Experimental comparison of lesion detectability for four fully-3D PET reconstruction schemes

Kadrmas, Dan; Casey, Michael; Black, Noel; Hamill, James; Panin, Vladimir; Conti, Maurizio

doi:10.1109/nssmic.2007.4436724

Cited by 16 publications

(41 citation statements)

References 29 publications

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“…Kadrmas et al . (Kadrmas et al ., 2009a; Kadrmas et al ., 2009b) performed numeric channelized non-prewhitened (CNPW) (discussed below) as well as human observer studies of tumour detection using a multi-component whole-body phantom with realistic FDG uptake and including “shell-less” 68 Ge silicone gel lesions. Inclusion of RM led to statistically significant improvements in the context of localization ROC (LROC) analysis.…”

Section: Discussionmentioning

confidence: 99%

Noise propagation in resolution modeled PET imaging and its impact on detectability

Rahmim

Tang

2013

Phys. Med. Biol.

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PET imaging is affected by a number of resolution degrading phenomena, including positron range, photon non-collinearity and inter-crystal blurring. An approach to this issue is to model some or all of these effects within the image reconstruction task, referred to as resolution modeling (RM). This approach is commonly observed to yield images of higher resolution and subsequently contrast, and can be thought of as improving the modulation transfer function (MTF). Nonetheless, RM can substantially alter the noise distribution. In this work, we utilize noise propagation models in order to accurately characterize the noise texture of reconstructed images in the presence of RM. Furthermore we consider the task of lesion or defect detection, which is highly determined by the noise distribution as quantified using the noise power spectrum (NPS). Ultimately, we use this framework to demonstrate why conventional trade-off analyses (e.g. contrast vs. noise, using simplistic noise metrics) do not provide a complete picture of the impact of RM and that improved performance of RM according to such analyses does not necessarily translate to the superiority of RM in detection task performance.”

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Section: Discussionmentioning

confidence: 99%

Noise propagation in resolution modeled PET imaging and its impact on detectability

Rahmim

Tang

2013

Phys. Med. Biol.

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“…Additional details regarding the CNPW observer and its training and application to our experimental phantom data can be found in [5–8, 13, 17]. Of note, the observer template was the same size (44mm × 44mm) for both the 2mm and 4mm pixel images, and hence contained twice as many pixels for the 2mm case.…”

Section: Methodsmentioning

confidence: 99%

“…The fraction of lesions found (P LOC ) and area under the LROC curve (A LROC ) for each observer were also computed for each observer, and then averaged to obtain the final results. The methods used were the same as those previously developed and used for the Utah PET Lesion Detection Database Resource, and additional details can be found in [5, 6]. As with the CNPW observer results, a paired-sample Tukey HSD multiple-comparison test was performed to determine statistically-significant differences in A LROC for the different pixel sizes.…”

Section: Methodsmentioning

confidence: 99%

“…Our group has developed techniques for evaluating general oncologic lesion-detectability in PET using whole-body phantom experiments [5–8], and these methodologies and data have been combined into the Utah PET Lesion Detection Database Resource [9]. The resource contains experimental data useful for performing localization receiver operating characteristics (LROC) studies [10–12] with both the channelized nonprewhitened (CNPW) mathematical observer [13] and human observers.…”

Section: Introductionmentioning

confidence: 99%

“…The resource contains experimental data useful for performing localization receiver operating characteristics (LROC) studies [10–12] with both the channelized nonprewhitened (CNPW) mathematical observer [13] and human observers. These data and LROC methods have been previously used to evaluate the effects of modeling the point spread function (PSF) [5], TOF [6], and varying the number of OSEM subsets used for iterative reconstruction [8]. …”

Section: Introductionmentioning

confidence: 99%

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Effect of Using 2 mm Voxels on Observer Performance for PET Lesion Detection

Morey

Noo

Kadrmas

2016

IEEE Trans. Nucl. Sci.

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Positron emission tomography (PET) images are typically reconstructed with an in-plane pixel size of approximately 4mm for cancer imaging. The objective of this work was to evaluate the effect of using smaller pixels on general oncologic lesion-detection. A series of observer studies was performed using experimental phantom data from the Utah PET Lesion Detection Database, which modeled whole-body FDG PET cancer imaging of a 92kg patient. The data comprised 24 scans over 4 days on a Biograph mCT time-of-flight (TOF) PET/CT scanner, with up to 23 lesions (diam. 6–16mm) distributed throughout the phantom each day. Images were reconstructed with 2.036mm and 4.073mm pixels using ordered-subsets expectation-maximization (OSEM) both with and without point spread function (PSF) modeling and TOF. Detection performance was assessed using the channelized non-prewhitened numerical observer with localization receiver operating characteristic (LROC) analysis. Tumor localization performance and the area under the LROC curve were then analyzed as functions of the pixel size. In all cases, the images with ~2mm pixels provided higher detection performance than those with ~4mm pixels. The degree of improvement from the smaller pixels was larger than that offered by PSF modeling for these data, and provided roughly half the benefit of using TOF. Key results were confirmed by two human observers, who read subsets of the test data. This study suggests that a significant improvement in tumor detection performance for PET can be attained by using smaller voxel sizes than commonly used at many centers. The primary drawback is a 4-fold increase in reconstruction time and data storage requirements.

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Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs

2020

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Background: In oncology, lesion characterization is essential for tumor grading, treatment planning, and follow-up of cancer patients. Hybrid imaging systems, such as Single Photon Emission Computed Tomography (SPECT)/CT, Positron Emission Tomography (PET)/CT, or PET/magnetic resonance imaging (MRI), play an essential role for the noninvasive quantification of tumor characteristics. However, most of the existing approaches are challenged by intra-and intertumor heterogeneity. Novel quantitative imaging parameters that can be derived from textural feature analysis (as part of radiomics) are promising complements for improved characterization of tumor heterogeneity, thus, supporting clinically relevant implementations of personalized medicine concepts. Nevertheless, establishing new quantitative parameters for tumor characterization requires the use of standardized imaging objects to test the reliability of results prior to their implementation in patient studies. Methods: In this review, we summarize existing reports on heterogeneous phantoms with a focus on simulating tumor heterogeneity. We discuss the techniques, materials, advantages, and limitations of the existing phantoms for PET, CT, and MR imaging modalities. Conclusions: Finally, we outline the future directions and requirements for the design of cross modality imaging phantoms.

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Experimental comparison of lesion detectability for four fully-3D PET reconstruction schemes

Cited by 16 publications

References 29 publications

Noise propagation in resolution modeled PET imaging and its impact on detectability

Noise propagation in resolution modeled PET imaging and its impact on detectability

Effect of Using 2 mm Voxels on Observer Performance for PET Lesion Detection

Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs

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