Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method

Wangerin, Kristen A.; Ahn, Sangtae; Wollenweber, Scott D.; Ross, Steven G.; Kinahan, Paul E.; Manjeshwar, Ravindra

doi:10.1117/1.jmi.4.1.011002

Cited by 31 publications

(49 citation statements)

References 46 publications

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“…Assessing the impact of these areas on image-based tasks can be time-consuming and difficult. Our solution to evaluate both SUV and kinetic parameter metrics was to use a virtual clinical trial (VCT) approach, which uses a series of linked simulations to evaluate the impact of steps in the entire imaging process on each metric (Moore et al 2012, Kurland et al 2013, 2016, Harrison et al 2014, Maidment 2014, Rashidnasab et al 2015, Wangerin et al 2015, 2017, Häggström et al 2016). We first defined ground truth based on data from prior patient studies.…”

Section: Introductionmentioning

confidence: 99%

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy

et al. 2017

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We developed a method to evaluate variations in the PET imaging process in order to characterize the relative ability of static and dynamic metrics to measure breast cancer response to therapy in a clinical trial setting. We performed a virtual clinical trial by generating 540 independent and identically distributed PET imaging study realizations for each of 22 original dynamic fluorodeoxyglucose (18F-FDG) breast cancer patient studies pre- and post-therapy. Each noise realization accounted for known sources of uncertainty in the imaging process, such as biological variability and SUV uptake time. Four definitions of SUV were analyzed, which were SUVmax, SUVmean, SUVpeak, and SUV50%. We performed a ROC analysis on the resulting SUV and kinetic parameter uncertainty distributions to assess the impact of the variability on the measurement capabilities of each metric. The kinetic macro parameter, Ki, showed more variability than SUV (mean CV Ki = 17%, SUV = 13%), but Ki pre- and post-therapy distributions also showed increased separation compared to the SUV pre- and post-therapy distributions (mean normalized difference Ki = 0.54, SUV = 0.27). For the patients who did not show perfect separation between the pre- and post-therapy parameter uncertainty distributions (ROC AUC < 1), dynamic imaging outperformed SUV in distinguishing metabolic change in response to therapy, ranging from 12 to 14 of 16 patients over all SUV definitions and uptake time scenarios (p < 0.05). For the patient cohort in this study, which is comprised of non-high-grade ER+ tumors, Ki outperformed SUV in an ROC analysis of the parameter uncertainty distributions pre- and post-therapy. This methodology can be applied to different scenarios with the ability to inform the design of clinical trials using PET imaging.

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Section: Introductionmentioning

confidence: 99%

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy

et al. 2017

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“…Using this tool, we employed a technique that specifies the activity distribution and positional location of the lesion with respect to the reconstructed target patient image [27]. The projector generates TOF projections for one phi angle at a time taking into account specific detector efficiencies (intrinsic and geometric), patient attenuation and scanner resolution (PSF) [27]. During the image reconstruction process, TOF patient projections were combined with simulated TOF lesion projections.…”

Section: Methods -Lesion Insertion In Petmentioning

confidence: 99%

“…expert panels) (purple in Figure 9), are advantageous for their clinical realism, but can be tedious to obtain and they are not useful for optimizing steps leading up to image generation [24]. Furthermore, the reference truth is limited by the accuracy of the viewers and becomes exceedingly unreliable as lesions become more difficult to perceive, as they approach the LOD [27]. Surrogate markers such as other imaging modalities, histopathology, or outcomes are also of limited value for optimizing lesion detection with PET because they are less sensitive than PET for tumor detection, they do not correlate sufficiently with PET, and they may accurately portray lesion properties.…”

Section: Figure 9: Steps In Medical Imaging For Lesion Detection Andmentioning

confidence: 99%

“…Digital lesion synthesis is a compromise that inserts artificial lesion data into real patient data (red workflow in Figure 9). Lesion synthesis has been successfully applied for characterizing lesion perception tasks in various imaging modalities [23], including few PET studies [19], [22], [26], [27].…”

Section: Figure 9: Steps In Medical Imaging For Lesion Detection Andmentioning

confidence: 99%

“…Like Monte Carlo based lesion synthesis an activity distribution map and patient attenuation information are required for lesion synthesis. Activity maps were volumetric images in units of Bq/cc and attenuation was derived by the toolbox for CT images [27].…”

Section: Lesion Synthesis -Analytical 35mentioning

confidence: 99%

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Lesion Synthesis Toolbox: Development and Validation of Dedicated Software for Synthesis of Lesions in Raw PET and CT Patient Images

Juma¹

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Positron Emission Tomography (PET) is one of the most sensitive clinical tools for early detections of small tumours, but its performance is dependent on multiple factors including image quality and human perception. Current methods relying on physical and numerical image quality phantoms are inadequate for evaluating clinical task-based performance, such as limits of lesion detection, due to lack of physiologic realism of the images.In this work, we describe development and validation of the Lesion Synthesis Toolbox, easy-to-use software to synthetize well-characterized, user-defined lesions in real patient PET data prior to image reconstruction and in corresponding Computed Tomography (CT) images on GE Discovery line of PET/CT scanners.We employ synthetic lesions in a preliminary perception study to estimate the limits-of-detection (LOD) in representative clinical setting. Finally, we describe a custom developed, web-based tool for conducting perception studies using synthetic lesions of varying parameters to characterize the LOD. We conclude with a discussion of how these tools can be employed in future studies to compare LOD between imaging methods, image generation algorithms, observers, display types, and computer-based lesion detection solutions.ii

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Assessment of lesion insertion tool in pelvis PET/MR data with applications to attenuation correction method development

Natsuaki,

Leynes,

Wangerin

et al. 2024

J Applied Clin Med Phys

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BackgroundIn modern positron emission tomography (PET) with multi‐modality imaging (e.g., PET/CT and PET/MR), the attenuation correction (AC) is the single largest correction factor for image reconstruction. One way to assess AC methods and other reconstruction parameters is to utilize software‐based simulation tools, such as a lesion insertion tool. Extensive validation of these simulation tools is required to ensure results of the study are clinically meaningful.PurposeTo evaluate different PET AC methods using a synthetic lesion insertion tool that simulates lesions in a patient cohort that has both PET/MR and PET/CT images. To further demonstrate how lesion insertion tool may be used to extend knowledge of PET reconstruction parameters, including but not limited to AC.MethodsLesion quantitation is compared using conventional Dixon‐based MR‐based AC (MRAC) to that of using CT‐based AC (CTAC, a “ground truth”). First, the pre‐existing lesions were simulated in a similar environment; a total of 71 lesions were identified in 18 pelvic PET/MR patient images acquired with a time‐of‐flight simultaneous PET/MR scanner, and matched lesions were inserted contralaterally on the same axial slice. Second, synthetic lesions were inserted into four anatomic target locations in a cohort of four patients who didn't have any observed clinical lesions in the pelvis.ResultsThe matched lesion insertions resulted in unity between the lesion error ratios (mean SUVs), demonstrating that the inserted lesions successfully simulated the original lesions. In the second study, the inserted lesions had distinct characteristics by target locations and demonstrated negative max‐SUV%diff trends for bone‐dominant sites across the patient cohort.ConclusionsThe current work demonstrates that the applied lesion insertion tool can simulate uptake in pelvic lesions and their expected SUV values, and that the lesion insertion tool can be extended to evaluate further PET reconstruction corrections and algorithms and their impact on quantitation accuracy and precision.

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Evaluation of lesion detectability in positron emission tomography when using a convergent penalized likelihood image reconstruction method

Cited by 31 publications

References 46 publications

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy

A virtual clinical trial comparing static versus dynamic PET imaging in measuring response to breast cancer therapy

Lesion Synthesis Toolbox: Development and Validation of Dedicated Software for Synthesis of Lesions in Raw PET and CT Patient Images

Assessment of lesion insertion tool in pelvis PET/MR data with applications to attenuation correction method development

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