The aim of this study was to validate quantitative performance of a newly released simultaneous positron emission tomography (PET)/magnetic resonance imaging (MRI) scanner, by using MR-based attenuation correction (MRAC), both in phantom study and in patient study. PET/MRI image uniformities of a phantom under different hardware configurations were tested and compared. Thirty patients were examined with 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) PET/computed tomography (CT) and subsequent PET/MRI. PET images from PET/MRI were corrected with MRAC (PETMR), CT-based attenuation maps (μ-maps, PETCT), and segmented CT μ-maps (PETCTSeg) derived from PET/CT. Standardized uptake values (SUVs) were compared among the 3 sets of PET in main organs (bone, liver and lung) and in 52 FDG-avid lesions, including soft-tissue lesions and bone lesions. The result showed that PET imaging uniformities of PET/MRI under different configurations were good (<8.8%). The SUV differences among the 3 sets of PET varied with organs and lesion types. In detail, the mean relative differences of SUV between PETMR and PETCT were as follows: −18.8%, bone (SUVmean); −8.0%, liver (SUVmean); −12.2%, lung (SUVmean); −18.1%, bone lesions (SUVmean); −13.3%, bone lesions (SUVmax); −8.2%, soft-tissue lesions (SUVmean); and −7.3%, soft-tissue lesions (SUVmax). The mean relative differences between PETMR and PETCTSeg were as follows: −19.0%, bone (SUVmean); −3.5%, liver (SUVmean); −3.3%, lung (SUVmean); −19.3%, bone lesions (SUVmean); −17.5%, bone lesions (SUVmax); −5.5%, soft-tissue lesions (SUVmean); and −4.4%, soft-tissue lesions (SUVmax). The differences of SUV between PETMR and PETCT were larger than those between PETMR and PETCTSeg, in both soft tissue and soft-tissue lesions (P < 0.001), but not in bone or bone lesions. In conclusion, MRAC in the newly released PET/MR system is accurate in most tissues, with SUV deviations being generally less than 10%, compared to PET/CT. In bone, however, underestimations can be substantial, which may be partially attributed to segmentation of the MR-based μ-maps.

Subject head motion is a major challenge in DWI, leading to image blurring, signal losses, and biases in the estimated diffusion parameters. Here, we investigate a combined application of prospective motion correction and spatial-angular locally low-rank constrained reconstruction to obtain robust, multi-shot, high-resolution diffusion-weighted MRI under substantial motion.Methods: Single-shot EPI with retrospective motion correction can mitigate motion artifacts and resolve any mismatching of gradient encoding orientations; however, it is limited by low spatial resolution and image distortions. Multi-shot acquisition strategies could achieve higher resolution and image fidelity but increase the vulnerability to motion artifacts and phase variations related to cardiac pulsations from shot to shot. We use prospective motion correction with optical markerless motion tracking to remove artifacts and reduce image blurring due to bulk motion, combined with locally low-rank regularization to correct for remaining artifacts due to shot-to-shot phase variations. Results:The approach was evaluated on healthy adult volunteers at 3 Tesla under different motion patterns. In multi-shot DWI, image blurring due to motion with 20 mm translations and 30 • rotations was successfully removed by prospective motion correction, and aliasing artifacts caused by shot-to-shot phase variations were addressed by locally low-rank regularization. The ability of prospective motion correction to preserve the orientational information in DTI without requiring a reorientation of the b-matrix is highlighted. Conclusion:The described technique is proved to hold valuable potential for mapping brain diffusivity and connectivity at high resolution for studies in subjects/cohorts where motion is common, including neonates, pediatrics, and patients with neurological disorders.

Background Integrated whole-body PET/MR technology continues to mature and is now extensively used in clinical settings. However, due to the special design architecture, integrated whole-body PET/MR comes with a few inherent limitations. Firstly, whole-body PET/MR lacks sensitivity and resolution for focused organs. Secondly, broader clinical access of integrated PET/MR has been significantly restricted due to its prohibitively high cost. The MR-compatible PET insert is an independent and removable PET scanner which can be placed within an MRI bore. However, the mobility and configurability of all existing MR-compatible PET insert prototypes remain limited. Methods An MR-compatible portable PET insert prototype, dual-panel portable PET (DP-PET), has been developed for simultaneous PET/MR imaging. Using SiPM, digital readout electronics, novel carbon fiber shielding, phase-change cooling, and MRI compatible battery power, DP-PET was designed to achieve high-sensitivity and high-resolution with compatibility with a clinical 3-T MRI scanner. A GPU-based reconstruction method with resolution modeling (RM) has been developed for the DP-PET reconstruction. We evaluated the system performance on PET resolution, sensitivity, image quality, and the PET/MR interference. Results The initial results reveal that the DP-PET prototype worked as expected in the MRI bore and caused minimal compromise to the MRI image quality. The PET performance was measured to show a spatial resolution ≤ 2.5 mm (parallel to the detector panels), maximum sensitivity = 3.6% at the center of FOV, and energy resolution = 12.43%. MR pulsing introduces less than 2% variation to the PET performance measurement results. Conclusions We developed a MR-compatible PET insert prototype and performed several studies to begin to characterize the performance of the proposed DP-PET. The results showed that the proposed DP-PET performed well in the MRI bore and would cause little influence on the MRI images. The Derenzo phantom test showed that the proposed reconstruction method could obtain high-quality images using DP-PET.

It is a routine procedure to use Iterative Closest Point (ICP) algorithm to correct motion in medical imaging of the head. However, the basic assumption of ICP is the rigid body motion, and it is not strictly valid when there is non-rigid motion (facial expression). In this study, we propose a novel method to reduce the adverse effects of facial expressions on head motion estimation. First, we design a network named DFG-Net to generate a deformation field on the RGB domain and compute a confidence map. We then extend the original ICP by embedding the confidence map representing each point's degree of non-rigid motion. Compared to other techniques, our proposed method eliminates the negative effects of facial expression while making the most of pose information. The experiments demonstrate that our approach reduces the position error by 19.23% and orientation error by 36.37% compared to the original ICP algorithm. They also show that our method is robust to variations of expressions.INDEX TERMS Non-rigid motion, ICP, deep learning, head motion correction.