An overview of recent developments in FLUKA PET tools.

The new developments of the FLUKA Positron-Emission-Tomography (PET) tools are detailed. FLUKA is a fully integrated Monte Carlo (MC) particle transport code, used for an extended range of applications, including Medical Physics. Recently, it provided the medical community with dedicated simulation tools for clinical applications, including the PET simulation package. PET is a well-established imaging technique in nuclear medicine, and a promising method for clinical in vivo treatment verification in hadrontherapy. The application of clinically established PET scanners to new irradiation environments such as hadrontherapy requires further experimental and theoretical research to which MC simulations could be applied. The FLUKA PET tools, besides featuring PET scanner models in its library, allow the configuration of new PET prototypes via the FLUKA Graphical User Interface (GUI) Flair. Both the beam time structure and scan time can be specified by the user, reproducing PET acquisitions in time, in a particle therapy scenario. Furthermore, different scoring routines allow the analysis of single and coincident events, and identification of parent isotopes generating annihilation events. Two reconstruction codes are currently supported: the Filtered Back-Projection (FBP) and Maximum-Likelihood Expectation Maximization (MLEM), the latter embedded in the tools. Compatibility with other reconstruction frameworks is also possible. The FLUKA PET tools package has been successfully tested for different detectors and scenarios, including conventional functional PET applications and in beam PET, either using radioactive sources, or simulating hadron beam irradiations. The results obtained so far confirm the FLUKA PET tools suitability to perform PET simulations in R&D environment.

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.

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.