Performance evaluation of SimPET-L and SimPET-XL: MRI-compatible small-animal PET systems with rat-body imaging capability.

BACKGROUND: SimPET-L and SimPET-XL have recently been introduced with increased transaxial fields of view (FOV) compared with their predecessors (SimPET™ and SimPET-X), enabling whole-body positron emission tomography (PET) imaging of rats. We conducted performance evaluations of SimPET-L and SimPET-XL and rat-body imaging with SimPET-XL to demonstrate the benefits of increased axial and transaxial FOVs. PROCEDURES: The detector blocks in SimPET-L and SimPET-XL consist of two 4 × 4 silicon photomultiplier arrays coupled with 20 × 9 array lutetium oxyorthosilicate crystals. SimPET-L and SimPET-XL have an inner diameter (bore size) of 7.6 cm, and they are composed of 40 and 80 detector blocks yielding axial lengths of 5.5 and 11 cm, respectively. Each system was evaluated according to the National Electrical Manufacturers Association NU4-2008 protocol. Rat imaging studies, such as 18F-NaF and 18F-FDG PET, were performed using SimPET-XL. RESULTS: The radial resolutions at the axial center measured using the filtered back projection, 3D ordered-subset expectation maximization (OSEM), and 3D OSEM with point spread functions correction were 1.7, 0.82, and 0.82 mm FWHM in SimPET-L and 1.7, 0.91, and 0.91 mm FWHM in SimPET-XL, respectively. The peak sensitivities of SimPET-L and SimPET-XL were 6.30% and 10.4% for an energy window of 100-900 keV and 4.44% and 7.25% for a window of 250-750 keV, respectively. The peak noise equivalent count rate with an energy window of 250-750 keV was 249 kcps at 44.9 MBq for SimPET-L and 349 kcps at 31.3 MBq for SimPET-XL. In SimPET-L, the uniformity was 4.43%, and the spill-over ratios in air- and water-filled chambers were 5.54% and 4.10%, respectively. In SimPET-XL, the uniformity was 3.89%, and the spill-over ratio in the air- and water-filled chambers were 3.56% and 3.60%. Moreover, SimPET-XL provided high-quality images of rats. CONCLUSION: SimPET-L and SimPET-XL show adequate performance compared with other SimPET systems. In addition, their large transaxial and long axial FOVs provide imaging capability for rats with high image quality.

Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.

PURPOSE: This study aims to compare two approaches using only emission PET data and a convolution neural network (CNN) to correct the attenuation (µ) of the annihilation photons in PET. METHODS: One of the approaches uses a CNN to generate µ-maps from the non-attenuation-corrected (NAC) PET images (µ-CNNNAC). In the other method, CNN is used to improve the accuracy of µ-maps generated using maximum likelihood estimation of activity and attenuation (MLAA) reconstruction (µ-CNNMLAA). We investigated the improvement in the CNN performance by combining the two methods (µ-CNNMLAA+NAC) and the suitability of µ-CNNNAC for providing the scatter distribution required for MLAA reconstruction. Image data from 18F-FDG (n = 100) or 68 Ga-DOTATOC (n = 50) PET/CT scans were used for neural network training and testing. RESULTS: The error of the attenuation correction factors estimated using µ-CT and µ-CNNNAC was over 7%, but that of scatter estimates was only 2.5%, indicating the validity of the scatter estimation from µ-CNNNAC. However, CNNNAC provided less accurate bone structures in the µ-maps, while the best results in recovering the fine bone structures were obtained by applying CNNMLAA+NAC. Additionally, the µ-values in the lungs were overestimated by CNNNAC. Activity images (λ) corrected for attenuation using µ-CNNMLAA and µ-CNNMLAA+NAC were superior to those corrected using µ-CNNNAC, in terms of their similarity to λ-CT. However, the improvement in the similarity with λ-CT by combining the CNNNAC and CNNMLAA approaches was insignificant (percent error for lung cancer lesions, λ-CNNNAC = 5.45% ± 7.88%; λ-CNNMLAA = 1.21% ± 5.74%; λ-CNNMLAA+NAC = 1.91% ± 4.78%; percent error for bone cancer lesions, λ-CNNNAC = 1.37% ± 5.16%; λ-CNNMLAA = 0.23% ± 3.81%; λ-CNNMLAA+NAC = 0.05% ± 3.49%). CONCLUSION: The use of CNNNAC was feasible for scatter estimation to address the chicken-egg dilemma in MLAA reconstruction, but CNNMLAA outperformed CNNNAC.

Data-driven respiratory phase-matched PET attenuation correction without CT.

We propose a deep learning-based data-driven respiratory phase-matched gated-PET attenuation correction (AC) method that does not need a gated-CT. The proposed method is a multi-step process that consists of data-driven respiratory gating, gated attenuation map estimation using maximum-likelihood reconstruction of attenuation and activity (MLAA) algorithm, and enhancement of the gated attenuation maps using convolutional neural network (CNN). The gated MLAA attenuation maps enhanced by the CNN allowed for the phase-matched AC of gated-PET images. We conducted a non-rigid registration of the gated-PET images to generate motion-free PET images. We trained the CNN by conducting a 3D patch-based learning with 80 oncologic whole-body18F-fluorodeoxyglucose (18F-FDG) PET/CT scan data and applied it to seven regional PET/CT scans that cover the lower lung and upper liver. We investigated the impact of the proposed respiratory phase-matched AC of PET without utilizing CT on tumor size and standard uptake value (SUV) assessment, and PET image quality (%STD). The attenuation corrected gated and motion-free PET images generated using the proposed method yielded sharper organ boundaries and better noise characteristics than conventional gated and ungated PET images. A banana artifact observed in a phase-mismatched CT-based AC was not observed in the proposed approach. By employing the proposed method, the size of tumor was reduced by 12.3% and SUV90%was increased by 13.3% in tumors with larger movements than 5 mm. %STD of liver uptake was reduced by 11.1%. The deep learning-based data-driven respiratory phase-matched AC method improved the PET image quality and reduced the motion artifacts.

Performance Evaluation of SimPET-X, a PET Insert for Simultaneous Mouse Total-Body PET/MR Imaging.

PURPOSE: In this study, a small animal PET insert (SimPET-X, Brightonix Imaging Inc.) for simultaneous PET/MR imaging studies is presented. This insert covers an 11-cm-long axial field-of-view (FOV) and enables imaging of mouse total-bodies and rat heads. PROCEDURES: SimPET-X comprises 16 detector modules to yield a ring diameter of 63 mm and an axial FOV of 110 mm. The detector module supports four detector blocks, each comprising two 4 × 4 SiPM arrays coupled with a 20 × 9 array of LSO crystals (1.2 × 1.2 × 10 mm3). The physical characteristics of SimPET-X were measured in accordance with the NEMA NU4-2008 standard protocol. In addition, we assessed the compatibility of SimPET-X with a small animal-dedicated MRI (M7, Aspect Imaging) and conducted phantom and animal studies. RESULTS: The radial spatial resolutions at the center based on 3D OSEM without and with the warm background were 0.73 mm and 0.99 mm, respectively. The absolute peak sensitivity of the system was 10.44% with an energy window of 100-900 keV and 8.27% with an energy window of 250-750 keV. The peak NECR and scatter fraction for the mouse phantom were 348 kcps at 26.2 MBq and 22.1% with an energy window of 250-750 keV, respectively. The standard deviation of pixel value in the uniform region of an NEMA IQ phantom was 4.57%. The spillover ratios for air- and water-filled chambers were 9.0% and 11.0%, respectively. In the hot-rod phantom image reconstructed using 3D OSEM-PSF, all small rods were resolved owing to the high spatial resolution of the SimPET-X system. There was no notable interference between SimPET-X and M7 MRI. SimPET-X provided high-quality mouse images with superior spatial resolution, sensitivity, and counting rate performance. CONCLUSION: SimPET-X yielded a remarkably improved sensitivity and NECR compared with SimPETTM.

Development and Initial Results of a Brain PET Insert for Simultaneous 7-Tesla PET/MRI Using an FPGA-Only Signal Digitization Method.

In study, we developed a positron emission tomography (PET) insert for simultaneous brain imaging within 7-Tesla (7T) magnetic resonance (MR) imaging scanners. The PET insert has 18 sectors, and each sector is assembled with two-layer depth-of-interaction (DOI)-capable high-resolution block detectors. The PET scanner features a 16.7-cm-long axial field-of-view (FOV) to provide entire human brain images without bed movement. The PET scanner early digitizes a large number of block detector signals at a front-end data acquisition (DAQ) board using a novel field-programmable gate array (FPGA)-only signal digitization method. All the digitized PET data from the front-end DAQ boards are transferred using gigabit transceivers via non-magnetic high-definition multimedia interface (HDMI) cables. A back-end DAQ system provides a common clock and synchronization signal for FPGAs over the HDMI cables. An active cooling system using copper heat pipes is applied for thermal regulation. All the 2.17-mm-pitch crystals with two-layer DOI information were clearly identified in the block detectors, exhibiting a system-level energy resolution of 12.6%. The PET scanner yielded clear hot-rod and Hoffman brain phantom images and demonstrated 3D PET imaging capability without bed movement. We also performed a pilot simultaneous PET/MR imaging study of a brain phantom. The PET scanner achieved a spatial resolution of 2.5 mm at the center FOV (NU 4) and a sensitivity of 18.9 kcps/MBq (NU 2) and 6.19% (NU 4) in accordance with the National Electrical Manufacturers Association (NEMA) standards.

[18F]CB251 PET/MR imaging probe targeting translocator protein (TSPO) independent of its Polymorphism in a Neuroinflammation Model.

The 18 kDa translocator protein (TSPO) has been proposed as a biomarker for the detection of neuroinflammation. Although various PET probes targeting TSPO have been developed, a highly selective probe for detecting TSPO is still needed because single nucleotide polymorphisms in the human TSPO gene greatly affect the binding affinity of TSPO ligands. Here, we describe the visualization of neuroinflammation with a multimodality imaging system using our recently developed TSPO-targeting radionuclide PET probe [18F]CB251, which is less affected by TSPO polymorphisms. Methods: To test the selectivity of [18F]CB251 for TSPO polymorphisms, 293FT cells expressing polymorphic TSPO were generated by introducing the coding sequences of wild-type (WT) and mutant (Alanine → Threonine at 147th Amino Acid; A147T) forms. Competitive inhibition assay was conducted with [3H]PK11195 and various TSPO ligands using membrane proteins isolated from 293FT cells expressing TSPO WT or mutant-A147T, representing high-affinity binder (HAB) or low-affinity binder (LAB), respectively. IC50 values of each ligand to [3H]PK11195 in HAB or LAB were measured and the ratio of IC50 values of each ligand to [3H]PK11195 in HAB to LAB was calculated, indicating the sensitivity of TSPO polymorphism. Cellular uptake of [18F]CB251 was measured with different TSPO polymorphisms, and phantom studies of [18F]CB251-PET using 293FT cells were performed. To test TSPO-specific cellular uptake of [18F]CB251, TSPO expression was regulated with pCMV-TSPO (or shTSPO)/eGFP vector. Intracranial lipopolysaccharide (LPS) treatment was used to induce regional inflammation in the mouse brain. Gadolinium (Gd)-DOTA MRI was used to monitor the disruption of the blood-brain barrier (BBB) and infiltration by immune cells. Infiltration of peripheral immune cells across the BBB, which exacerbates neuroinflammation to produce higher levels of neurotoxicity, was also monitored with bioluminescence imaging (BLI). Peripheral immune cells isolated from luciferase-expressing transgenic mice were transferred to syngeneic inflamed mice. Neuroinflammation was monitored with [18F]CB251-PET/MR and BLI. To evaluate the effects of anti-inflammatory agents on intracranial inflammation, an inflammatory cytokine inhibitor, 2-cyano-3, 12-dioxooleana-1, 9-dien-28-oic acid methyl ester (CDDO-Me) was administered in intracranial LPS challenged mice. Results: The ratio of IC50 values of [18F]CB251 in HAB to LAB indicated similar binding affinity to WT and mutant TSPO and was less affected by TSPO polymorphisms. [18F]CB251 was specific for TSPO, and its cellular uptake reflected the amount of TSPO. Higher [18F]CB251 uptake was also observed in activated immune cells. Simultaneous [18F]CB251-PET/MRI showed that [18F]CB251 radioactivity was co-registered with the MR signals in the same region of the brain of LPS-injected mice. Luciferase-expressing peripheral immune cells were located at the site of LPS-injected right striatum. Quantitative evaluation of the anti-inflammatory effect of CDDO-Me on neuroinflammation was successfully monitored with TSPO-targeting [18F]CB251-PET/MR and BLI. Conclusion: Our results indicate that [18F]CB251-PET has great potential for detecting neuroinflammation with higher TSPO selectivity regardless of polymorphisms. Our multimodal imaging system, [18F]CB251-PET/MRI, tested for evaluating the efficacy of anti-inflammatory agents in preclinical studies, might be an effective method to assess the severity and therapeutic response of neuroinflammation.

Recovery of inter-detector and inter-crystal scattering in brain PET based on LSO and GAGG crystals.

Gadolinium aluminum gallium garnet (GAGG) is a promising scintillator crystal for positron emission tomography (PET) detectors owing to its advantages of energy resolution, light yield, and absence of intrinsic radiation. However, a large portion of the incident photons undergoes Compton scattering within GAGG crystal because of its low stopping power compared to that of lutetium-based crystals such as Lu2SiO5 (LSO). Inter-detector scattering (IDS) and inter-crystal scattering (ICS) result in loss of sensitivity and image quality of PET, respectively. We performed a Monte Carlo simulation study to evaluate IDS recovery in our currently developing brain-dedicated PET, and extended the idea to ICS recovery. We also compared the impact of the recoveries on LSO- and GAGG-based PET scanners. We measured the sensitivity and spatial resolution of the brain PET, and analyzed the image quality using a lesion phantom, a hot-rod phantom, and a 2D Hoffman phantom with applying IDS or ICS recovery. IDS recovery increased the PET sensitivity and improved the noise level of the reconstructed images. ICS recovery enhanced the spatial resolution and the contrast of the images was improved. As the occurrence rates of IDS and ICS were higher in GAGG than in LSO, the overall impact of IDS or ICS recovery was significant in GAGG. In conclusion, we showed that the proportional method would be suitable for IDS and ICS recoveries of PET, and emphasized the importance of ICS and IDS recoveries for PET using crystals with low stopping power.

SimPET: a Preclinical PET Insert for Simultaneous PET/MR Imaging.

PURPOSE: SimPET/M7 system is a small-animal dedicated simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) scanner. The SimPET insert has been upgraded from its prototype with a focus on count rate performance and sensitivity. The M7 scanner is a 1-T permanent magnet-based compact MRI system without any cryogens. Here, we present performance evaluation results of SimPET along with the results of mutual interference evaluation and simultaneously acquired PET/MR imaging. PROCEDURES: Following NEMA NU 4-2008 standard, we evaluated the performance of the SimPET system. The M7 MRI compatibility of SimPET was also assessed by analyzing MRI images of a uniform phantom under different PET conditions and PET count rates with different MRI pulse sequences. Mouse imaging was performed including a whole-body 18F-NaF PET scan and a simultaneous PET/MRI scan with 64Cu-NOTA-ironoxide. RESULTS: The spatial resolution at center based on 3D OSEM without and with warm background was 0.7 mm and 1.45 mm, respectively. Peak sensitivity was 4.21 % (energy window = 250-750 keV). The peak noise equivalent count rate with the same energy window was 151 kcps at 38.4 MBq. The uniformity was 4.42 %, and the spillover ratios in water- and air-filled chambers were 14.6 % and 12.7 %, respectively. In the hot rod phantom image, 0.75-mm-diameter rods were distinguishable. There were no remarkable differences in the SNR and uniformity of MRI images and PET count rates with different PET conditions and MRI pulse sequences. In the whole-body 18F-NaF PET images, fine skeletal structures were well resolved. In the simultaneous PET/MRI study with 64Cu-NOTA-ironoxide, both PET and MRI signals changed before and after injection of the dual-modal imaging probe, which was evident with the exact spatiotemporal correlation. CONCLUSIONS: We demonstrated that the SimPET scanner has a high count rate performance and excellent spatial resolution. The combined SimPET/M7 enabled simultaneous PET/MR imaging studies with no remarkable mutual interference between the two imaging modalities.

Comparator-less PET data acquisition system using single-ended memory interface input receivers of FPGA.

In this study, we propose a linear field-programmable gate array (FPGA)-based charge measurement method by combining a charge-to-time converter (QTC) with a single-ended memory interface (SeMI) input receiver. The QTC automatically converts the input charge into a dual-slope pulse, which has a width proportional to the input charge. Dual-slope pulses are directly digitized by the FPGA input/output (I/O) buffers configured with SeMI input receivers. A proof-of-concept comparator-less QTC/SeMI data acquisition (DAQ) system, consisting of 132 energy and 33 timing channels, was developed and applied to a prototype brain-dedicated positron emission tomography (PET) scanner. The PET scanner consisted of 14 sectors, each containing 2 × 1 block detectors, and each block detector yielded four energy signals and one timing signal. Because a single QTC/SeMI DAQ system can receive signals from up to eight sectors, two QTC/SeMI DAQ systems connected using high-speed gigabit transceivers were used to acquire data from the PET scanner. All crystals in the PET block detectors, consisting of dual-layer stacked lutetium oxyorthosilicate (LSO) scintillation crystal and silicon photomultiplier arrays, were clearly resolved in the flood maps with an excellent energy resolution. The PET images of hot-rod, cylindrical, and two-dimensional Hoffman brain phantoms were also acquired using the prototype PET scanner and two QTC/SeMI DAQ systems.

Generation of PET Attenuation Map for Whole-Body Time-of-Flight 18F-FDG PET/MRI Using a Deep Neural Network Trained with Simultaneously Reconstructed Activity and Attenuation Maps.

We propose a new deep learning-based approach to provide more accurate whole-body PET/MRI attenuation correction than is possible with the Dixon-based 4-segment method. We use activity and attenuation maps estimated using the maximum-likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a convolutional neural network (CNN) to learn a CT-derived attenuation map. Methods: The whole-body 18F-FDG PET/CT scan data of 100 cancer patients (38 men and 62 women; age, 57.3 ± 14.1 y) were retrospectively used for training and testing the CNN. A modified U-net was trained to predict a CT-derived µ-map (µ-CT) from the MLAA-generated activity distribution (λ-MLAA) and µ-map (µ-MLAA). We used 1.3 million patches derived from 60 patients' data for training the CNN, data of 20 others were used as a validation set to prevent overfitting, and the data of the other 20 were used as a test set for the CNN performance analysis. The attenuation maps generated using the proposed method (µ-CNN), µ-MLAA, and 4-segment method (µ-segment) were compared with the µ-CT, a ground truth. We also compared the voxelwise correlation between the activity images reconstructed using ordered-subset expectation maximization with the µ-maps, and the SUVs of primary and metastatic bone lesions obtained by drawing regions of interest on the activity images. Results: The CNN generates less noisy attenuation maps and achieves better bone identification than MLAA. The average Dice similarity coefficient for bone regions between µ-CNN and µ-CT was 0.77, which was significantly higher than that between µ-MLAA and µ-CT (0.36). Also, the CNN result showed the best pixel-by-pixel correlation with the CT-based results and remarkably reduced differences in activity maps in comparison to CT-based attenuation correction. Conclusion: The proposed deep neural network produced a more reliable attenuation map for 511-keV photons than the 4-segment method currently used in whole-body PET/MRI studies.

Systematic study on factors influencing the performance of interdetector scatter recovery in small-animal PET.

PURPOSE: Interdetector scatter (IDS) is a triple coincidence caused by the Compton scatter of an annihilation photon from one detector block to another which frequently occurs in small-animal positron emission tomography (PET). By finding the true lines-of-response (LORs) of annihilation photon pairs among three possible LORs in IDS events, we can utilize these recovered events to improve the sensitivity of PET systems. IDS recovery should be accurate to yield reliable images with relatively short scan times. We systemically investigated physical factors affecting IDS recovery performance, focusing on the reconstructed image quality of small-animal PET. We evaluated sensitivity increase, recovery accuracy, and image quality by applying different combinations of energy window, recovery scheme, and scanner properties. METHODS: We used GATE Monte Carlo simulation to acquire coincidence events from a NEMA NU 4-2008 image quality phantom using small-animal PET scanner with axial field of view of 55 mm and diameter of 64 mm. We first defined energy window criteria to obtain valid IDS events. Their role was to assign triple coincidences as IDS events and to restrict the number of LOR candidates to two. We tested three different energy windows around 511 keV. Second, we applied four different recovery schemes (maximum energy, Compton kinematics, neural network, and proportional) to assigned IDS events. To measure the effects of scanner properties, energy resolutions of 0-20% and one to four depth-of-interaction (DOI) layers were simulated. For every combination of the factors, we measured sensitivity increase and recovery accuracy. We also analyzed the reconstructed images for each IDS recovery method in terms of mean pixel intensity, noise, signal-to-noise ratio (SNR), contrast, and recovery coefficients. RESULTS: Sensitivity increase depended on the energy window and energy resolution. The maximum increase in sensitivity was 33% when energy window of [250, 750] keV was applied. Higher energy resolution yielded larger sensitivity increase, especially for narrow windows. Recovery accuracy was affected by all the factors tested in this study. Accuracy increased with narrower energy window, and a neural network scheme was the most accurate. The better energy resolution and DOI capability improved accuracy by providing precise measurement of energies and interaction positions. In image quality analysis, noise and SNR were highly dependent on the sensitivity increase and energy window. When the same energy window was applied, SNR, contrast, and recovery coefficients were higher with higher accuracy of the scheme. Meanwhile, the proportional scheme yielded the best image quality among the schemes and reduced 20% of scan time to achieve the same SNR as that of double coincidence images. CONCLUSIONS: As a fundamental research for real implementation of IDS recovery, we conducted a simulation study to evaluate the factors affecting sensitivity increase, recovery accuracy, and image quality. Sensitivity increase was dependent on the energy window and energy resolution, while the recovery accuracy was affected by energy window, recovery scheme, energy resolution, and DOI capability. In image quality analysis, sensitivity increase and recovery accuracy dominantly affected the noise and quantitative accuracy, respectively. Among the recovery schemes, the proportional scheme obtained the best image quality.

Computed tomography super-resolution using deep convolutional neural network.

The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolution images using the modified U-Net. To verify the proposed method, we train and test the CNN using axially averaged data of existing thin-slice CT images as input and their middle slice as the label. Fifty-two CT studies are used as the CNN training set, and 13 CT studies are used as the test set. We perform five-fold cross-validation to confirm the performance consistency. Because all input and output images are used in two-dimensional slice format, the total number of slices for training the CNN is 7670. We assess the performance of the proposed method with respect to the resolution and contrast, as well as the noise properties. The CNN generates output images that are virtually equivalent to the ground truth. The most remarkable image-recovery improvement by the CNN is deblurring of boundaries of bone structures and air cavities. The CNN output yields an approximately 10% higher peak signal-to-noise ratio and lower normalized root mean square error than the input (thicker slices). The CNN output noise level is lower than the ground truth and equivalent to the iterative image reconstruction result. The proposed deep learning method is useful for both super-resolution and de-noising.

Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep Learning.

Simultaneous reconstruction of activity and attenuation using the maximum-likelihood reconstruction of activity and attenuation (MLAA) augmented by time-of-flight information is a promising method for PET attenuation correction. However, it still suffers from several problems, including crosstalk artifacts, slow convergence speed, and noisy attenuation maps (µ-maps). In this work, we developed deep convolutional neural networks (CNNs) to overcome these MLAA limitations, and we verified their feasibility using a clinical brain PET dataset. Methods: We applied the proposed method to one of the most challenging PET cases for simultaneous image reconstruction (18F-fluorinated-N-3-fluoropropyl-2-ß-carboxymethoxy-3-ß-(4-iodophenyl)nortropane [18F-FP-CIT] PET scans with highly specific binding to striatum of the brain). Three different CNN architectures (convolutional autoencoder [CAE], Unet, and Hybrid of CAE) were designed and trained to learn a CT-derived µ-map (µ-CT) from the MLAA-generated activity distribution and µ-map (µ-MLAA). The PET/CT data of 40 patients with suspected Parkinson disease were used for 5-fold cross-validation. For the training of CNNs, 800,000 transverse PET and CT slices augmented from 32 patient datasets were used. The similarity to µ-CT of the CNN-generated µ-maps (µ-CAE, µ-Unet, and µ-Hybrid) and µ-MLAA was compared using Dice similarity coefficients. In addition, we compared the activity concentration of specific (striatum) and nonspecific (cerebellum and occipital cortex) binding regions and the binding ratios in the striatum in the PET activity images reconstructed using those µ-maps. Results: The CNNs generated less noisy and more uniform µ-maps than the original µ-MLAA. Moreover, the air cavities and bones were better resolved in the proposed CNN outputs. In addition, the proposed deep learning approach was useful for mitigating the crosstalk problem in the MLAA reconstruction. The Hybrid network of CAE and Unet yielded the most similar µ-maps to µ-CT (Dice similarity coefficient in the whole head = 0.79 in the bone and 0.72 in air cavities), resulting in only about a 5% error in activity and binding ratio quantification. Conclusion: The proposed deep learning approach is promising for accurate attenuation correction of activity distribution in time-of-flight PET systems.

Proof-of-concept prototype time-of-flight PET system based on high-quantum-efficiency multianode PMTs.

PURPOSE: Time-of-flight (TOF) information in positron emission tomography (PET) scanners enhances the diagnostic power of PET scans owing to the increased signal-to-noise ratio of reconstructed images. There are numerous additional benefits of TOF reconstruction, including the simultaneous estimation of activity and attenuation distributions from emission data only. Exploring further TOF gains by using TOF PET scanners is important because it can broaden the applications of PET scans and expand our understanding of TOF techniques. Herein, we present a prototype TOF PET scanner with fine-time performance that can experimentally demonstrate the benefits of TOF information. METHODS: A single-ring PET system with a coincidence resolving time of 360 ps and a spatial resolution of 3.1/2.2 mm (filtered backprojection/ordered-subset expectation maximization) was developed. The scanner was based on advanced high-quantum-efficiency (high-QE) multianode photomultiplier tubes (PMTs). The impact of its fine-time performance was demonstrated by evaluating body phantom images reconstructed with and without TOF information. Moreover, the feasibility of the scanner as an experimental validator of TOF gains was verified by investigating the improvement of images under various conditions, such as the use of joint estimation algorithms of activity and attenuation, erroneous data correction factors (e.g., without normalization correction), and incompletely sampled data. RESULTS: The prototype scanner showed excellent performance, producing improved phantom images, when TOF information was employed in the reconstruction process. In addition, investigation of the TOF benefits using the phantom data in different conditions verified the usefulness of the developed system for demonstrating the practical effects of TOF reconstruction. CONCLUSIONS: We developed a prototype TOF PET scanner with good performance and a fine-timing resolution based on advanced high-QE multianode PMTs and demonstrated its feasibility as an experimental validator of TOF gains, suggesting its usefulness for investigating new applications of PET scans and clarifying TOF techniques in detail.

Prototype pre-clinical PET scanner with depth-of-interaction measurements using single-layer crystal array and single-ended readout.

In this study, we developed a proof-of-concept prototype PET system using a pair of depth-of-interaction (DOI) PET detectors based on the proposed DOI-encoding method and digital silicon photomultiplier (dSiPM). Our novel cost-effective DOI measurement method is based on a triangular-shaped reflector that requires only a single-layer pixelated crystal and single-ended signal readout. The DOI detector consisted of an 18 × 18 array of unpolished LYSO crystal (1.47 × 1.47 × 15 mm3) wrapped with triangular-shaped reflectors. The DOI information was encoded by depth-dependent light distribution tailored by the reflector geometry and DOI correction was performed using four-step depth calibration data and maximum-likelihood (ML) estimation. The detector pair and the object were placed on two motorized rotation stages to demonstrate 12-block ring PET geometry with 11.15 cm diameter. Spatial resolution was measured and phantom and animal imaging studies were performed to investigate imaging performance. All images were reconstructed with and without the DOI correction to examine the impact of our DOI measurement. The pair of dSiPM-based DOI PET detectors showed good physical performances respectively: 2.82 and 3.09 peak-to-valley ratios, 14.30% and 18.95% energy resolution, and 4.28 and 4.24 mm DOI resolution averaged over all crystals and all depths. A sub-millimeter spatial resolution was achieved at the center of the field of view (FOV). After applying ML-based DOI correction, maximum 36.92% improvement was achieved in the radial spatial resolution and a uniform resolution was observed within 5 cm of transverse PET FOV. We successfully acquired phantom and animal images with improved spatial resolution and contrast by using the DOI measurement. The proposed DOI-encoding method was successfully demonstrated in the system level and exhibited good performance, showing its feasibility for animal PET applications with high spatial resolution and sensitivity.

Simultaneous Multiparametric PET/MRI with Silicon Photomultiplier PET and Ultra-High-Field MRI for Small-Animal Imaging.

UNLABELLED: Visualization of biologic processes at molecular and cellular levels has revolutionized the understanding and treatment of human diseases. However, no single biomedical imaging modality provides complete information, resulting in the emergence of multimodal approaches. Combining state-of-the-art PET and MRI technologies without loss of system performance and overall image quality can provide opportunities for new scientific and clinical innovations. Here, we present a multiparametric PET/MR imager based on a small-animal dedicated, high-performance, silicon photomultiplier (SiPM) PET system and a 7-T MR scanner. METHODS: A SiPM-based PET insert that has the peak sensitivity of 3.4% and center volumetric resolution of 1.92/0.53 mm(3) (filtered backprojection/ordered-subset expectation maximization) was developed. The SiPM PET insert was placed between the mouse body transceiver coil and gradient coil of a 7-T small-animal MRI scanner for simultaneous PET/MRI. Mutual interference between the MRI and SiPM PET systems was evaluated using various MR pulse sequences. A cylindric corn oil phantom was scanned to assess the effects of the SiPM PET on the MR image acquisition. To assess the influence of MRI on the PET imaging functions, several PET performance indicators including scintillation pulse shape, flood image quality, energy spectrum, counting rate, and phantom image quality were evaluated with and without the application of MR pulse sequences. Simultaneous mouse PET/MRI studies were also performed to demonstrate the potential and usefulness of the multiparametric PET/MRI in preclinical applications. RESULTS: Excellent performance and stability of the PET system were demonstrated, and the PET/MRI combination did not result in significant image quality degradation of either modality. Finally, simultaneous PET/MRI studies in mice demonstrated the feasibility of the developed system for evaluating the biochemical and cellular changes in a brain tumor model and facilitating the development of new multimodal imaging probes. CONCLUSION: We developed a multiparametric imager with high physical performance and good system stability and demonstrated its feasibility for small-animal experiments, suggesting its usefulness for investigating in vivo molecular interactions of metabolites, and cross-validation studies of both PET and MRI.

Evaluation of a silicon photomultiplier PET insert for simultaneous PET and MR imaging.

PURPOSE: In this study, the authors present a silicon photomultiplier (SiPM)-based positron emission tomography (PET) insert dedicated to small animal imaging with high system performance and robustness to temperature change. METHODS: The insert consists of 64 LYSO-SiPM detector blocks arranged in 4 rings of 16 detector blocks to yield a ring diameter of 64 mm and axial field of view of 55 mm. Each detector block consists of a 9 × 9 array of LYSO crystals (1.2 × 1.2 × 10 mm(3)) and a monolithic 4 × 4 SiPM array. The temperature of each monolithic SiPM is monitored, and the proper bias voltage is applied according to the temperature reading in real time to maintain uniform performance. The performance of this PET insert was characterized using National Electrical Manufacturers Association NU 4-2008 standards, and its feasibility was evaluated through in vivo mouse imaging studies. RESULTS: The PET insert had a peak sensitivity of 3.4% and volumetric spatial resolutions of 1.92 (filtered back projection) and 0.53 (ordered subset expectation maximization) mm(3) at center. The peak noise equivalent count rate and scatter fraction were 42.4 kcps at 15.08 MBq and 16.5%, respectively. By applying the real-time bias voltage adjustment, an energy resolution of 14.2% ± 0.3% was maintained and the count rate varied ≤1.2%, despite severe temperature changes (10-30 °C). The mouse imaging studies demonstrate that this PET insert can produce high-quality images useful for imaging studies on the small animals. CONCLUSIONS: The developed MR-compatible PET insert is designed for insertion into a narrow-bore magnetic resonance imaging scanner, and it provides excellent imaging performance for PET/MR preclinical studies.