Speaker
Description
Introduction The Human Dynamic NeuroChemical Connectome (HDNCC) scanner is a novel, brain-dedicated imaging system that integrates the high spatio-temporal resolution NeuroSphere PET insert with a Siemens MAGNETOM Terra.X 7T MRI scanner. While PET-MR technology offers exceptional molecular sensitivity, existing systems often lack the temporal resolution required to capture dynamic changes on time scales comparable to cognitive processes—a capability typically reserved for functional MRI. To address this, the NeuroSphere leverages a unique spherical geometry and state-of-the-art detectors to significantly boost sensitivity and spatial resolution. This work presents a comprehensive simulation of the NeuroSphere’s performance following NEMA protocols, demonstrating its potential to achieve functional time frames on the order of seconds.
Materials and Methods The NeuroSphere is composed of 872 detector modules arranged in a spherical configuration with a 32 cm inner diameter. This design provides a solid angle coverage of approximately 70% around the human head, which is critical for enhancing signal-to-noise ratio in dynamic studies. Each detector block consists of a 10x10 array of 1.6x1.6x26 mm3 LSO crystals coupled to a 4x4 matrix of MPPCs (4x4 mm2). Light sharing for Depth-of-Interaction (DOI) identification is enabled by a 380 μm methacrylate light guide applied to the entry face of each block, achieving a DOI resolution of 7 mm.
System performance was evaluated using the GPU-based UMC-PET Monte Carlo simulator, which offers high-fidelity voxelized modeling and operates up to 2,000 times faster than CPU-based alternatives. Detector coordinates were imported directly from the CAD model to create a realistic digital twin, including realistic detector modeling based on actual characterization measurements indicating an energy resolution below 13% and a coincidence timing resolution of 526 ps. Simulations followed NEMA NU-2-2018 standards to estimate spatial resolution, count rates, and sensitivity. Sensitivity was further optimized by exploiting triple coincidences from inter-detector scatter events. Image reconstruction was performed using a 3D-OSEM algorithm incorporating spatially variant point-spread-function (SV-PSF) modeling to ensure high resolution across the entire field of view (FOV).
Results Simulations demonstrate an outstanding average sensitivity over 30% for a 435-585 keV energy window across the brain. The system achieves a homogeneous spatial resolution of better than 1.6 mm; reconstructed images of a Derenzo phantom confirmed that 1.6 mm rods are clearly identifiable at both centered and 80 mm off-center positions.
Regarding count rate performance, the peak Noise Equivalent Count (NEC) rate was obtained over 20 kBq/mL (440 MBq in a 200 mm diameter and 700 mm length phantom). For typical brain activity levels, the system maintains high trues-to-prompts ratios, with approximately 500 kcps trues 100 MBq total activity (<5 kBq/mL).
Conclusion The NeuroSphere PET insert shows exceptional potential for high-speed dynamic brain imaging. Its high sensitivity and sub-1.5 mm spatial resolution offer the capability to push PET temporal resolution toward levels comparable to fMRI. The first complete assembly of the NeuroSphere is currently undergoing experimental calibration, which will further refine these performance estimates for future clinical and research applications.
| Track | PSMR |
|---|---|
| Presentation type | Oral |