PET textural features stability and pattern discrimination power for radiomics analysis: An "ad-hoc" phantoms study.

PURPOSE: The analysis of PET images by textural features, also known as radiomics, shows promising results in tumor characterization. However, radiomic metrics (RMs) analysis is currently not standardized and the impact of the whole processing chain still needs deep investigation. We characterized the impact on RM values of: i) two discretization methods, ii) acquisition statistics, and iii) reconstruction algorithm. The influence of tumor volume and standardized-uptake-value (SUV) on RM was also investigated. METHODS: The Chang-Gung-Image-Texture-Analysis (CGITA) software was used to calculate 39 RMs using phantom data. Thirty noise realizations were acquired to measure statistical effect size indicators for each RM. The parameter η2 (fraction of variance explained by the nuisance factor) was used to assess the effect of categorical variables, considering η2 < 20% and 20% < η2 < 40% as representative of a "negligible" and a "small" dependence respectively. The Cohen's d was used as discriminatory power to quantify the separation of two distributions. RESULTS: We found the discretization method based on fixed-bin-number (FBN) to outperform the one based on fixed-bin-size in units of SUV (FBS), as the latter shows a higher SUV dependence, with 30 RMs showing η2 > 20%. FBN was also less influenced by the acquisition and reconstruction setup:with FBN 37 RMs had η2 < 40%, only 20 with FBS. Most RMs showed a good discriminatory power among heterogeneous PET signals (for FBN: 29 out of 39 RMs with d > 3). CONCLUSIONS: For RMs analysis, FBN should be preferred. A group of 21 RMs was suggested for PET radiomics analysis.

4D ML reconstruction as a tool for volumetric PET-based treatment verification in ion beam radiotherapy.

PURPOSE: An innovative strategy to improve the sensitivity of positron emission tomography (PET)-based treatment verification in ion beam radiotherapy is proposed. METHODS: Low counting statistics PET images acquired during or shortly after the treatment (Measured PET) and a Monte Carlo estimate of the same PET images derived from the treatment plan (Expected PET) are considered as two frames of a 4D dataset. A 4D maximum likelihood reconstruction strategy was adapted to iteratively estimate the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected PET and Measured PET frames. The outputs generated by the proposed strategy are as follows: (1) an estimate of the Measured PET with an image quality comparable to the Expected PET and (2) an estimate of the motion field mapping Expected PET to Measured PET. The details of the algorithm are presented and the strategy is preliminarily tested on analytically simulated datasets. RESULTS: The algorithm demonstrates (1) robustness against noise, even in the worst conditions where 1.5 × 10(4) true coincidences and a random fraction of 73% are simulated; (2) a proper sensitivity to different kind and grade of mismatches ranging between 1 and 10 mm; (3) robustness against bias due to incorrect washout modeling in the Monte Carlo simulation up to 1/3 of the original signal amplitude; and (4) an ability to describe the mismatch even in presence of complex annihilation distributions such as those induced by two perpendicular superimposed ion fields. CONCLUSIONS: The promising results obtained in this work suggest the applicability of the method as a quantification tool for PET-based treatment verification in ion beam radiotherapy. An extensive assessment of the proposed strategy on real treatment verification data is planned.

Motion-Tracking Hardware and Advanced Applications in PET and PET/CT.

Respiratory and cardiac motions represent important sources of image degradation in both PET and computed tomography (CT) studies that need to be taken into account and compensated to improve image quality and quantitative accuracy. This review describes the hardware needed to perform respiratory and cardiac gating with PET and PET/CT systems. In particular, most of the proposed motion-tracking devices for the management of respiratory, cardiac, and multidimensional movements are described and compared. Some advanced applications in PET and PET/CT made possible by the gating technology are considered and analyzed.

ML segmentation strategies for object interference compensation in FDG-PET lesion quantification.

BACKGROUND: Quantification of lesion activity by FDG uptake in oncological PET is severely limited by partial volume effects. A maximum likelihood (ML) expectation maximization (EM) algorithm considering regional basis functions (AWOSEM-region) had been previously developed. Regional basis functions are iteratively segmented and quantified, thus identifying the volume and the activity of the lesion. OBJECTIVES: Improvement of AWOSEM-region when analyzing proximal interfering hot objects is addressed by proper segmentation initialization steps and models of spill-out and partial volume effects. Conditions relevant to lung PET-CT studies are considered: 1) lesion close to hot organ (e.g. chest wall, heart and mediastinum), 2) two close lesions. METHODS: CT image was considered for pre-segmenting hot anatomical structures, never for lesion identification, solely defined by iterations on PET data. Further resolution recovery beyond the smooth standard clinical image was necessary to start lesion segmentation. A watershed algorithm was used to separate two close lesions. A subtraction of the spill-out from a nearby hot organ was introduced to enhance a lesion for the initial segmentation and start the further quantification steps. Biograph scanner blurring was modeled from phantom data in order to implement the procedure for 3D clinical lung studies. RESULTS: In simulations, the procedure was able to separate structures as close as one pixel-size (2.25 mm). Robustness against the input segmentation errors defining the addressed objects was tested showing that convergence was not sensitive to initial volume overestimates up to 130%. Poor robustness was found against underestimates. A clinical study of a small lung lesion close to chest wall displayed a good recovery of both lesion activity and volume. CONCLUSIONS: With proper initialization and models of spill-out from hot organs, AWOSEM-region can be successfully applied to lung oncological studies.

Applying 2D ML iterative reconstruction methods with resolution recovery to 3D PET data: evaluation of rebinning effects.

The applicability of OSEM reconstruction algorithms with space dependent resolution recovery to clinical FDG-PET studies is verified. The performance of the 2D algorithm is improved by means of a low resolution initialization and by a infra-iteration Metz filtering. Effects of different rebinning algorithms on 3D data are assessed, concluding that they do not alter the transaxial plane blurring parameters, thus permitting a straightforward application of 2D OSEM reconstruction after rebinning, with the same system matrix. Finally axial degradation was also quantified, finding that FORE is the best rebinning method to be combined with the 2D OSEM reconstruction.