PETcoil: first results from a second-generation RF-penetrable TOF-PET brain insert for simultaneous PET/MRI

Simultaneous positron emission tomography (PET)/magnetic resonance imaging provides concurrent information about anatomic, functional, and molecular changes in disease. We are developing a second generation MR-compatible RF-penetrable TOF-PET insert. The insert has a smaller scintillation crystal size and ring diameter compared to clinical whole-body PET scanners, resulting in higher spatial resolution and sensitivity. This paper reports the initial system performance of this full-ring PET insert. The global photopeak energy resolution and global coincidence time resolution, 11.74 ± 0.03 % FWHM and 238.1 ± 0.5 ps FWHM, respectively, are preserved as we scaled up the system to a full ring comprising 12, 288 LYSO-SiPM channels (crystal size: 3.2 × 3.2 × 20 mm3). Throughout a ten-hour experiment, the system performance remained stable, exhibiting a less than 1% change in all measured parameters. In a resolution phantom study, the system successfully resolved all 2.8 mm diameter rods, achieving an average VPR of 0.28 ± 0.08 without TOF and 0.24 ± 0.07 with TOF applied. Moreover, the implementation of TOF in the Hoffman phantom study also enhanced image quality. Initial MR compatibility studies of the full PET ring were performed with it unpowered as a milestone to focus on looking for material and geometry-related artifacts. During all MR studies, the MR body coil functioned as both the transmit and receive coil, and no observable artifacts were detected. As expected, using the body coil also as the RF receiver, MR image signal-to-noise ratio exhibited degradation (∼30%), so we are developing a high quality receive-only coil that resides inside the PET ring.

A Novel J-Shape Antenna Array for Simultaneous MR-PET or MR-SPECT Imaging

Simultaneous MR-PET/-SPECT is an emerging technology that capitalises on the invaluable advantages of both modalities, allowing access to numerous sensitive tracers and superior soft-tissue contrast alongside versatile functional imaging capabilities. However, to optimise these capabilities, concurrent acquisitions require the MRI antenna located inside the PET/SPECT field-of-view to be operated without compromising any aspects of system performance or image quality compared to the stand-alone instrumentation. Here, we report a novel gamma-radiation-transparent antenna concept. The end-fed J-shape antenna is particularly adept for hybrid ultra-high field MR-PET/-SPECT applications as it enables all highly attenuating materials to be placed outside the imaging field-of-view. Furthermore, this unique configuration also provides advantages in stand-alone MR applications by reducing the amount of coupling between the cables and the antenna elements, and by lowering the potential specific absorption rate burden. The use of this new design was experimentally verified according to the important features for both ultra-high field MRI and the 511 keV transmission scan. The reconstructed attenuation maps evidently showed much lower attenuation (  ∼ 15 %) for the proposed array when compared to the conventional dipole antenna array since there were no high-density components. In MR, it was observed that the signal-to-noise ratio from the whole volume obtained using the proposed array was comparable to that acquired by the conventional array which was also in agreement with the simulation results. The unique feature, J-shape array, would enable simultaneous MR-PET/-SPECT experiments to be conducted without unduly compromising any aspects of system performance and image quality compared to the stand-alone instrumentation.

PET/MR Part 4: Clinical Applications of PET/MRI

Position emission tomography (PET) and magnetic resonance imaging (MRI) as a hybrid modality provides novel imaging opportunities. While there are a very broad array of pathologies that could benefit from PET/MRI, there is only a narrow range of applications where benefit over standard care justifies the higher resource utilization and, in particular, offers a net positive trade-off over PET/CT. This benefit is generally associated with the omission of CT and the associated radiation dose from the patient workup. This manuscript provides a summary of the generally accepted clinical applications of PET/MRI in both adult and pediatric populations. While there are a number of potential applications and certainly exciting research that may expand applications in the future, the purpose of this paper was to focus on current, mainstream applications. This is the final manuscript in a four-part integrated series sponsored by the SNMMI-TS PET/MR Task Force in conjunction with the SNMMI-TS Publication Committee.

Imitation learning for improved 3D PET/MR attenuation correction

The assessment of the quality of synthesised/pseudo Computed Tomography (pCT) images is commonly measured by an intensity-wise similarity between the ground truth CT and the pCT. However, when using the pCT as an attenuation map (μ-map) for PET reconstruction in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI) minimising the error between pCT and CT neglects the main objective of predicting a pCT that when used as μ-map reconstructs a pseudo PET (pPET) which is as similar as possible to the gold standard CT-derived PET reconstruction. This observation motivated us to propose a novel multi-hypothesis deep learning framework explicitly aimed at PET reconstruction application. A convolutional neural network (CNN) synthesises pCTs by minimising a combination of the pixel-wise error between pCT and CT and a novel metric-loss that itself is defined by a CNN and aims to minimise consequent PET residuals. Training is performed on a database of twenty 3D MR/CT/PET brain image pairs. Quantitative results on a fully independent dataset of twenty-three 3D MR/CT/PET image pairs show that the network is able to synthesise more accurate pCTs. The Mean Absolute Error on the pCT (110.98 HU ± 19.22 HU) compared to a baseline CNN (172.12 HU ± 19.61 HU) and a multi-atlas propagation approach (153.40 HU ± 18.68 HU), and subsequently lead to a significant improvement in the PET reconstruction error (4.74% ± 1.52% compared to baseline 13.72% ± 2.48% and multi-atlas propagation 6.68% ± 2.06%).

Image-domain Material Decomposition for Spectral CT using a Generalized Dictionary Learning

The spectral computed tomography (CT) has huge advantages by providing accurate material information. Unfortunately, due to the instability or overdetermination of material decomposition model, the accuracy of material decomposition can be compromised in practice. Very recently, the dictionary learning based image-domain material decomposition (DLIMD) can obtain high accuracy for material decompositions from reconstructed spectral CT images. This method can explore the correlation of material components to some extent by training a unified dictionary from all material images. In addition, the dictionary learning based prior as a penalty is applied on material components independently, and many parameters would be carefully elaborated in practice. Because the concentration of contrast agent in clinical applications is low, it can result in data inconsistency for dictionary based representation during the iteration process. To avoid the aforementioned limitations and further improve the accuracy of materials, we first construct a generalized dictionary learning based image-domain material decomposition (GDLIMD) model. Then, the material tensor image is unfolded along the mode-1 to enhance the correlation of different materials. Finally, to avoid the data inconsistency of low iodine contrast, a normalization strategy is employed. Both physical phantom and tissue-synthetic phantom experiments demonstrate the proposed GDLIMD method outperforms the DLIMD and direct inversion (DI) methods.

Dictionary learning based image-domain material decomposition for spectral CT

The potential huge advantage of spectral computed tomography (CT) is that it can provide accurate material identification and quantitative tissue information by material decomposition. However, material decomposition is a typical inverse problem, where the noise can be magnified. To address this issue, we develop a dictionary learning based image-domain material decomposition (DLIMD) method for spectral CT to achieve accurate material components with better image quality. Specifically, a set of image patches are extracted from the mode-1 unfolding of normalized material images decomposed by direct inversion to train a unified dictionary using the K-SVD technique. Then, the DLIMD model is established to explore the redundant similarities of the material images, where the split-Bregman is employed to optimize the model. Finally, more constraints (i.e. volume conservation and the bounds of each pixel within material maps) are integrated into the DLIMD model. Numerical phantom, physical phantom and preclinical experiments are performed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.