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

Abstract

Purpose: We 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. Methods: The core of this novel PET technology includes trackable PET detectors and a fully threedimensional, 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 prototype using two planar detectors each consisting of a photomultiplier tube (PMT) optically coupled to an array of 48 9 48 lutetium-yttrium oxyorthosilicate (LYSO) crystals (1.0 9 1.0 9 10.0 mm 3 each). Only 38 9 38 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 mm in diameter, 150 mm 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 ps full-width-at-half-maximum (FWHM). The second study simulates a body-size phantom (316 9 228 9 160 mm 3 ) imaged by a larger POC-PET system that has 4 9 6 modules (32 9 32 LYSO crystals/module, four in axial and six in transaxial directions) in the front panel and 3 9 8 modules (16 9 16 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. Results: The 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 9 10 6 events/s/iteration for 600 9 600 9 224 voxel rect...

A second‐generation virtual‐pinhole PET device for enhancing contrast recovery and improving lesion detectability of a whole‐body PET/CT scanner

“…As a result, a precalculated system matrix may no longer be possible for this second‐generation VP‐PET system. We developed a new list‐mode image reconstruction framework based on GPU to calculate the system matrix on‐the‐fly and to speed‐up the forward and backward projections …”

“…We developed a new list-mode image reconstruction framework based on GPU [43][44][45][46] to calculate the system matrix on-the-fly and to speed-up the forward and backward projections. 39,40 This reconstruction software runs on multiple GPUs to perform forward and back projections between individual tube-of-responses and image voxels. 44 Attenuation correction is based on forward projection of a composite attenuation map using CT images of the patient and the known geometry of the panel detectors.…”

“…A commercial robotic arm was used to provide flexible and reproducible positioning of the flat‐panel VP‐PET detectors to conform to a patient's body and to target selected region‐of‐interest. A list mode Maximum‐Likelihood Estimation‐Maximization (MLEM) image reconstruction software running on multiple graphics processing units (GPUs) was developed to support PET scanners with arbitrary geometry. This study describes the design of the second‐generation VP‐PET device, validates the functionality of the system and evaluates its potentials in the improvement of contrast recovery coefficient (CRC) of small lesions through a torso phantom imaging study.…”

Augmented Whole-Body Scanning via Magnifying PET

“…With a roughly 10 mm thick device close to the patient, random correction and scatter correction techniques are critical in order to produce fully quantitative PET images using such a system. These correction techniques will be implemented for our GPU-based list-mode image reconstruction framework [36].…”

“…The two bed positions, both in simulation and experimental studies, were chosen to ensure that the spherical tumors were in the FOV of the scanner at the first bed position and go outside of the FOV of the scanner but in the augmented imaging zone(s) at the second bed position. All images were reconstructed using custom list-mode image reconstruction software [36] that computes the system matrix on-the-fly using 4 GPUs [37,38]. Attenuation correction is estimated by forward projection using a CT image derived attenuation map.…”