Preliminary investigation of a Monte Carlo-based system matrix approach for quantitative clinical brain <sup>123</sup>I SPECT imaging

Self Cite

Objective. Monte-Carlo simulation studies have been essential for advancing various developments in SPECT imaging, such as system design and accurate image reconstruction. Among the simulation software available, GATE is one of the most used simulation toolkits in nuclear medicine, which allows building systems and attenuation phantom geometries based on the combination of idealized volumes. However, these idealized volumes are inadequate for modeling free-form shape components of such geometries. Recent GATE versions alleviate these major limitations by allowing users to import triangulated surface meshes. Approach. In this study, we describe our mesh-based simulations of a next-generation multi-pinhole SPECT system dedicated to clinical brain imaging, called AdaptiSPECT-C. To simulate realistic imaging data, we incorporated in our simulation the XCAT phantom, which provides an advanced anatomical description of the human body. An additional challenge with the AdaptiSPECT-C geometry is that the default voxelized XCAT attenuation phantom was not usable in our simulation due to intersection of objects of dissimilar materials caused by overlap of the air containing regions of the XCAT beyond the surface of the phantom and the components of the imaging system. Main Results. We validated our mesh-based modeling against the one constructed by idealized volumes for a simplified single vertex configuration of AdaptiSPECT-C through simulated projection data of 123I-activity distributions. We resolved the overlap conflict by creating and incorporating a mesh-based attenuation phantom following a volume hierarchy. We then evaluated our reconstructions with attenuation and scatter correction for projections obtained from simulation consisting of mesh-based modeling of the system and the attenuation phantom for brain imaging. Our approach demonstrated similar performance as the reference scheme simulated in air for uniform and clinical-like 123I-IMP brain perfusion source distributions. Significance. This work enables the simulation of complex SPECT acquisitions and reconstructions for emulating realistic imaging data close to those of actual patients.

Section: Introductionmentioning

confidence: 99%

Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction

Auer¹,

Könik²,

Fromme³

et al. 2023

Self Cite

“…This approach is thus heavily used in the design and development of nuclear imaging systems such as Positron Emission Tomography or single photon emission computed tomography (SPECT). For example, the design of new SPECT imaging devices (Auer et al 2018, Massari et al 2020, Brown 2021 or the development of reconstruction algorithms require realistic Monte Carlo simulations in various configurations. Such simulations create a mapping from a given activity source distribution inside a patient or a phantom to a signal captured by the imaging device outside of the patient/phantom by tracking particles one-by-one through the objects present in the simulation.…”

Section: Introductionmentioning

confidence: 99%

Modeling families of particle distributions with conditional GAN for Monte Carlo SPECT simulations

Saporta¹,

Etxebeste²,

Kaprelian³

et al. 2022

Objective. We propose a method to model families of distributions of particles exiting a phantom with a conditional Generative Adversarial Network (condGAN) during Monte Carlo simulation of SPECT imaging devices. Approach. The proposed condGAN is trained on a low statistics dataset containing the energy, the time, the position and the direction of exiting particles. In addition, it also contains a vector of conditions composed of four dimensions: the initial energy and the position of emitted particles within the phantom (a total of 12 dimensions). The information related to the gammas absorbed within the phantom is also added in the dataset. At the end of the training process, one component of the condGAN, the generator (G), is obtained. Main results. Particles with specific energies and positions of emission within the phantom can then be generated with G to replace the tracking of particle within the phantom, allowing reduced computation time compared to conventional Monte Carlo simulation. Significance. The condGAN generator is trained only once for a given phantom but can generate particles from various activity source distributions.

Section: Introductionmentioning

confidence: 99%

“…Monte Carlo simulations in medical physics are widely used in the design and development of imaging systems such as positron emission tomography (PET) or single photon emission computed tomography (SPECT), to monitor nuclear decay, fragmentation in the patient body or for range verification in particle therapy. For example, many works on emerging instrumentation for SPECT imaging systems (Auer et al 2018, Brown 2021, Massari et al 2020 require extensive and realistic Monte Carlo simulations to investigate and optimize the detection modules and novel geometrical configurations such as multi-head detectors. In abstract terms, such simulations create a mapping from a given source distribution inside the patient to a signal captured by the imaging device outside of the patient by transporting particles one-by-one through the objects present in the simulation.…”

Section: Introductionmentioning

confidence: 99%

Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

Sarrut¹,

Etxebeste²,

Krah³

et al. 2021

A method is proposed to model by a Generative Adversarial Network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for SPECT imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.