Determination of lutetium density in LYSO crystals: methodology and PET detector applications

Objective. Lutetium yttrium oxyorthosilicate (LYSO) scintillation crystals are used in positron emission tomography (PET) due to their high gamma attenuation, fair energy resolution, and fast scintillation decay time. The enduring presence of the176Lu isotope, characterized by a half-life of 37.9 billion years, imparts a consistent radiation background (BG) profile that depends on the geometry and composition attributes of the LYSO crystals.Approach. In this work, we proposed a methodology for estimating the composition of LYSO crystals in cases where the exact Lutetium composition remains unknown. The connection between BG spectrum intensity and intrinsic radioactivity enables precise estimation of Lutetium density in LYSO crystal samples. This methodology was initially applied to a well-characterized LYSO crystal sample, yielding results closely aligned with the known composition. The composition estimation approach was extended to several samples of undisclosed LYSO crystals, encompassing single crystal and crystal array configurations. Furthermore, we model the background spectrum observed in the LYSO-based detector and validate the observed spectra via simulations.Main results. The estimated Lutetium composition exhibited adequate consistency across different samples of the same LYSO material, with variations of less than 1%. The result of the proposed approach coupled with the simulation successfully models the background radiation spectra in various LYSO-based detector geometries.Significance. The implications of this work extend to the predictive assessment of system behaviors and the autonomous configuration parameters governing LYSO-based detectors.

Hardware Architectures for Real-Time Medical Imaging

Medical imaging is considered one of the most important advances in the history of medicine and has become an essential part of the diagnosis and treatment of patients. Earlier prediction and treatment have been driving the acquisition of higher image resolutions as well as the fusion of different modalities, raising the need for sophisticated hardware and software systems for medical image registration, storage, analysis, and processing. In this scenario and given the new clinical pipelines and the huge clinical burden of hospitals, these systems are often required to provide both highly accurate and real-time processing of large amounts of imaging data. Additionally, lowering the prices of each part of imaging equipment, as well as its development and implementation, and increasing their lifespan is crucial to minimize the cost and lead to more accessible healthcare. This paper focuses on the evolution and the application of different hardware architectures (namely, CPU, GPU, DSP, FPGA, and ASIC) in medical imaging through various specific examples and discussing different options depending on the specific application. The main purpose is to provide a general introduction to hardware acceleration techniques for medical imaging researchers and developers who need to accelerate their implementations.