OMEGA-open-source emission tomography software

Anatomy-guided multi-resolution image reconstruction in PET

Objective. In this paper, we propose positron emission tomography image reconstruction using a multi-resolution triangular mesh. The mesh can be adapted based on patient specific anatomical information that can be in the form of a computed tomography or magnetic resonance imaging image in the hybrid imaging systems. The triangular mesh can be adapted to high resolution in localized anatomical regions of interest (ROI) and made coarser in other regions, leading to an imaging model with high resolution in the ROI with clearly reduced number of degrees of freedom compared to a conventional uniformly dense imaging model.Approach.We compare maximum likelihood expectation maximization reconstructions with the multi-resolution model to reconstructions using a uniformly dense mesh, a sparse mesh and regular rectangular pixel mesh. Two simulated cases are used in the comparison, with the first one using the NEMA image quality phantom and the second the XCAT human phantom.Main results.When compared to the results with the uniform imaging models, the locally refined multi-resolution mesh retains the accuracy of the dense mesh reconstruction in the ROI while being faster to compute than the reconstructions with the uniformly dense mesh. The locally dense multi-resolution model leads also to more accurate reconstruction than the pixel-based mesh or the sparse triangular mesh.Significance.The findings suggest that triangular multi-resolution mesh, which can be made patient and application specific, is a potential alternative for pixel-based reconstruction.

Breaking Barriers in Cranioplasty: 3D Printing in Low and Middle-Income Settings-Insights from Zenica, Bosnia and Herzegovina

Background and Objectives: Cranial defects pose significant challenges in low and middle-income countries (LIMCs), necessitating innovative and cost-effective craniofacial reconstruction strategies. The purpose of this study was to present the Bosnia and Herzegovina model, showcasing the potential of a multidisciplinary team and 3D-based technologies, particularly PMMA implants, to address cranial defects in a resource-limited setting. Materials and Methods: An observational, non-experimental prospective investigation involved three cases of cranioplasty at the Department of Neurosurgery, Cantonal Hospital Zenica, Bosnia and Herzegovina, between 2019 and 2023. The technical process included 3D imaging and modeling with MIMICS software (version 10.01), 3D printing of the prototype, mold construction and intraoperative modification for precise implant fitting. Results: The Bosnia and Herzegovina model demonstrated successful outcomes in cranioplasty, with PMMA implants proving cost-effective and efficient in addressing cranial defects. Intraoperative modification contributed to reduced costs and potential complications, while the multidisciplinary approach and 3D-based technologies facilitated accurate reconstruction. Conclusions: The Bosnia and Herzegovina model showcases a cost-effective and efficient approach for craniofacial reconstruction in LIMICs. Collaborative efforts, 3D-based technologies, and PMMA implants contribute to successful outcomes. Further research is needed to validate sustained benefits and enhance craniofacial reconstruction strategies in resource-constrained settings.

TOPAS-imaging: extensions to the TOPAS simulation toolkit for medical imaging systems

Objective. The TOol for PArticle Simulation (TOPAS) is a Geant4-based Monte Carlo software application that has been used for both research and clinical studies in medical physics. So far, most users of TOPAS have focused on radiotherapy-related studies, such as modeling radiation therapy delivery systems or patient dose calculation. Here, we present the first set of TOPAS extensions to make it easier for TOPAS users to model medical imaging systems.Approach. We used the extension system of TOPAS to implement pre-built, user-configurable geometry components such as detectors (e.g. flat-panel and multi-planar detectors) for various imaging modalities and pre-built, user-configurable scorers for medical imaging systems (e.g. digitizer chain).Main results. We developed a flexible set of extensions that can be adapted to solve research questions for a variety of imaging modalities. We then utilized these extensions to model specific examples of cone-beam CT (CBCT), positron emission tomography (PET), and prompt gamma (PG) systems. The first of these new geometry components, the FlatImager, was used to model example CBCT and PG systems. Detected signals were accumulated in each detector pixel to obtain the intensity of x-rays penetrating objects or prompt gammas from proton-nuclear interaction. The second of these new geometry components, the RingImager, was used to model an example PET system. Positron-electron annihilation signals were recorded in crystals of the RingImager and coincidences were detected. The simulated data were processed using corresponding post-processing algorithms for each modality and obtained results in good agreement with the expected true signals or experimental measurement.Significance. The newly developed extension is a first step to making it easier for TOPAS users to build and simulate medical imaging systems. Together with existing TOPAS tools, this extension can help integrate medical imaging systems with radiotherapy simulations for image-guided radiotherapy.

ProTheRaMon - a GATE simulation framework for proton therapy range monitoring using PET imaging

OBJECTIVE

This paper reports on the implementation and shows examples of the use of the ProTheRaMon framework for simulating the delivery of proton therapy treatment plans and range monitoring using positron emission tomography (PET). ProTheRaMon offers complete processing of proton therapy treatment plans, patient CT geometries, and intra-treatment PET imaging, taking into account therapy and imaging coordinate systems and activity decay during the PET imaging protocol specific to a given proton therapy facility. We present the ProTheRaMon framework and illustrate its potential use case and data processing steps for a patient treated at the Cyclotron Centre Bronowice (CCB) proton therapy center in Krakow, Poland.

APPROACH

The ProTheRaMon framework is based on GATE Monte Carlo software, the CASToR reconstruction package and in-house developed Python and bash scripts. The framework consists of five separated simulation and data processing steps, that can be further optimized according to the user's needs and specific settings of a given proton therapy facility and PET scanner design.

MAIN RESULTS

ProTheRaMon is presented using example data from a patient treated at CCB and the J-PET scanner to demonstrate the application of the framework for proton therapy range monitoring. The output of each simulation and data processing stage is described and visualized.

SIGNIFICANCE

We demonstrate that the ProTheRaMon simulation platform is a high-performance tool, capable of running on a computational cluster and suitable for multi-parameter studies, with databases consisting of large number of patients, as well as different PET scanner geometries and settings for range monitoring in a clinical environment. Due to its modular structure, the ProTheRaMon framework can be adjusted for different proton therapy centers and/or different PET detector geometries. It is available to the community via github.

Open and reproducible neuroimaging: From study inception to publication

Empirical observations of how labs conduct research indicate that the adoption rate of open practices for transparent, reproducible, and collaborative science remains in its infancy. This is at odds with the overwhelming evidence for the necessity of these practices and their benefits for individual researchers, scientific progress, and society in general. To date, information required for implementing open science practices throughout the different steps of a research project is scattered among many different sources. Even experienced researchers in the topic find it hard to navigate the ecosystem of tools and to make sustainable choices. Here, we provide an integrated overview of community-developed resources that can support collaborative, open, reproducible, replicable, robust and generalizable neuroimaging throughout the entire research cycle from inception to publication and across different neuroimaging modalities. We review tools and practices supporting study inception and planning, data acquisition, research data management, data processing and analysis, and research dissemination. An online version of this resource can be found at https://oreoni.github.io. We believe it will prove helpful for researchers and institutions to make a successful and sustainable move towards open and reproducible science and to eventually take an active role in its future development.