3D task‐transfer function representation of the signal transfer properties of low‐contrast lesions in FBP‐ and iterative‐reconstructed CT

Robins, Marthony; Solomon, Justin; Richards, Taylor; Samei, Ehsan

doi:10.1002/mp.13205

Cited by 13 publications

(9 citation statements)

References 13 publications

(22 reference statements)

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“…Future directions might include further advanced experiments since various innovative approaches are being developed. 3D TTF is an example [ 39 ] that would provide an estimation of spatial resolution not only in-plane but also in the z-direction; the latter is relevant because it may differ from the TTF measured in the x-y plane, especially in a non-linear environment such as when using iterative reconstruction. However, specific methods need to be developed to achieve 3D TTF analysis.…”

Section: Discussionmentioning

confidence: 99%

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

et al. 2021

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Aims: To evaluate spectral photon-counting CT’s (SPCCT) objective image quality characteristics in vitro, compared with standard-of-care energy-integrating-detector (EID) CT. Methods: We scanned a thorax phantom with a coronary artery module at 10 mGy on a prototype SPCCT and a clinical dual-layer EID-CT under various conditions of simulated patient size (small, medium, and large). We used filtered back-projection with a soft-tissue kernel. We assessed noise and contrast-dependent spatial resolution with noise power spectra (NPS) and target transfer functions (TTF), respectively. Detectability indices (d’) of simulated non-calcified and lipid-rich atherosclerotic plaques were computed using the non-pre-whitening with eye filter model observer. Results: SPCCT provided lower noise magnitude (9–38% lower NPS amplitude) and higher noise frequency peaks (sharper noise texture). Furthermore, SPCCT provided consistently higher spatial resolution (30–33% better TTF10). In the detectability analysis, SPCCT outperformed EID-CT in all investigated conditions, providing superior d’. SPCCT reached almost perfect detectability (AUC ≈ 95%) for simulated 0.5-mm-thick non-calcified plaques (for large-sized patients), whereas EID-CT had lower d’ (AUC ≈ 75%). For lipid-rich atherosclerotic plaques, SPCCT achieved 85% AUC vs. 77.5% with EID-CT. Conclusions: SPCCT outperformed EID-CT in detecting simulated coronary atherosclerosis and might enhance diagnostic accuracy by providing lower noise magnitude, markedly improved spatial resolution, and superior lipid core detectability.

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Section: Discussionmentioning

confidence: 99%

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

et al. 2021

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“…Lastly, the task‐transfer function (TTF) was computed. The TTF is a type of modulation‐transfer function, which is also valid for nonlinear systems and incorporates contrast and noise 24 . For this, the ICRU implementation for modulation transfer function calculation was used 22 .…”

Section: Methodsmentioning

confidence: 99%

“…The TTF is a type of modulation-transfer-function, which is also valid for non-linear systems and incorporates contrast and noise. 24 For this, the ICRU implementation for modulation transfer function calculation was used. 22 For robustness, the TTF was calculated by radially averaging the edge-spread-function (ESF) of the calibration rod, as described previously Accepted Article by Van Ommen et al 23 Due to the proximity of the water-equivalent calibration rod, the ESF in the direction of this rod were excluded from the analysis.…”

Section: Image Quality Assessmentmentioning

confidence: 99%

Fully automated quantification method (FQM) of coronary calcium in an anthropomorphic phantom

et al. 2021

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ObjectiveCoronary artery calcium (CAC) score is a strong predictor for future adverse cardiovascular events.Anthropomorphic phantoms are often used for CAC studies on computed tomography (CT) to allow for evaluation or variation of scanning or reconstruction parameters within or across scanners against a reference standard. This often results in large number of datasets. Manual assessment of these large datasets is time consuming and cumbersome. Therefore, this study aimed to develop and validate a fully automated, open-source quantification method (FQM) for coronary calcium in a standardized phantom. Materials and MethodsA standard, commercially available anthropomorphic thorax phantom was used with an insert containing nine calcifications with different sizes and densities. To simulate two different patient sizes, an extension ring was used. Image data was acquired with four state-of-the-art CT systems using routine CAC scoring acquisition protocols. For inter-scan variability, each acquisition was repeated five times with small translations and/or rotations. Vendor-specific CAC scores (Agatston, volume, and mass) were calculated as reference scores using vendor-specific software. Both the international standard CAC quantification methods as well as vendorspecific adjustments were implemented in FQM. Reference and FQM scores were compared using Bland-Altman analysis, intraclass correlation coefficients, risk reclassifications, and Cohen's kappa. Also, robustness of FQM was assessed using varied acquisitions and reconstruction settings and validation on a dynamic phantom. Further, image quality metrics were implemented: noise power spectrum, task transfer function, and contrast-and signal-to-noise ratio among others. Results were validated using imQuest software. ResultsThree parameters in CAC scoring methods varied among the different vendor-specific software packages: the Hounsfield unit (HU) threshold, the minimum area used to designate a group of voxels as calcium, and the usage of isotropic voxels for the volume score. The FQM was in high agreement with vendor-specific scores and ICC's (median [95% CI]) were excellent (1.

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“…The SVM training and testing dataset comprised NPS and TTF results measured from scans of a 3D-printed low-contrast insert phantom as described in a previous publication. 13 An example of two sections of the phantom is shown in Figure 3. The cylindrical phantom contained a section with two cylindrical low-contrast inserts (Figure 3, left) for measuring the low-contrast TTF in the axial plane, as well as 24 anthropomorphic "lesions," which were not used in this study.…”

Section: Validation Of the Low-contrast Resolution Estimation Methodo...mentioning

confidence: 99%

Development, validation, and relevance of in vivo low‐contrast task transfer function to estimate detectability in clinical CT images

et al. 2021

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The current state-of -the-art calculation of detectability index (d') is largely phantom-based, with the latest being based on a hybrid phantom noise power spectrum (NPS) combined with patient-specific noise magnitude and high-contrast air-skin interface. The purpose of this study was to develop and assess the use of fully patient-specific measurements of noise and low-contrast resolution, derived entirely from patient images on d'. Methods: This study developed a d' calculation that is patient-and task-specific, employing newly developed algorithms for estimating patient-specific NPS and low-contrast task transfer function (TTF).The TTF estimation methodology used a trained regression support vector machine (SVM) to estimate a fitted form of the TTF given a variance-normalized estimate of the NPS (referred to as the TTF NPS ). The regression SVM was trained and tested using five-fold crossvalidation on 192 scans (4 dose levels x 6 reconstruction kernels x 4 repeats) of a phantom with low-contrast polyethylene insert and reconstructed with filtered backprojection and iterative reconstructions across 12 clinically relevant kernels (FBP: B20f, B31f, B45f; SAFIRE: I26f, I31f, J45f with strengths: 2, 3, 5). To test the low-contrast TTF estimation method, the estimated TTF NPS measurements were compared to (1) TTF measurements from the air-phantom interface (referred to as the TTF air , representing the most patient-specific clinical alternative) and (2) TTF measurements from the edge of the low-contrast polyethylene insert (referred to as the TTF poly ), which represented the gold standard of lowcontrast TTF measurement. Patient-specific NPS, patient-specific noise magnitude, and patient-specific low-contrast TTF were further combined with a reference task function to calculate a d' (according to a non-prewhitening matched filter model) across 1120 lesions previously evaluated in 2AFC human observer detection of liver lesions. The resulting values were compared to the observer results using a generalized linear mixed-effects statistical model. The correlations between the model and observer results were also compared with previously reported values (using a hybrid method with phantom-derived NPS and TTF air ). Results:The TTF NPS more accurately represented resolution across the considered reconstruction settings, compared with the TTF air . The out-of -fold predictions of the TTF NPS had statistically better root-mean-square error concordance (p < 0.05, one-tailed Wilcoxon rank-sum test) to gold standard than the TTF air (the alternative, measured from the air-phantom interface). Detectability indices informed by purely patient-specific NPS and TTF were strongly correlated with 2AFC outcomes (p < 0.05). R 2 between human detection accuracy 7698

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3D task‐transfer function representation of the signal transfer properties of low‐contrast lesions in FBP‐ and iterative‐reconstructed CT

Abstract: The visual appearance, edge blur, size, and shape of simulated lesions were similar to the physical lesions, which suggests 3D-TTF models the low-contrast signal transfer properties of this non-linear CT system reasonably well.

Cited by 13 publications

References 13 publications

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

Performance of Spectral Photon-Counting Coronary CT Angiography and Comparison with Energy-Integrating-Detector CT: Objective Assessment with Model Observer

Fully automated quantification method (FQM) of coronary calcium in an anthropomorphic phantom

Development, validation, and relevance of in vivo low‐contrast task transfer function to estimate detectability in clinical CT images

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