Recently, a class of no-gold-standard (NGS) techniques have been proposed to evaluate quantitative imaging methods using patient data. These techniques provide figures of merit (FoMs) quantifying the precision of the estimated quantitative value without requiring repeated measurements and without requiring a gold standard. However, applying these techniques to patient data presents several practical difficulties including assessing the underlying assumptions, accounting for patient-sampling-related uncertainty, and assessing the reliability of the estimated FoMs. To address these issues, we propose statistical tests that provide confidence in the underlying assumptions and in the reliability of the estimated FoMs. Furthermore, the NGS technique is integrated within a bootstrap-based methodology to account for patient-sampling-related uncertainty. The developed NGS framework was applied to evaluate four methods for segmenting lesions from F-Fluoro-2-deoxyglucose positron emission tomography images of patients with head-and-neck cancer on the task of precisely measuring the metabolic tumor volume. The NGS technique consistently predicted the same segmentation method as the most precise method. The proposed framework provided confidence in these results, even when gold-standard data were not available. The bootstrap-based methodology indicated improved performance of the NGS technique with larger numbers of patient studies, as was expected, and yielded consistent results as long as data from more than 80 lesions were available for the analysis.
We propose a generalized resolution modeling (RM) framework, including extensive task-based optimization,
wherein we continualize the conventionally discrete framework of RM vs. no RM, to include varying degrees of RM.
The proposed framework has the advantage of providing a trade-off between the enhanced contrast recovery by RM and
the reduced inter-voxel correlations in the absence of RM, and to enable improved task performance. The investigated
context was that of oncologic lung FDG PET imaging. Given a realistic blurring kernel of FWHM h (‘true PSF’), we
performed iterative EM including RM using a wide range of ‘modeled PSF’ kernels with varying widths h. In our
simulations, h = 6mm, while h varied from 0 (no RM) to 12mm, thus considering both underestimation and
overestimation of the true PSF. Detection task performance was performed using prewhitened (PWMF) and nonprewhitened
matched filter (NPWMF) observers. It was demonstrated that an underestimated resolution blur (h = 4mm)
enhanced task performance, while slight over-estimation (h = 7mm) also achieved enhanced performance. The latter is
ironically attributed to the presence of ringing artifacts. Nonetheless, in the case of the NPWMF, the increasing intervoxel
correlations with increasing values of h degrade detection task performance, and underestimation of the true PSF
provides the optimal task performance. The proposed framework also achieves significant improvement of
reproducibility, which is critical in quantitative imaging tasks such as treatment response monitoring.
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