Joint EANM/SNMMI guideline on radiomics in nuclear medicine : Jointly supported by the EANM Physics Committee and the SNMMI Physics, Instrumentation and Data Sciences Council.

PURPOSE: The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches. METHODS: In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives. CONCLUSION: Radiomics is a very quickly evolving field of research. The present guideline focused on established findings as well as recommendations based on the state of the art. Though this guideline recognizes both hand-crafted and deep learning-based radiomics approaches, it primarily focuses on the former as this field is more mature. This guideline will be updated once more studies and results have contributed to improved consensus regarding the application of deep learning methods for radiomics. Although methodological recommendations in the present document are valid for most medical image modalities, we focus here on nuclear medicine, and specific recommendations when necessary are made for PET/CT, PET/MR, and quantitative SPECT.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets.

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography-computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Analytic system matrix resolution modeling in PET: an application to Rb-82 cardiac imaging.

This work explores application of a novel resolution modeling technique based on analytic physical models which individually models the various resolution degrading effects in PET (positron range, photon non-collinearity, inter-crystal scattering and inter-crystal penetration) followed by their combination and incorporation within the image reconstruction task. In addition to phantom studies, the proposed technique was particularly applied to and studied in the task of clinical Rb-82 myocardial perfusion imaging, which presently suffers from poor statistics and resolution properties in the reconstructed images. Overall, the approach is able to produce considerable enhancements in image quality. The reconstructed FWHM for a Discovery RX PET/CT scanner was seen to improve from 5.1 mm to 7.7 mm across the field-of-view (FoV) to approximately 3.5 mm nearly uniformly across the FoV. Furthermore, extended-source phantom studies indicated clearly improved images in terms of contrast versus noise performance. Using Monte Carlo simulations of clinical Rb-82 imaging, the resolution modeling technique was seen to significantly outperform standard reconstructions qualitatively, and also quantitatively in terms of contrast versus noise (contrast between the myocardium and other organs, as well as between myocardial defects and the left ventricle).

Estimation of dynamic time activity curves from dynamic cardiac SPECT imaging.

Whole-heart coronary flow reserve (CFR) may be useful as an early predictor of cardiovascular disease or heart failure. Here we propose a simple method to extract the time-activity curve, an essential component needed for estimating the CFR, for a small number of compartments in the body, such as normal myocardium, blood pool, and ischemic myocardial regions, from SPECT data acquired with conventional cameras using slow rotation. We evaluated the method using a realistic simulation of (99m)Tc-teboroxime imaging. Uptake of (99m)Tc-teboroxime based on data from the literature were modeled. Data were simulated using the anatomically-realistic 3D NCAT phantom and an analytic projection code that realistically models attenuation, scatter, and the collimator-detector response. The proposed method was then applied to estimate time activity curves (TACs) for a set of 3D volumes of interest (VOIs) directly from the projections. We evaluated the accuracy and precision of estimated TACs and studied the effects of the presence of perfusion defects that were and were not modeled in the estimation procedure.The method produced good estimates of the myocardial and blood-pool TACS organ VOIs, with average weighted absolute biases of less than 5% for the myocardium and 10% for the blood pool when the true organ boundaries were known and the activity distributions in the organs were uniform. In the presence of unknown perfusion defects, the myocardial TAC was still estimated well (average weighted absolute bias <10%) when the total reduction in myocardial uptake (product of defect extent and severity) was ≤ 5%. This indicates that the method was robust to modest model mismatch such as the presence of moderate perfusion defects and uptake nonuniformities. With larger defects where the defect VOI was included in the estimation procedure, the estimated normal myocardial and defect TACs were accurate (average weighted absolute bias ≈ 5% for a defect with 25% extent and 100% severity).

Statistical list-mode image reconstruction for the high resolution research tomograph.

We have investigated statistical list-mode reconstruction applicable to a depth-encoding high resolution research tomograph. An image non-negativity constraint has been employed in the reconstructions and is shown to effectively remove the overestimation bias introduced by the sinogram non-negativity constraint. We have furthermore implemented a convergent subsetized (CS) list-mode reconstruction algorithm, based on previous work (Hsiao et al 2002 Conf. Rec. SPIE Med. Imaging 4684 10-19; Hsiao et al 2002 Conf. Rec. IEEE Int. Symp. Biomed. Imaging 409-12) on convergent histogram OSEM reconstruction. We have demonstrated that the first step of the convergent algorithm is exactly equivalent (unlike the histogram-mode case) to the regular subsetized list-mode EM algorithm, while the second and final step takes the form of additive updates in image space. We have shown that in terms of contrast, noise as well as FWHM width behaviour, the CS algorithm is robust and does not result in limit cycles. A hybrid algorithm based on the ordinary and the convergent algorithms is also proposed, and is shown to combine the advantages of the two algorithms (i.e. it is able to reach a higher image quality in fewer iterations while maintaining the convergent behaviour), making the hybrid approach a good alternative to the ordinary subsetized list-mode EM algorithm.