorithm was tested under variations in image bias, resolution, noise, and errors in the expected sphere size. A web-based application was implemented to deploy the image analysis pipeline in a cloud-based infrastructure to support multicenter data acquisition, under Software-as-a-Service (SaaS) model. The automated detection algorithm localized the phantom reliably. Simulation results showed stable behavior when image properties and input parameters were varied. The PET “pocket” phantom has the potential to reduce and/or check for standardized uptake value measurement errors.

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Simultaneous Estimation of Bias and Resolution in PET Images with a Long-Lived “Pocket” Phantom System

Cited by 2 publications

References 30 publications

Calibration phantom-based prediction of CT lung nodule volume measurement performance

Calibration phantom-based prediction of CT lung nodule volume measurement performance

Calibration Software for Quantitative PET/CT Imaging Using Pocket Phantoms

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