A recent entry into the rapidly evolving field of integrated PET/MR scanners is presented in this paper: a whole body hybrid PET/MR system (SIGNA PET/MR, GE Healthcare) capable of simultaneous acquisition of both time-of-flight (TOF) PET and high resolution MR data. The PET ring was integrated into an existing 3T MR system resulting in a (patient) bore opening of 60 cm diameter, with a 25 cm axial FOV. PET performance was evaluated both on the standalone PET ring and on the same detector integrated into the MR system, to assess the level of mutual interference between both subsystems. In both configurations we obtained detector performance data. PET detector performance was not significantly affected by integration into the MR system. The global energy resolution was within 2% (10.3% versus 10.5%), and the system coincidence time resolution showed a maximum change of < 3% (385 ps versus 394 ps) when measured outside MR and during simultaneous PET/MRI acquisitions, respectively. To evaluate PET image quality and resolution, the NEMA IQ phantom was acquired with MR idle and with MR active. Impact of PET on MR IQ was assessed by comparing SNR with PET acquisition on and off. B0 and B1 homogeneities were acquired before and after the integration of the PET ring inside the magnet. In vivo brain and whole body head-to-thighs data were acquired to demonstrate clinical image quality.

Accurate and robust attenuation correction remains challenging in hybrid PET/MR particularly for torsos because it is difficult to segment bones, lungs and internal air in MR images. Additionally, MR suffers from susceptibility artifacts when a metallic implant is present. Recently, joint estimation (JE) of activity and attenuation based on PET data, also known as maximum likelihood reconstruction of activity and attenuation, has gained considerable interest because of (1) its promise to address the challenges in MR-based attenuation correction (MRAC), and (2) recent advances in time-of-flight (TOF) technology, which is known to be the key to the success of JE. In this paper, we implement a JE algorithm using an MR-based prior and evaluate the algorithm using whole-body PET/MR patient data, for both FDG and non-FDG tracers, acquired from GE SIGNA PET/MR scanners with TOF capability. The weight of the MR-based prior is spatially modulated, based on MR signal strength, to control the balance between MRAC and JE. Large prior weights are used in strong MR signal regions such as soft tissue and fat (i.e. MR tissue classification with a high degree of certainty) and small weights are used in low MR signal regions (i.e. MR tissue classification with a low degree of certainty). The MR-based prior is pragmatic in the sense that it is convex and does not require training or population statistics while exploiting synergies between MRAC and JE. We demonstrate the JE algorithm has the potential to improve the robustness and accuracy of MRAC by recovering the attenuation of metallic implants, internal air and some bones and by better delineating lung boundaries, not only for FDG but also for more specific non-FDG tracers such as Ga-DOTATOC andF-Fluoride.

Purpose To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discovery PET/CT scanners, were split into ¾-, ½- and ¼-duration scans. Full-duration datasets were reconstructed using the convergent block sequential regularised expectation maximisation (BSREM) algorithm. Short-duration datasets were reconstructed with the faster OSEM algorithm. The 277 examinations were divided into training (n = 237), validation (n = 15) and testing (n = 25) sets. Three deep learning enhancement (DLE) models were trained to map full and partial-duration OSEM images into their target full-duration BSREM images. In addition to standardised uptake value (SUV) evaluations in lesions, liver and lungs, two experienced radiologists scored the quality of testing set images and BSREM in a blinded clinical reading (175 series). Results OSEM reconstructions demonstrated up to 22% difference in lesion SUVmax, for different scan durations, compared to full-duration BSREM. Application of the DLE models reduced this difference significantly for full-, ¾- and ½-duration scans, while simultaneously reducing the noise in the liver. The clinical reading showed that the standard DLE model with full- or ¾-duration scans provided an image quality substantially comparable to full-duration scans with BSREM reconstruction, yet in a shorter reconstruction time. Conclusion Deep learning–based image enhancement models may allow a reduction in scan time (or injected activity) by up to 50%, and can decrease reconstruction time to a third, while maintaining image quality.

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