3D Positron Emission Tomography Research Articles

Improvement in the estimation of perfusable tissue fraction and myocardial flow reserve in the ischemic myocardial lesions using ECG-gated dynamic myocardial PET with 15O-water.

Perfusable tissue fraction (PTF) and myocardial flow reserve (MFR) from 15O-water dynamic positron emission tomography (PET) are parameters of myocardial viability. However, myocardial motion causes errors in these values. We aimed to develop accurate estimation of PTF and MFR in ischemic lesions using an electro-cardiogram (ECG)-gated dynamic myocardial PET with 15O-water. Twenty-seven patients with ischemic heart disease were enrolled. All patients underwent percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). List mode 3D PET data and ECG signals were acquired using Philips Gemini TF64 instrument. For each scan, 500MBq of 15O-water was infused slowly for 2min, and the dynamic data were scanned for 6min. Both non-gated dynamic images and ECG-gated diastolic dynamic images were reconstructed. On the myocardial PET images of each patient, the entire myocardial region of interest (ROI) was set and divided into 17 segments. Myocardial blood flow in the resting state (rest MBF), hyperemic state (stress MBF), PTF, and MFR in each segment were estimated from both non-gated and ECG-gated dynamic PET images. Coronary arteriograms were obtained for all patients. In total, 128 normal segments without stenosis and 50 ischemic segments with > 90% stenosis were evaluated. In the ischemic myocardial segments, the PTF with ECG-gated PET was estimated as significantly lower than that with non-gated PET (0.63 ± 0.09 vs. 0.72 ± 0.08 [mL/mL], p < 0.001). The ECG-gated PET estimated a significantly lower PTF in the ischemic segments than in the normal segments (0.63 ± 0.09 vs. 0.67 ± 0.07 [mL/mL], p < 0.01). In the normal segments, the ECG-gated PET detected no significant difference in MFR compared with those from the non-gated PET (2.15 ± 0.76 vs. 2.24 ± 0.79, p = 0.28). However, in the ischemic myocardial segments, the MFR with ECG-gated PET was estimated as significantly lower than that with the non-gated PET (1.23 ± 0.29 vs. 1.69 ± 0.71, p < 0.001). The ECG-gated PET presented a significantly higher inter-observer reproducibility of PTF and rest MBF than the non-gated PET (p < 0.01). Neither stress MBF nor MFR yielded significant differences in inter-observer reproducibility between the ECG-gated and non-gated PET. The ECG-gated dynamic 15O-water PET suppressed the myocardial motion effect and resulted in a lower PTF and MFR in ischemic myocardial lesions than the non-gated PET. The ECG-gated PET seemed to be better than the conventional non-gated dynamic PET for the detection of ischemic myocardial lesion.

Score-Based Generative Models for PET Image Reconstruction

Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography. However, their application to Positron Emission Tomography (PET) is still largely unexplored. PET image reconstruction involves a variety of challenges, including Poisson noise with high variance and a wide dynamic range. To address these challenges, we propose several PET-specific adaptations of score-based generative models. The proposed framework is developed for both 2D and 3D PET. In addition, we provide an extension to guided reconstruction using magnetic resonance images. We validate the approach through extensive 2D and 3D &lt;emph&gt;in-silico&lt;/emph&gt; experiments with a model trained on patient-realistic data without lesions, and evaluate on data without lesions as well as out-of-distribution data with lesions. This demonstrates the proposed method’s robustness and significant potential for improved PET reconstruction.

Joint learning framework of cross-modal synthesis and diagnosis for Alzheimer’s disease by mining underlying shared modality information

Alzheimer’s disease (AD) is one of the most common neurodegenerative disorders presenting irreversible progression of cognitive impairment. How to identify AD as early as possible is critical for intervention with potential preventive measures. Among various neuroimaging modalities used to diagnose AD, functional positron emission tomography (PET) has higher sensitivity than structural magnetic resonance imaging (MRI), but it is also costlier and often not available in many hospitals. How to leverage massive unpaired unlabeled PET to improve the diagnosis performance of AD from MRI becomes rather important. To address this challenge, this paper proposes a novel joint learning framework of unsupervised cross-modal synthesis and AD diagnosis by mining underlying shared modality information, improving the AD diagnosis from MRI while synthesizing more discriminative PET images. We mine underlying shared modality information in two aspects: diversifying modality information through the cross-modal synthesis network and locating critical diagnosis-related patterns through the AD diagnosis network. First, to diversify the modality information, we propose a novel unsupervised cross-modal synthesis network, which implements the inter-conversion between 3D PET and MRI in a single model modulated by the AdaIN module. Second, to locate shared critical diagnosis-related patterns, we propose an interpretable diagnosis network based on fully 2D convolutions, which takes either 3D synthesized PET or original MRI as input. Extensive experimental results on the ADNI dataset show that our framework can synthesize more realistic images, outperform the state-of-the-art AD diagnosis methods, and have better generalization on external AIBL and NACC datasets.

In Situ Measurements of Dynamic Bacteria Transport and Attachment in Heterogeneous Sand-Packed Columns.

Prevention, mitigation, and regulation of bacterial contaminants in groundwater require a fundamental understanding of the mechanisms of transport and attachment in complex geological materials. Discrepancies in bacterial transport behaviors observed between field studies and laboratory experiments indicate an incomplete understanding of dynamic bacterial transport and immobilization processes in realistic heterogeneous geologic systems. Here, we develop a new experimental approach for in situ quantification of dynamic bacterial transport and attachment distribution in geologic media that relies on radiolabelingEscherichia coliwith positron-emitting radioisotopes and quantifying transport with three-dimensional (3D) positron emission tomography (PET) imaging. Our results indicate that the highest bacterial attachment occurred at the interfaces between sand layers oriented orthogonal to the direction of flow. The predicted bacterial attachment from a 3D numerical model matched the experimental PET results, highlighting that the experimentally observed bacterial transport behavior can be accurately captured with a distribution of a first-order irreversible attachment model. This is the first demonstration of the direct measurement of attachment coefficient distributions from bacterial transport experiments in geologic media and provides a transformational approach to better understand bacterial transport mechanisms, improve model parametrization, and accurately predict how local geologic conditions can influence bacterial fate and transport in groundwater.

Assessment of resting myocardial blood flow in regions of known transmural scar to confirm accuracy and precision of 3D cardiac positron emission tomography

BackgroundComposite invasive and non-invasive data consistently demonstrate that resting myocardial blood flow (rMBF) in regions of known transmural myocardial scar (TMS) converge on a value of ~ 0.30 mL/min/g or lower. This value has been confirmed using the 3 most common myocardial perfusion agents (13N, 15O-H2O and 82Rb) incorporating various kinetic models on older 2D positron emission tomography (PET) systems. Thus, rMBF in regions of TMS can serve as a reference “truth” to evaluate low-end accuracy of various PET systems and software packages (SWPs). Using 82Rb on a contemporary 3D-PET-CT system, we sought to determine whether currently available SWP can accurately and precisely measure rMBF in regions of known TMS.ResultsMedian rMBF (in mL/min/g) and COV in regions of TMS were 0.71 [IQR 0.52–1.02] and 0.16 with 4DM; 0.41 [0.34–0.54] and 0.10 with 4DM-FVD; 0.66 [0.51–0.85] and 0.11 with Cedars; 0.51 [0.43–0.61] and 0.08 with Emory-Votaw; 0.37 [0.30–0.42], 0.07 with Emory-Ottawa, and 0.26 [0.23–0.32], COV 0.07 with HeartSee.ConclusionsSWPs varied widely in low end accuracy based on measurement of rMBF in regions of known TMS. 3D PET using 82Rb and HeartSee software accurately (0.26 mL/min/g, consistent with established values) and precisely (COV = 0.07) quantified rMBF in regions of TMS. The Emory-Ottawa software yielded the next-best accuracy (0.37 mL/min/g), though rMBF was higher than established gold-standard values in ~ 5% of the resting scans. 4DM, 4DM-FDV, Cedars and Emory-Votaw SWP consistently resulted values higher than the established gold standard (0.71, 0.41, 0.66, 0.51 mL/min/g, respectively), with higher interscan variability (0.16, 0.11, 0.11, and 0.09, respectively).Trial registration: clinicaltrial.gov, NCT05286593, Registered December 28, 2021, https://clinicaltrials.gov/ct2/show/NCT05286593.

Fully 3D implementation of the end-to-end deep image prior-based PET image reconstruction using block iterative algorithm

Objective. Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction method, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function. Approach. A practical implementation of a fully 3D PET image reconstruction could not be performed at present because of a graphics processing unit memory limitation. Consequently, we modify the DIP optimization to a block iteration and sequential learning of an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term is added to the loss function to enhance the quantitative accuracy of the PET image. Main results. We evaluated our proposed method using Monte Carlo simulation with [18F]FDG PET data of a human brain and a preclinical study on monkey-brain [18F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximum a posteriori EM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that, compared with other algorithms, the proposed method improved the PET image quality by reducing statistical noise and better preserved the contrast of brain structures and inserted tumors. In the preclinical experiment, finer structures and better contrast recovery were obtained with the proposed method. Significance. The results indicated that the proposed method could produce high-quality images without a prior training dataset. Thus, the proposed method could be a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.

Deep learning based registration for head motion correction in positron emission tomography as a strategy for improved image quantification

Objectives: Positron emission tomography (PET) is affected by various kinds of patient movement during a scan. Frame-by-frame image registration is one of the most practiced motion correction techniques. In recent years, deep learning has shown a remarkable ability to quickly and accurately register images once trained. This paper studies the feasibility of using a deep learning framework to correct 3D positron emission tomography image volumes for head motion in routine positron emission tomography imaging to improve quantification in motion impacted data.Materials and Methods: A neural network was trained with 3D positron emission tomography image volumes in an unsupervised manner to predict transformation parameters required to perform image registration. A multi-step convolutional neural network (CNN) was combined with a spatial transform layer. Pairs of target and source images were used as input to the network. To prepare the training dataset, a previously published TOF-PEPT algorithm was applied to automatically detect static frames where the patient remained in a relatively steady position and transitional frames where they underwent abrupt motion. A single image volume was reconstructed for each static frame. The image reconstructed from the first static frame served as the target image with images from subsequent static frames being used as source images. The trained neural network predicted transformation parameters that could be used to perform frame-by-frame image-based motion correction but also enabled raw listmode positron emission tomography data correction where individual line-of-responses were repositioned. Line profiles and ROIs were drawn across the reconstructed image volumes to compare performance and quantitative results between standard registration tools and the deep learning technique. Corrected volumes were further compared to motion free images quantitatively using Dice indices.Results: In total, one hundred 3D positron emission tomography image volumes were used to train the network. Cross-validation was carried out using a 4:1 ratio for the training and test data. A conventional algorithm for affine registration from the Advanced Normalization Tools (ANTs) software package served as a baseline. To evaluate the correction performance, the mean Dice index and standardized uptake value (SUV) were used. Application of the algorithm to clinical data showed good performance with respect to registration accuracy as well as processing time. The neural network yielded a mean Dice index of ∼0.87 which was similar to the advanced Normalization Tools algorithm and did so ∼3x faster using a multi-core CPU and ∼20x faster with a GPU. Standardized uptake value analysis showed that quantitative results were 30%–60% higher in the motion-corrected images, and the neural network performed better than or close to the advanced Normalization Tools.Conclusion: The aim of this work was to study the quantitative impact of using a data-driven deep learning motion correction technique for positron emission tomography data and assess its performance. The results showed the technique is capable of producing high quality registrations that compensate for patient motion that occurs during a scan and improve quantitative accuracy.

Auto-segmentation of head and neck tumors in positron emission tomography images using non-local means and morphological frameworks.

Accurately segmenting head and neck cancer (HNC) tumors in medical images is crucial for effective treatment planning. However, current methods for HNC segmentation are limited in their accuracy and efficiency. The present study aimed to design a model for segmenting HNC tumors in three-dimensional (3D) positron emission tomography (PET) images using Non-Local Means (NLM) and morphological operations. The proposed model was tested using data from the HECKTOR challenge public dataset, which included 408 patient images with HNC tumors. NLM was utilized for image noise reduction and preservation of critical image information. Following pre-processing, morphological operations were used to assess the similarity of intensity and edge information within the images. The Dice score, Intersection Over Union (IoU), and accuracy were used to evaluate the manual and predicted segmentation results. The proposed model achieved an average Dice score of 81.47 ± 3.15, IoU of 80 ± 4.5, and accuracy of 94.03 ± 4.44, demonstrating its effectiveness in segmenting HNC tumors in PET images. The proposed algorithm provides the capability to produce patient-specific tumor segmentation without manual interaction, addressing the limitations of current methods for HNC segmentation. The model has the potential to improve treatment planning and aid in the development of personalized medicine. Additionally, this model can be extended to effectively segment other organs from limited annotated medical images.

Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction.

It is proven that radiomic characteristics extracted from the tumor region are predictive. The first step in radiomic analysis is the segmentation of the lesion. However, this task is time consuming and requires a highly trained physician. This process could be automated using computer-aided detection (CAD) tools. Current state-of-the-art methods are trained in a supervised learning setting, which requires a lot of data that are usually not available in the medical imaging field. The challenge is to train one model to segment different types of tumors with only a weak segmentation ground truth. In this work, we propose a prediction framework including a 3D tumor segmentation in positron emission tomography (PET) images, based on a weakly supervised deep learning method, and an outcome prediction based on a 3D-CNN classifier applied to the segmented tumor regions. The key step is to locate the tumor in 3D. We propose to (1) calculate two maximum intensity projection (MIP) images from 3D PET images in two directions, (2) classify the MIP images into different types of cancers, (3) generate the class activation maps through a multitask learning approach with a weak prior knowledge, and (4) segment the 3D tumor region from the two 2D activation maps with a proposed new loss function for the multitask. The proposed approach achieves state-of-the-art prediction results with a small data set and with a weak segmentation ground truth. Our model was tested and validated for treatment response and survival in lung and esophageal cancers on 195 patients, with an area under the receiver operating characteristic curve (AUC) of 67% and 59%, respectively, and a dice coefficient of 73% and 0.77% for tumor segmentation.

The MVM ventilator and 3D PET scanner development arising from basic science work.

During the COVID-19 pandemic, members of the DarkSide-20k (DS) Dark Matter international experimental collaboration together with engineers, scientists and manufacturers in Italy, Canada, the USA and eight other countries diverted their research efforts to develop MVM, a new readily manufactured, relatively low-cost ventilator tailored to the most severe intubated patients. Using expertise developed for DS, they produced and received authorization for 7300 units delivered to the Canadian government for its stockpile, now being considered for donation to other countries in urgent need. Other members of DS are using photo-electronics innovation to develop 3Dpi a more sensitive positron emission tomography (PET) scanner that can provide better resolution and reduce doses, of particular value for providing access for pediatric patients.

Anatomical-guided attention enhances unsupervised PET image denoising performance.

Although supervised convolutional neural networks (CNNs) often outperform conventional alternatives for denoising positron emission tomography (PET) images, they require many low- and high-quality reference PET image pairs. Herein, we propose an unsupervised 3D PET image denoising method based on an anatomical information-guided attention mechanism. The proposed magnetic resonance-guided deep decoder (MR-GDD) utilizes the spatial details and semantic features of MR-guidance image more effectively by introducing encoder-decoder and deep decoder subnetworks. Moreover, the specific shapes and patterns of the guidance image do not affect the denoised PET image, because the guidance image is input to the network through an attention gate. In a Monte Carlo simulation of [18F]fluoro-2-deoxy-D-glucose (FDG), the proposed method achieved the highest peak signal-to-noise ratio and structural similarity (27.92±0.44dB/0.886±0.007), as compared with Gaussian filtering (26.68±0.10dB/0.807±0.004), image guided filtering (27.40±0.11dB/0.849±0.003), deep image prior (DIP) (24.22±0.43dB/0.737±0.017), and MR-DIP (27.65±0.42dB/0.879±0.007). Furthermore, we experimentally visualized the behavior of the optimization process, which is often unknown in unsupervised CNN-based restoration problems. For preclinical (using [18F]FDG and [11C]raclopride) and clinical (using [18F]florbetapir) studies, the proposed method demonstrates state-of-the-art denoising performance while retaining spatial resolution and quantitative accuracy, despite using a common network architecture for various noisy PET images with 1/10th of the full counts. These results suggest that the proposed MR-GDD can reduce PET scan times and PET tracer doses considerably without impacting patients.

Respiratory motion correction for enhanced quantification of hepatic lesions in simultaneous PET and DCE-MR imaging

Simultaneous positron-emission tomography (PET)-magnetic resonance (MR) imaging is a hybrid technique in oncological hepatic imaging combining soft-tissue and functional contrast of dynamic contrast enhanced MR (DCE-MR) with metabolic information from PET. In this context, respiratory motion represents a major challenge by introducing blurring, artifacts and misregistration in the liver. In this work, we propose a free-breathing 3D non-rigid respiratory motion correction framework for simultaneously acquired DCE-MR and PET data, which makes use of higher spatial resolution MR data to derive motion information used directly during image reconstruction to minimize image blurring and motion artifacts. The main aim was to increase contrast of hepatic metastases to improve their detection and characterization. DCE-MR data were acquired at 3T through a golden radial phase encoding scheme, enabling derivation of motion fields. These were used in the motion compensated image reconstruction of DCE-MR time-series (48 time-points, 6 s temporal resolution, 1.5 mm isotropic spatial resolution) and 3D PET activity map, which was subsequently interpolated to the DCE-MR resolution. The extended Tofts model was fitted to DCE-MR data, obtaining functional parametric maps related to perfusion such as the endothelial permeability (). Fifty-seven hepatic metastases were identified and analyzed. Quantitative evaluations of motion correction in PET images demonstrated average percentage increases of 16% ± 5% (mean ± SD) in Contrast (C), 18% ± 6% in SUVmean and 14% ± 2% in SUVmax, while DCE-MR and scored contrast-to-noise-ratio increases of 64% ± 3% and 90% ± 6%, respectively. Motion-corrected data visually showed improved image contrast of hepatic metastases and effectively reduced blurring and motion artefacts. Scatter plots of SUVmean versus suggested that the proposed framework improved differentiation of measurements. The presented motion correction framework for simultaneously acquired PET-DCE-MR data provides accurately aligned images with increased contrast of hepatic lesions allowing for improved detection and characterization.

Multi-Objective Evolutionary Algorithm for PET Image Reconstruction: Concept.

In many diagnostic imaging settings, including positron emission tomography (PET), images are typically used for multiple tasks such as detecting disease and quantifying disease. Unlike conventional image reconstruction that optimizes a single objective, this work proposes a multi-objective optimization algorithm for PET image reconstruction to identify a set of images that are optimal for more than one task. This work is reliant on a genetic algorithm to evolve a set of solutions that satisfies two distinct objectives. In this paper, we defined the objectives as the commonly used Poisson log-likelihood function, typically reflective of quantitative accuracy, and a variant of the generalized scan-statistic model, to reflect detection performance. The genetic algorithm uses new mutation and crossover operations at each iteration. After each iteration, the child population is selected with non-dominated sorting to identify the set of solutions along the dominant front or fronts. After multiple iterations, these fronts approach a single non-dominated optimal front, defined as the set of PET images for which none the objective function values can be improved without reducing the opposing objective function. This method was applied to simulated 2D PET data of the heart and liver with hot features. We compared this approach to conventional, single-objective approaches for trading off performance: maximum likelihood estimation with increasing explicit regularization and maximum a posteriori estimation with varying penalty strength. Results demonstrate that the proposed method generates solutions with comparable to improved objective function values compared to the conventional approaches for trading off performance amongst different tasks. In addition, this approach identifies a diverse set of solutions in the multi-objective function space which can be challenging to estimate with single-objective formulations.

AW-SDRLSE: Adaptive Weighting and Scalable Distance Regularized Level Set Evolution for Lymphoma Segmentation on PET Images.

Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymphoma boundaries on 2D PET slices. There are three important characteristics with respect to AW-SDRLSE: 1) A scalable distance regularization term is proposed and a parameter q can control the contour's convergence rate and precision in theory. 2) A novel dynamic annular mask is proposed to calculate mean intensities of local interior and exterior regions and further define the region energy term. 3) As the level set method is sensitive to parameters, we thus propose an adaptive weighting strategy for the length and area energy terms using local region intensity and boundary direction information. AW-SDRLSE is evaluated on 90 cases of real PET data with a mean Dice coefficient of 0.8796. Comparative results demonstrate the accuracy and robustness of AW-SDRLSE as well as its performance advantages as compared with related level set methods. In addition, experimental results indicate that AW-SDRLSE can be a fine segmentation method for improving the lymphoma segmentation results obtained by deep learning (DL) methods significantly.

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.

Penalized-Likelihood PET Image Reconstruction Using 3D Structural Convolutional Sparse Coding.

Positron emission tomography (PET) is widely used for clinical diagnosis. As PET suffers from low resolution and high noise, numerous efforts try to incorporate anatomical priors into PET image reconstruction, especially with the development of hybrid PET/CT and PET/MRI systems. In this work, we proposed a cube-based 3D structural convolutional sparse coding (CSC) concept for penalized-likelihood PET image reconstruction, named 3D PET-CSC. The proposed 3D PET-CSC takes advantage of the convolutional operation and manages to incorporate anatomical priors without the need of registration or supervised training. As 3D PET-CSC codes the whole 3D PET image, instead of patches, it alleviates the staircase artifacts commonly presented in traditional patch-based sparse coding methods. Compared with traditional coding methods in Fourier domain, the proposed method extends the 3D CSC to a straightforward approach based on the pursuit of localized cubes. Moreover, we developed the residual-image and order-subset mechanisms to further reduce the computational cost and accelerate the convergence for the proposed 3D PET-CSC method. Experiments based on computer simulations and clinical datasets demonstrate the superiority of 3D PET-CSC compared with other reference methods.

Evaluating attenuation correction strategies in a dedicated, single-gantry breast PET-tomosynthesis scanner

We are developing a dedicated, combined breast positron emission tomography (PET)-tomosynthesis scanner. Both the PET and digital breast tomosynthesis (DBT) scanners are integrated in a single gantry to provide spatially co-registered 3D PET-tomosynthesis images. The DBT image will be used to identify the breast boundary and breast density to improve the quantitative accuracy of the PET image. This paper explores PET attenuation correction (AC) strategies that can be performed with the combined breast PET-DBT scanner to obtain more accurate, quantitative high-resolution 3D PET images. The PET detector is comprised of a 32 × 32 array of 1.5 × 1.5 × 15 mm3 LYSO crystals. The PET scanner utilizes two detector heads separated by either 9 or 11 cm, with each detector head having a 4 × 2 arrangement of PET detectors. GEANT4 Application for Tomographic Emission simulations were performed using an anthropomorphic breast phantom with heterogeneous attenuation under clinical DBT-compression. FDG-avid lesions, each 5 mm in diameter with 8:1 uptake, were simulated at four locations within the breast. Simulations were performed with a scan time of 2 min. PET AC was performed using the actual breast simulation model as well as DBT reconstructed volumetric images to derive the breast outline. In addition to using the known breast density as defined by the breast model, we also modeled it as uniform patient-independent soft-tissue, and as a uniform patient-specific material derived from breast tissue composition. Measured absolute lesion uptake was used to evaluate the quantitative accuracy of performing AC using the various strategies. This study demonstrates that AC is necessary to obtain a closer estimate of the true lesion uptake and background activity in the breast. The DBT image dataset assists in measuring lesion uptake with low bias by facilitating accurate breast delineation as well as providing accurate information related to the breast tissue composition. While both the uniform soft-tissue and patient-specific material approaches provides a close estimate to the ground truth, <5% bias can be achieved by using a uniform patient-specific material to define the attenuation map.

A 3D attention residual encoder–decoder least-square GAN for low-count PET denoising

In this paper, to reduce patient scan times and maintain high image quality, we propose a 3D attention least-square (LS) generative adversarial network (GAN) to estimate positron emission tomography (PET) images with long scan times from short-scan-time images; this network is called 3D a-LSGAN. To explore the structural information between slices, a 3D network implementation is used. We take a low-count 3D PET image scanned for 75 s as the input and generate a high-count (HC) 3D PET image corresponding to an estimated scan time of 150 s. Specifically, a U-Net-like deep learning network is combined with a residual network and self-attention strategy to transfer the important information from the encoder part to the corresponding decoder part of the network. In addition, the mean square error (MSE) loss is added to the adversarial loss to form a new loss function that removes artifacts and yields high-quality PET images. The qualitative and quantitative experimental results show that the proposed 3D a-LSGAN method for low-count PET image noise reduction performs better than the state-of-the-art methods considered.

11CO2 positron emission imaging reveals the in-situ gas concentration profile as function of time and position in opaque gas-solid contacting systems

The in situ analysis of industrial processes, mostly conducted in opaque equipment is difficult. Whereas previously the positron emission technique was successfully applied to study the flow and mixing in gas–solid and liquid–solid systems using radio-active tracer particles, research on imaging a radio- active tracer gas is scarce.The present paper demonstrates the use of a fully three-dimensional (3D) Positron Emission Tomography (PET) in imaging the adsorption of 11CO2 tracer gas, while validating the measurement by conventional exit gas analysis. It will be demonstrated that PET can be used to measure the kinetics of high-pressure CO2 adsorption in situ, including the essential breakthrough and mass transfer zone characteristics. Such high-pressure operation is characteristic of pre-combustion CO2 capturing processes. It is expected that this work will foster further studies of gas–solid systems of adsorption, gas–solid catalysis, gas–solid hydrodynamics, and processes where the gas–solid interaction is of primary importance.

Acceleration of PET-based industrial internal defect image reconstruction using FPGA

Positron emission tomography (PET) can be used to measure the internal defects of industrial parts. However, PET requires a long execution time of image reconstruction, which hinders its practical usage in industrial measurements. A novel parallel scheme based on field-programmable gate arrays (FPGAs) is proposed in this study to accelerate PET image reconstruction. A fast maximum-likelihood expectation–maximization iteration reconstruction algorithm with prior estimation is implemented on the FPGA. This method can achieve satisfactory PET images with limited iteration times. The resources in the FPGA are divided into several groups, and each group supports the image reconstruction for a single sinogram. Thus, several sinograms can be processed in parallel. Two internal defect detection experiments are conducted to apply the proposed method to industrial measurements. Results show that the inner structure can be detected, whereas the inner defects can be visualized. A group of 104 slice images is reconstructed in parallel on FPGAs, and the final 3D PET image of the inner defects is acquired in 10 s.