Feasibility study of a point‐of‐care positron emission tomography system with interactive imaging capability

Abstract

PURPOSE\nWe investigated the feasibility of a novel positron emission tomography (PET) system that provides near real-time feedback to an operator who can interactively scan a patient to optimize image quality. The system should be compact and mobile to support point-of-care (POC) molecular imaging applications. In this study, we present the key technologies required and discuss the potential benefits of such new capability.\n\n\nMETHODS\nThe core of this novel PET technology includes trackable PET detectors and a fully three-dimensional, fast image reconstruction engine implemented on multiple graphics processing units (GPUs) to support dynamically changing geometry by calculating the system matrix on-the-fly using a tube-of-response approach. With near real-time image reconstruction capability, a POC-PET system may comprise a maneuverable front PET detector and a second detector panel which can be stationary or moved synchronously with the front detector such that both panels face the region-of-interest (ROI) with the detector trajectory contoured around a patient s body. We built a proof-of-concept\xa0prototype using two planar detectors each consisting of a photomultiplier tube (PMT) optically coupled to an array of 48\xa0×\xa048 lutetium-yttrium oxyorthosilicate (LYSO) crystals (1.0\xa0×\xa01.0\xa0×\xa010.0\xa0mm3 each). Only 38\xa0×\xa038 crystals in each arrays can be clearly re-solved and used for coincidence detection. One detector was mounted to a robotic arm which can position it at arbitrary locations, and the other detector was mounted on a rotational stage. A cylindrical phantom (102\xa0mm in diameter, 150\xa0mm long) with nine spherical lesions (8:1 tumor-to-background activity concentration ratio) was imaged from 27 sampling angles. List-mode events were reconstructed to form images without or with time-of-flight (TOF) information. We conducted two Monte Carlo simulations using two POC-PET systems. The first one uses the same phantom and detector setup as our experiment, with the detector coincidence re-solving time (CRT) ranging from 100 to 700\xa0ps full-width-at-half-maximum (FWHM). The second study simulates a body-size phantom (316\xa0×\xa0228\xa0×\xa0160\xa0mm3 ) imaged by a larger POC-PET system that has 4\xa0×\xa06 modules (32\xa0×\xa032 LYSO crystals/module, four in axial and six in transaxial directions) in the front panel and 3\xa0×\xa08 modules (16\xa0×\xa016 LYSO crystals/module, three in axial and eight in transaxial directions) in the back panel. We also evaluated an interactive scanning strategy by progressively increasing the number of data sets used for image reconstruction. The updated images were analyzed based on the number of data sets and the detector CRT.\n\n\nRESULTS\nThe proof-of-concept prototype re-solves most of the spherical lesions despite a limited number of coincidence events and incomplete sampling. TOF information reduces artifacts in the reconstructed images. Systems with better timing resolution exhibit improved image quality and reduced artifacts. We observed a reconstruction speed of 0.96\xa0×\xa0106 events/s/iteration for 600\xa0×\xa0600\xa0×\xa0224 voxel rectilinear space using four GPUs. A POC-PET system with significantly higher sensitivity can interactively image a body-size object from four angles in less than 7\xa0min.\n\n\nCONCLUSIONS\nWe have developed GPU-based fast image reconstruction capability to support a PET system with arbitrary and dynamically changing geometry. Using TOF PET detectors, we demonstrated the feasibility of a PET system that can provide timely visual feedback to an operator who can scan a patient interactively to support POC imaging applications.