Positron Emission Tomography Image Reconstruction Research Articles

PET image reconstruction using weighted nuclear norm maximization and deep learning prior

The ill-posed Positron emission tomography (PET) reconstruction problem usually results in limited resolution and significant noise. Recently, deep neural networks have been incorporated into PET iterative reconstruction framework to improve the image quality. In this paper, we propose a new neural network-based iterative reconstruction method by using weighted nuclear norm (WNN) maximization, which aims to recover the image details in the reconstruction process. The novelty of our method is the application of WNN maximization rather than WNN minimization in PET image reconstruction. Meanwhile, a neural network is used to control the noise originated from WNN maximization. Our method is evaluated on simulated and clinical datasets. The simulation results show that the proposed approach outperforms state-of-the-art neural network-based iterative methods by achieving the best contrast/noise tradeoff with a remarkable contrast improvement on the lesion contrast recovery. The study on clinical datasets also demonstrates that our method can recover lesions of different sizes while suppressing noise in various low-dose PET image reconstruction tasks. Our code is available athttps://github.com/Kuangxd/PETReconstruction.

Quantitative analysis of the effect of the magnetic field generated by a PET/MR scanner on positron range

Image quality in positron emission tomography (PET) is influenced by positron range. In this work, the effect of the magnetic field of a PET/MR Siemens Biograph mMR 3T on the quality of PET images was studied. Experimental measurements were conducted using18F and68Ga-filled phantoms to quantify image uniformity, recovery coefficients (RCs), spill-over ratios and percent contrast for spherical lesions. Tissue-equivalent phantoms (lung inhale and exhale, adipose, water, trabecular and cortical bone) were used together with a line source to quantify the impact of the magnetic field on the reconstructed PET images. A comparative analysis was made with images obtained with a PET/CT Biograph Vision 600, using the same radionuclides and phantoms. Higher RCs values were obtained when the image quality phantom was filled with68Ga and scanned with the PET/MR system compared to those obtained with the PET/CT scanner. Hot spheres in the lesion detectability phantom, appear contracted in the transverse direction in the PET/MR system, an effect more evident for68Ga compared to18F, but no elongation in the direction parallel to the magnetic field was observed. In the PET/CT scanner, radial profiles taken from axial slices of line sources, show longer distribution tails extending beyond 20 mm when filled with68Ga and placed inside lung-inhale tissue. In the PET/MR scanner the radial profiles of all materials collapsed into a single distribution with tails extending no more than 10 mm in the direction perpendicular to the magnetic field. Positron range depends on positron energy and material density in which they traverse. The results show an evident improvement in image quality in the transaxial direction only, particularly in phantoms filled with68Ga when using a PET/MR system as opposed to images acquired in the PET/CT system due to the presence of the magnetic field.

Two-step optimization for accelerating deep image prior-based PET image reconstruction.

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction.

Exact parameter identification in PET pharmacokinetic modeling using the irreversible two tissue compartment model * *This work was partly funded by the NIH Grants P41-EB017183 and R01-EB031199-02.

Objective. In quantitative dynamic positron emission tomography (PET), time series of images, reflecting the tissue response to the arterial tracer supply, are reconstructed. This response is described by kinetic parameters, which are commonly determined on basis of the tracer concentration in tissue and the arterial input function. In clinical routine the latter is estimated by arterial blood sampling and analysis, which is a challenging process and thus, attempted to be derived directly from reconstructed PET images. However, a mathematical analysis about the necessity of measurements of the common arterial whole blood activity concentration, and the concentration of free non-metabolized tracer in the arterial plasma, for a successful kinetic parameter identification does not exist. Here we aim to address this problem mathematically. Approach. We consider the identification problem in simultaneous pharmacokinetic modeling of multiple regions of interests of dynamic PET data using the irreversible two-tissue compartment model analytically. In addition to this consideration, the situation of noisy measurements is addressed using Tikhonov regularization. Furthermore, numerical simulations with a regularization approach are carried out to illustrate the analytical results in a synthetic application example.Main results. We provide mathematical proofs showing that, under reasonable assumptions, all metabolic tissue parameters can be uniquely identified without requiring additional blood samples to measure the arterial input function. A connection to noisy measurement data is made via a consistency result, showing that exact reconstruction of the ground-truth tissue parameters is stably maintained in the vanishing noise limit. Furthermore, our numerical experiments suggest that an approximate reconstruction of kinetic parameters according to our analytic results is also possible in practice for moderate noise levels. Significance. The analytical result, which holds in the idealized, noiseless scenario, suggests that for irreversible tracers, fully quantitative dynamic PET imaging is in principle possible without costly arterial blood sampling and metabolite analysis.

Deep Residual-in-Residual Model-Based PET Image Super-Resolution with Motion Blur

Positron emission tomography (PET) is a non-invasive molecular imaging technique. The limited spatial resolution of PET images, due to technological and physical imaging constraints, directly affects the precise localization and interpretation of small lesions and biological processes. The super-resolution (SR) technique aims to enhance image quality by improving spatial resolution, thereby aiding clinicians in achieving more accurate diagnoses. However, most conventional SR methods rely on idealized degradation models and fail to effectively capture both low- and high-frequency information present in medical images. For the challenging SR reconstruction of PET images exhibiting motion-induced artefacts, a degradation model that better aligns with practical scanning scenarios was designed by us. Furthermore, we proposed a PET image SR method based on the deep residual-in-residual network (DRRN), focusing on the recovery of both low- and high-frequency information. By incorporating multi-level residual connections, our approach facilitates direct feature propagation across different network levels. This design effectively mitigates the lack of feature correlation between adjacent convolutional layers in deep networks. Our proposed method surpasses benchmark methods in both full-reference and no-reference metrics and subjective visual effects across small animal PET (SAPET), phantoms, and Alzheimer’s Disease Neuroimaging Initiative (ADNI) datasets. The experimental findings confirm the remarkable efficacy of DRRN in enhancing spatial resolution and mitigating blurring in PET images. In comparison to conventional SR techniques, this method demonstrates superior proficiency in restoring low-frequency structural texture information while simultaneously maintaining high-frequency details, thus showcasing exceptional multi-frequency information fusion capabilities.

Anatomically Guided PET Image Reconstruction Using Conditional Weakly-Supervised Multi-Task Learning Integrating Self-Attention.

To address the lack of high-quality training labels in positron emission tomography (PET) imaging, weakly-supervised reconstruction methods that generate network-based mappings between prior images and noisy targets have been developed. However, the learned model has an intrinsic variance proportional to the average variance of the target image. To suppress noise and improve the accuracy and generalizability of the learned model, we propose a conditional weakly-supervised multi-task learning (MTL) strategy, in which an auxiliary task is introduced serving as an anatomical regularizer for the PET reconstruction main task. In the proposed MTL approach, we devise a novel multi-channel self-attention (MCSA) module that helps learn an optimal combination of shared and task-specific features by capturing both local and global channel-spatial dependencies. The proposed reconstruction method was evaluated on NEMA phantom PET datasets acquired at different positions in a PET/CT scanner and 26 clinical whole-body PET datasets. The phantom results demonstrate that our method outperforms state-of-the-art learning-free and weakly-supervised approaches obtaining the best noise/contrast tradeoff with a significant noise reduction of approximately 50.0% relative to the maximum likelihood (ML) reconstruction. The patient study results demonstrate that our method achieves the largest noise reductions of 67.3% and 35.5% in the liver and lung, respectively, as well as consistently small biases in 8 tumors with various volumes and intensities. In addition, network visualization reveals that adding the auxiliary task introduces more anatomical information into PET reconstruction than adding only the anatomical loss, and the developed MCSA can abstract features and retain PET image details.

Bibliometric Review of Optimization and Image Processing of Positron Emission Tomography (PET) Imaging System between 1981-2022.

PET scan stands as a valuable diagnostic tool in nuclear medicine, enabling the observation of metabolic and physiological changes at a molecular level. However, PET scans have a number of drawbacks, such as poor spatial resolution, noisy images, scattered radiation, artifacts, and radiation exposure. These challenges demonstrate the need for optimization in image processing techniques. Our objective is to identify the evolving trends and impacts of publication in this field, as well as the most productive and influential countries, institutions, authors, themes, and articles. A bibliometric study was conducted using a comprehensive query string such as "positron emission tomography" AND "image processing" AND optimization to retrieve 1,783 publications from 1981 to 2022 found in the Scopus database related to this field of study. The findings revealed that the most influential country, institution, and authors are from the USA, and the most prevalent theme is TOF PET image reconstruction. The increasing trend in publication in the field of optimization of image processing in PET scans would address the challenges in PET scan by reducing radiation exposure, faster scanning speed, as well as enhancing lesion identification.

Enhanced PET image reconstruction utilizing morphological filtering and MLEM algorithm

Positron Emission Tomography (PET) image reconstruction remains a pivotal area in PET technology, critically influencing clinical diagnostic outcomes. Addressing the need for enhanced image quality, this study introduces a novel algorithm for PET image reconstruction. This algorithm integrates a penalty mechanism, morphological filtering, and the Maximum Likelihood Expectation Maximization (MLEM) algorithm, aiming to improve reconstructed image quality significantly. The operational process of the algorithm within each iteration encompasses two primary phases. Initially, image reconstruction is accomplished via the MLEM algorithm, followed by the application of a morphological filter to attenuate noise in the reconstructed image. Simulation experiments demonstrate that this algorithm effectively mitigates noise while preserving crucial details, such as image edges. Notably, this method presents the dual benefits of straightforward parameter configuration and ease of implementation. The results indicate a substantial enhancement in noise suppression and fine structure preservation in the reconstructed images, marking a significant advancement in PET image reconstruction techniques.

ReconU-Net: a direct PET image reconstruction using U-Net architecture with back projection-induced skip connection

Objective. This study aims to introduce a novel back projection-induced U-Net-shaped architecture, called ReconU-Net, based on the original U-Net architecture for deep learning-based direct positron emission tomography (PET) image reconstruction. Additionally, our objective is to visualize the behavior of direct PET image reconstruction by comparing the proposed ReconU-Net architecture with the original U-Net architecture and existing DeepPET encoder–decoder architecture without skip connections. Approach. The proposed ReconU-Net architecture uniquely integrates the physical model of the back projection operation into the skip connection. This distinctive feature facilitates the effective transfer of intrinsic spatial information from the input sinogram to the reconstructed image via an embedded physical model. The proposed ReconU-Net was trained using Monte Carlo simulation data from the Brainweb phantom and tested on both simulated and real Hoffman brain phantom data. Main results. The proposed ReconU-Net method provided better reconstructed image in terms of the peak signal-to-noise ratio and contrast recovery coefficient than the original U-Net and DeepPET methods. Further analysis shows that the proposed ReconU-Net architecture has the ability to transfer features of multiple resolutions, especially non-abstract high-resolution information, through skip connections. Unlike the U-Net and DeepPET methods, the proposed ReconU-Net successfully reconstructed the real Hoffman brain phantom, despite limited training on simulated data. Significance. The proposed ReconU-Net can improve the fidelity of direct PET image reconstruction, even with small training datasets, by leveraging the synergistic relationship between data-driven modeling and the physics model of the imaging process.

Anatomy-guided multi-resolution image reconstruction in PET

Objective. In this paper, we propose positron emission tomography image reconstruction using a multi-resolution triangular mesh. The mesh can be adapted based on patient specific anatomical information that can be in the form of a computed tomography or magnetic resonance imaging image in the hybrid imaging systems. The triangular mesh can be adapted to high resolution in localized anatomical regions of interest (ROI) and made coarser in other regions, leading to an imaging model with high resolution in the ROI with clearly reduced number of degrees of freedom compared to a conventional uniformly dense imaging model. Approach. We compare maximum likelihood expectation maximization reconstructions with the multi-resolution model to reconstructions using a uniformly dense mesh, a sparse mesh and regular rectangular pixel mesh. Two simulated cases are used in the comparison, with the first one using the NEMA image quality phantom and the second the XCAT human phantom. Main results. When compared to the results with the uniform imaging models, the locally refined multi-resolution mesh retains the accuracy of the dense mesh reconstruction in the ROI while being faster to compute than the reconstructions with the uniformly dense mesh. The locally dense multi-resolution model leads also to more accurate reconstruction than the pixel-based mesh or the sparse triangular mesh. Significance. The findings suggest that triangular multi-resolution mesh, which can be made patient and application specific, is a potential alternative for pixel-based reconstruction.

An iterative reconstruction algorithm for unsupervised PET image

Objective. In recent years, convolutional neural networks (CNNs) have shown great potential in positron emission tomography (PET) image reconstruction. However, most of them rely on many low-quality and high-quality reference PET image pairs for training, which are not always feasible in clinical practice. On the other hand, many works improve the quality of PET image reconstruction by adding explicit regularization or optimizing the network structure, which may lead to complex optimization problems. Approach. In this paper, we develop a novel iterative reconstruction algorithm by integrating the deep image prior (DIP) framework, which only needs the prior information (e.g. MRI) and sinogram data of patients. To be specific, we construct the objective function as a constrained optimization problem and utilize the existing PET image reconstruction packages to streamline calculations. Moreover, to further improve both the reconstruction quality and speed, we introduce the Nesterov’s acceleration part and the restart mechanism in each iteration. Main results. 2D experiments on PET data sets based on computer simulations and real patients demonstrate that our proposed algorithm can outperform existing MLEM-GF, KEM and DIPRecon methods. Significance. Unlike traditional CNN methods, the proposed algorithm does not rely on large data sets, but only leverages inter-patient information. Furthermore, we enhance reconstruction performance by optimizing the iterative algorithm. Notably, the proposed method does not require much modification of the basic algorithm, allowing for easy integration into standard implementations.

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 <emph>in-silico</emph> 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.

Deep Generalized Learning Model for PET Image Reconstruction.

Low-count positron emission tomography (PET) imaging is challenging because of the ill-posedness of this inverse problem. Previous studies have demonstrated that deep learning (DL) holds promise for achieving improved low-count PET image quality. However, almost all data-driven DL methods suffer from fine structure degradation and blurring effects after denoising. Incorporating DL into the traditional iterative optimization model can effectively improve its image quality and recover fine structures, but little research has considered the full relaxation of the model, resulting in the performance of this hybrid model not being sufficiently exploited. In this paper, we propose a learning framework that deeply integrates DL and an alternating direction of multipliers method (ADMM)-based iterative optimization model. The innovative feature of this method is that we break the inherent forms of the fidelity operators and use neural networks to process them. The regularization term is deeply generalized. The proposed method is evaluated on simulated data and real data. Both the qualitative and quantitative results show that our proposed neural network method can outperform partial operator expansion-based neural network methods, neural network denoising methods and traditional methods.

Prior information-guided reconstruction network for positron emission tomography images.

Deep learning has recently shown great potential in medical image reconstruction tasks. For positron emission tomography (PET) images, the direct reconstruction from raw data to radioactivity images using deep learning without any constraint may lead to the production of nonexistent structures. The aim of this study was to specifically develop and test a flexibly deep learning-based reconstruction network guided by any form of prior knowledge to achieve high quality and high reliability reconstruction. We developed a novel prior information-guided reconstruction network (PIGRN) with a dual-channel generator and a 2-scale discriminator based on a conditional generative adversarial network (cGAN). Besides the raw data channel, an additional channel is provided in the generator for prior information (PI) to guide the training phase. The PI can be reconstructed images obtained via conventional methods, nuclear medical images from other modalities, attenuation correction maps from time-of-flight-PET (TOF-PET) data, or any other physical parameters. For this study, the reconstructed images generated by filtered back projection (FBP) were chosen as the input of the additional channel. To improve the image quality, a 2-scale discriminator was adopted which can focus on both the coarse and fine field of the reconstruction images. Experiments were carried out on both a simulation dataset and a real Sprague Dawley (SD) rat dataset. Two classic deep learning-based reconstruction networks, including U-Net and Deep-PET, were compared in our study. Compared with these two methods, our method could provide much higher quality PET image reconstruction in the study of the simulation dataset. The peak signal-to-noise ratio (PSNR) value reached 31.8498, and the structure similarity index measure (SSIM) value reached 0.9754. The real study on SD rats indicated that the proposed network also has strong generalization ability. The flexible PIGRN based on cGAN for PET images combines both raw data and PI. The results of comparison experiments and a generalization experiment based on simulation and SD rat datasets demonstrated that the proposed PIGRN has the ability to improve image quality and has strong generalization ability.

Threshold for defining PSMA-positivity prior to 177Lu-PSMA therapy: a comparison of [68Ga]Ga-PSMA-11 and [18F]F-DCFPyL in metastatic prostate cancer

BackgroundIn 2022, the American Food and Drug Administration and the European Medicines Agency approved [177Lu]Lu-PSMA-617 (PLUVICTO™, Novartis AG, Basel, Switzerland) for radionuclide therapy with prostate-specific membrane antigen (PSMA) ligands in metastatic prostate cancer. Theranostics require appropriate patients to be identified by positron emission tomography (PET) prior to radionuclide therapy, usually employing [68Ga]Ga-PSMA-11. Alternatively, several 18F-labelled PSMA-PET tracers are available and may increasingly replace 68Ga-labelled compounds, with respect to their image quality, availability and other practical advantages. However, alternative tracers may differ in uptake behaviour, and their comparability with regard to patient selection for [177Lu]Lu-PSMA therapy has not yet been established. Here, we analysed whether tumour-to-background ratios determined by PET using the 18F-labelled PSMA-specific radiopharmaceutical [18F]F-DCFPyL were comparable to those determined by PET using [68Ga]Ga-PSMA-11.ResultsNo differences could be observed between [68Ga]Ga-PSMA-11-PET and [18F]F-DCFPyL-PET regarding tumour-to-liver ratios or tumour-to-mediastinum ratios (e. g. tumour-to-liver ratios using maximum SUV of the tumour lesion for ultra-high definition reconstructed PET images with a median of 2.5 (0.6–9.0) on [68Ga]Ga-PSMA-11-PET vs. 2,0 (0.6–11.4) on [18F]F-DCFPyL-PET). However, significant differences were observed in terms of contrast-to-noise ratios, thereby demonstrating the better image quality obtained with [18F]F-DCFPyL-PET.ConclusionsOur data showed that [18F]F-DCFPyl-PET and [68Ga]Ga-PSMA-11-PET provide comparable tumour-to-liver and tumour-to-mediastinum ratios. Therefore, a tumour uptake of [18F]F-DCFPyL above the liver background, like using [68Ga]Ga-PSMA-11, can be considered as equally suitable for defining PSMA-positivity by a semiquantitative assessment based on the liver background, e. g. prior to radioligand therapy with 177Lu-labelled PSMA ligands. In addition, our data suggest a tending advantage of [18F]F-DCFPyL in terms of lesion detectability.

AI for PET image reconstruction.

Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spatial resolution have remained issues in PET imaging, and state-of-the-art PET reconstruction has started to exploit other medical imaging modalities (such as MRI) to assist in noise reduction and enhancement of PET's spatial resolution. Nonetheless, there is an ongoing drive towards not only improving image quality, but also reducing the injected radiation dose and reducing scanning times. While the arrival of new PET scanners (such as total body PET) is helping, there is always a need to improve reconstructed image quality due to the time and count limited imaging conditions. Artificial intelligence (AI) methods are now at the frontier of research for PET image reconstruction. While AI can learn the imaging physics as well as the noise in the data (when given sufficient examples), one of the most common uses of AI arises from exploiting databases of high-quality reference examples, to provide advanced noise compensation and resolution recovery. There are three main AI reconstruction approaches: (i) direct data-driven AI methods which rely on supervised learning from reference data, (ii) iterative (unrolled) methods which combine our physics and statistical models with AI learning from data, and (iii) methods which exploit AI with our known models, but crucially can offer benefits even in the absence of any example training data whatsoever. This article reviews these methods, considering opportunities and challenges of AI for PET reconstruction.

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.

Low-count PET image reconstruction based on truncated inverse radon layer and U-shaped network

Objective. Positron emission tomography (PET) is a functional imaging widely used in various applications such as tumour detection. PET image reconstruction is an ill-posed inverse problem, and the model-based iterative reconstruction methods commonly used in clinical practice have disadvantages such as long time consumption and low signal-to-noise ratio, especially at low doses. Approach. In this study, we propose a deep learning-based reconstruction method that is capable of reconstructing images directly from low-count sinograms. Our network consists of two parts, a truncated inverse radon layer for implementing domain transform and a U-shaped network for image enhancement. Main result. We validated our method on both simulation data and real data. Compared to ordered subset expectation maximization with a post-Guassian filter, the structural similarity can be improved from 0.9357 to 0.9613 and the peak signal-to-noise ratio can be improved by 5 dB. Significance. The proposed method can directly convert low-count sinograms into PET images, while obtaining improved image quality and having less time consumption than iterative reconstruction algorithms and the state-of-the-art convolutional neural network.

Background reduction in PET by double Compton scattering of quantum entangled annihilation photons

The reduction of background events, such as random and scatter events, can improve positron emission tomography (PET) images quantitatively. The quantum entanglement property of annihilation photons has been theoretically studied and experimentally validated. Recently, simulation studies on PET image reconstruction using the quantum entanglement property of annihilation photons and studies on the measurement of entanglement and polarization properties with scintillation crystal arrays demonstrated that PET images with less background can be achieved. This study utilized the quantum entanglement property of annihilation photons in a typical PET system with block detectors. A data acquisition method for quantum entanglement PET (QEPET) and system response modelling for QEPET image reconstruction were proposed and implemented. An experimental study was conducted using a small animal PET system composed of Ce:GAGG scintillation crystals whose dimensions are 2.5 × 2.5 × 4mm3. The diameter of the system was 79.3 mm. Signals from all individual crystals were processed parallelly and independently; thus, Compton scatter and absorption crystals within a module were obtained directly. The 28 MBq experiment result revealed the random background ratio of QEPET was approximately 20% lower than that of PET and the count rate of QEPET was approximately 0.1% that of PET. Evaluation on reconstructed images from the 28 MBq experiment revealed that the signal-to-background ratio of QEPET was approximately two times better than that of PET while the signal-to-noise ratio of QEPET was about 77% of that of PET. The method proposed in this study is expected to improve PET images with high background, such as those with a high activity and high background gamma rays and improve range verification in proton therapy.

MRI tractography-guided PET image reconstruction regularisation using connectome-based nonlocal means filtering

Positron emission tomography (PET) molecular biomarkers and diffusion magnetic resonance imaging (dMRI) derived information show associations and highly complementary information in a number of neurodegenerative conditions, including Alzheimer’s disease. Diffusion MRI provides valuable information about the microstructure and structural connectivity (SC) of the brain which could guide and improve the PET image reconstruction when such associations exist. However, this potental has not been previously explored. In the present study, we propose a CONNectome-based non-local means one-atep late maximum a posteriori (CONN-NLM-OSLMAP) method, which allows diffusion MRI-derived connectivity information to be incorporated into the PET iterative image reconstruction process, thus regularising the estimated PET images. The proposed method was evaluated using a realistic tau-PET/MRI simulated phantom, demonstrating more effective noise reduction and lesion contrast improvement, as well as the lowest overall bias compared with both a median filter applied as an alternative regulariser and CONNectome-based non-local means as a post-reconstruction filter. By adding complementary SC information from diffusion MRI, the proposed regularisation method offers more useful and targeted denoising and regularisation, demonstrating the feasibility and effectiveness of integrating connectivity information into PET image reconstruction.