TiDE-Net: A time-guided dual-encoder ResUNet for Positron Emission Tomography (PET) image denoising.

Small-animal positron emission tomography as a tool for neuropharmacology

Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PETimages. The training datasets, including down-sampled standard-count PET images as the network input and high-count images as the desired network output, were derived from 27 whole-body PET datasets, which were acquired using 90-min dynamic scan. The down-sampled standard-count PET images were rebinned with matched noise level of 195 clinical static PET datasets, by matching the normalized standard derivation (NSTD) inside 3D liver region of interests (ROIs). Cross-validation was performed on 27 PET datasets. Normalized mean square error (NMSE), peak signal to noise ratio (PSNR), structural similarity index (SSIM), and standard uptake value (SUV) bias of lesions were used for evaluation on standard-count and VHC PET images, with real-high-count PET image of 90 min as the gold standard. In addition, the network trained with 27 dynamic PET datasets was applied to 195 clinical static datasets to obtain VHC PET images. The NSTD and mean/max SUV of hypermetabolic lesions in standard-count and VHC PET images were evaluated. Three experienced nuclear medicine physicians evaluated the overall image quality of randomly selected 50 out of 195 patients' standard-count and VHC images and conducted 5-score ranking. A Wilcoxon signed-rank test was used to compare differences in the grading of standard-count and VHCimages. The cross-validation results showed that VHC PET images had improved quantitative metrics scores than the standard-count PET images. The mean/max SUVs of 35 lesions in the standard-count and true-high-count PET images did not show significantly statistical difference. Similarly, the mean/max SUVs of VHC and true-high-count PET images did not show significantly statistical difference. For the 195 clinical data, the VHC PET images had a significantly lower NSTD than the standard-count images. The mean/max SUVs of 215 hypermetabolic lesions in the VHC and standard-count images showed no statistically significant difference. In the image quality evaluation by three experienced nuclear medicine physicians, standard-count images and VHC images received scores with mean and standard deviation of 3.34±0.80 and 4.26 ± 0.72 from Physician 1, 3.02 ± 0.87 and 3.96 ± 0.73 from Physician 2, and 3.74 ± 1.10 and 4.58 ± 0.57 from Physician 3, respectively. The VHC images were consistently ranked higher than the standard-count images. The Wilcoxon signed-rank test also indicated that the image quality evaluation between standard-count and VHC images had significantdifference. A DL method was proposed to convert the standard-count images to the VHC images. The VHC images had reduced noise level. No significant difference in mean/max SUV to the standard-count images was observed. VHC images improved image quality for better lesion detectability and clinicaldiagnosis.

Respiratory motion blurs positron emission tomography (PET) images in thorax and abdominal regions, and can cause attenuation-related errors in quantitative parameters. In addition, this motion can cause localization error and disappearance of tumor(s) near the diaphragm. Respiratory gated PET imaging, or 4D PET imaging, is an attractive solution. For 4D PET, a surrogate measurement of the patient's breathing is made during the scan, and based on this measurement the list-mode PET data are sorted and reconstructed into multiple images. In several studies, it has been reported that 4D PET imaging recovers tumor volume more accurately than 3D PET imaging, and yields more accurate standard uptake values. In spite of its promise, 4D PET images suffer from statistical noise because only a fraction of the acquired data can be used in each image. Moreover, they also suffer from inconsistent attenuation correction due to mismatches between PET and CT images. In this paper, we propose a novel framework for phased attenuation correction and respiratory motion compensation for a 3D PET image by using a 3D CT image and multiple respiratory-phase 3D MR images. For phased attenuation correction, we generate and utilize 4D CT image obtained via non-rigid registrations among a 3D CT image and multiple respiratory-phase 3D MR images. A 3D PET image at a selected respiratory phase is then obtained by adding attenuation corrected multiple 3D PET images through non-rigid transformation. Experimental result for a clinical dataset shows that the proposed algorithm can provide much clearer organ boundary with improved quality than the conventional method in a 3D PET image.

Positron Emission Tomography—the Promise of Metabolic Imaging

Yittrium-90 ((90)Y) is traditionally thought of as a pure beta emitter, and is used in targeted radionuclide therapy, with imaging performed using bremsstrahlung single-photon emission computed tomography (SPECT). However, because (90)Y also emits positrons through internal pair production with a very small branching ratio, positron emission tomography (PET) imaging is also available. Because of the insufficient image quality of (90)Y bremsstrahlung SPECT, PET imaging has been suggested as an alternative. In this paper, the authors present the Monte Carlo-based simulation-reconstruction framework for (90)Y to comprehensively analyze the PET and SPECT imaging techniques and to quantitatively consider the disadvantages associated with them. Our PET and SPECT simulation modules were developed using Monte Carlo simulation of Electrons and Photons (MCEP), developed by Dr. S. Uehara. PET code (MCEP-PET) generates a sinogram, and reconstructs the tomography image using a time-of-flight ordered subset expectation maximization (TOF-OSEM) algorithm with attenuation compensation. To evaluate MCEP-PET, simulated results of (18)F PET imaging were compared with the experimental results. The results confirmed that MCEP-PET can simulate the experimental results very well. The SPECT code (MCEP-SPECT) models the collimator and NaI detector system, and generates the projection images and projection data. To save the computational time, the authors adopt the prerecorded (90)Y bremsstrahlung photon data calculated by MCEP. The projection data are also reconstructed using the OSEM algorithm. The authors simulated PET and SPECT images of a water phantom containing six hot spheres filled with different concentrations of (90)Y without background activity. The amount of activity was 163 MBq, with an acquisition time of 40 min. The simulated (90)Y-PET image accurately simulated the experimental results. PET image is visually superior to SPECT image because of the low background noise. The simulation reveals that the detected photon number in SPECT is comparable to that of PET, but the large fraction (approximately 75%) of scattered and penetration photons contaminates SPECT image. The lower limit of (90)Y detection in SPECT image was approximately 200 kBq/ml, while that in PET image was approximately 100 kBq/ml. By comparing the background noise level and the image concentration profile of both the techniques, PET image quality was determined to be superior to that of bremsstrahlung SPECT. The developed simulation codes will be very useful in the future investigations of PET and bremsstrahlung SPECT imaging of (90)Y.

Positron emission tomography (PET) is a nuclear medical imaging technology that produces 3D images reflecting tissue metabolic activity in human body. PET has been widely used in various clinical applications, such as in diagnosis of brain disorders. High-quality PET images play an essential role in diagnosing brain diseases/disorders. In practice, in order to obtain high-quality PET images, a standard-dose radionuclide (tracer) needs to be used and injected into a living body. As a result, it will inevitably increase the patient's exposure to radiation. One solution to solve this problem is predicting standard-dose PET images using low-dose PET images. As yet, no previous studies with this approach have been reported. Accordingly, in this paper, the authors propose a regression forest based framework for predicting a standard-dose brain [(18)F]FDG PET image by using a low-dose brain [(18)F]FDG PET image and its corresponding magnetic resonance imaging (MRI) image. The authors employ a regression forest for predicting the standard-dose brain [(18)F]FDG PET image by low-dose brain [(18)F]FDG PET and MRI images. Specifically, the proposed method consists of two main steps. First, based on the segmented brain tissues (i.e., cerebrospinal fluid, gray matter, and white matter) in the MRI image, the authors extract features for each patch in the brain image from both low-dose PET and MRI images to build tissue-specific models that can be used to initially predict standard-dose brain [(18)F]FDG PET images. Second, an iterative refinement strategy, via estimating the predicted image difference, is used to further improve the prediction accuracy. The authors evaluated their algorithm on a brain dataset, consisting of 11 subjects with MRI, low-dose PET, and standard-dose PET images, using leave-one-out cross-validations. The proposed algorithm gives promising results with well-estimated standard-dose brain [(18)F]FDG PET image and substantially enhanced image quality of low-dose brain [(18)F]FDG PET image. In this paper, the authors propose a framework to generate standard-dose brain [(18)F]FDG PET image using low-dose brain [(18)F]FDG PET and MRI images. Both the visual and quantitative results indicate that the standard-dose brain [(18)F]FDG PET can be well-predicted using MRI and low-dose brain [(18)F]FDG PET.

Germanium-68 based attenuation correction (PET(Ge68)) is performed in positron emission tomography (PET) imaging for quantitative measurements. With the recent introduction of combined in-line PET/CT scanners, CT data can be used for attenuation correction. Since dental implants can cause artefacts in CT images, CT-based attenuation correction (PET(CT)) may induce artefacts in PET images. The purpose of this study was to evaluate the influence of dental metallic artwork on the quality of PET images by comparing non-corrected images and images attenuation corrected by PET(Ge68) and PET(CT). Imaging was performed on a novel in-line PET/CT system using a 40-mAs scan for PET(CT) in 41 consecutive patients with high suspicion of malignant or inflammatory disease. In 17 patients, additional PET(Ge68) images were acquired in the same imaging session. Visual analysis of fluorine-18 fluorodeoxyglucose (FDG) distribution in several regions of the head and neck was scored on a 4-point scale in comparison with normal grey matter of the brain in the corresponding PET images. In addition, artefacts adjacent to dental metallic artwork were evaluated. A significant difference in image quality scoring was found only for the lips and the tip of the nose, which appeared darker on non-corrected than on corrected PET images. In 33 patients, artefacts were seen on CT, and in 28 of these patients, artefacts were also seen on PET imaging. In eight patients without implants, artefacts were seen neither on CT nor on PET images. Direct comparison of PET(Ge68) and PET(CT) images showed a different appearance of artefacts in 3 of 17 patients. Malignant lesions were equally well visible using both transmission correction methods. Dental implants, non-removable bridgework etc. can cause artefacts in attenuation-corrected images using either a conventional 68Ge transmission source or the CT scan obtained with a combined PET/CT camera. We recommend that the non-attenuation-corrected PET images also be evaluated in patients undergoing PET of the head and neck.

Positron emission tomography (PET) is a vital tool for molecular-level imaging but is limited by high radiation exposure, prompting the development of low-dose PET techniques. However, low-dose PET images often exhibit increased noise, which reduces their diagnostic utility. Magnetic resonance (MR) imaging, renowned for its superior soft tissue contrast and absence of ionizing radiation, offers potential for enhancing low-dose PET quality. Deep learning methods, particularly convolutional neural networks (CNNs) and transformers, have shown promise in improving image quality through multi-modal data integration, yet challenges remain in effectively leveraging MR for PET denoising. To improve image quality of low-dose PET while minimizing radiotracer dosage by employing deep learning methods in conjunction with MRI. In this retrospective study, PET/MR data from 59 patients were used for training and validation. Merge Net, which utilizes a Unet++ architecture enhanced with a self-attention mechanism and takes registered low-dose PET and MR images as input, was used to synthesize full-dose PET images. To evaluate the contribution of MR images to PET denoising, we conducted the following comparative experiments: the PET_MR model used paired low-dose PET and MR images as input, while the PET_only model relied solely on low-dose PET images. Both models shared the same network architecture to ensure a fair comparison. Image quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) relative to full-dose PET images. Two readers scored subjective image quality using a 5-point Likert scale (5=excellent, 1=very poor). Statistical analyses were carried out to assess differences in image quality metrics and the detection of lesions. Compared with the low-dose images, the enhanced images, particularly those from the PET_MR model, demonstrated significant improvements across all quality metrics. Quantitative results from 12 test cases demonstrated high similarity between the enhanced low-dose PET images and the full-dose PET images (PSNR=33.9, SSIM=0.87, RMSE=2.08 for the PET_MR model; PSNR=33.43, SSIM=0.86, RMSE=2.18 for the PET_only model). Visually, the PET_MR model demonstrated clearer tissue boundaries and reduced noise levels compared with the PET_only model. All PET_MR images received scores of 3 or higher, comparable to those of the full-dose images. The PET_MR model significantly enhanced image quality, offering clearer tissue boundaries and reduced noise.

Bimodal MR–PET Agent for Quantitative pH Imaging

Atlas-guided non-uniform attenuation correction in cerebral 3D PET imaging

Simulation of positron emission tomography (PET) images is critical in dynamic imaging protocol optimization, quantitative imaging metric development, deep learning applications, and virtual imaging trials. These applications rely heavily on large volumes of simulated PET data. However, the current state-of-the-art PET image simulation platform is time-prohibitive and computationally intensive. Although deep learning-based generative models have been widely applied to generate PET images, they often fail to adequately account for the differing acquisition times of PET images. This study seeks to develop and validate a novel deep learning-based method, the noise-aware system generative model (NASGM), to simulate PET images of different acquisition times. NASGM is based on the conditional generative adversarial network and features a novel dual-domain discriminator that contains a spatial and a frequency branch to leverage information from both domains. A transformer-based structure is applied for the frequency discriminator because of its ability to encode positional information and capture global dependencies. The study is conducted on a simulated dataset, with a public PET/CT dataset as the input activity and attenuation maps, and an analytical PET simulation tool to simulate PET images of different acquisition times. Ablation studies are carried out to confirm the necessity of adopting the dual-domain discriminator. A comprehensive suite of evaluations, including image fidelity assessment, noise measurement, quantitative accuracy validation, task-based assessment, texture analysis, and human observer study, is performed to confirm the realism of generated images. The Wilcoxon signed-rank test with Bonferroni correction is applied to compare the NASGM with other networks in the ablation study at an adjusted p-value , and the alignment of features between the generated and target images is measured by the concordance correlation coefficient (CCC). The quantitative accuracy measured by the correlation of mean recovery coefficients of tumor groups, and the NASGM-generated images achieved CCC values of 0.95 across most of the acquisition times. This also illustrates NASGM's ability to replicate the partial volume effect in target images. Furthermore, NASGM was also demonstrated to generate images that exhibit noise characteristics and textures closely matching those in the target PET images. In atumor detection task-based observer study, the synthesized images achieved comparable performance to target images in the clinically relevant task. In the two-alternative forced choice human observer study, human observers achieved an accuracy of ∼50% for all tested acquisition times, confirming that the synthesized and target images are visually indistinguishable for human observers. Moreover, NASGM displayed strong generalizability within the training range, successfully generating images of frame durations not included in the training dataset. NASGM is developed and validated as a deep learning-based PET simulation framework that offers computationally efficient image generation compared to traditional methods, making it an ideal tool for producing large volumes of simulated PET image datasets across varying acquisition times. Furthermore, the dual-domain discriminator enhances the quality of generated images, while the noise-aware mechanism introduces realistic, controllable noise variability.

The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult to widely use. Therefore, we attempt to utilize high-quality images generated by uEXPLORER to optimize the quality of images from short-axis PET scanners through deep learning technology while controlling costs. The experiments were conducted using PET images of three anatomical locations (brain, lung, and abdomen) from 335 patients. To simulate PET images from different axes, two protocols were used to obtain PET image pairs (each patient was scanned once). For low-quality PET (LQ-PET) images with a 320-mm AFOV, we applied a 300-mm FOV for brain reconstruction and a 500-mm FOV for lung and abdomen reconstruction. For high-quality PET (HQ-PET) images, we applied a 1940-mm AFOV during the reconstruction process. A 3D Unet was utilized to learn the mapping relationship between LQ-PET and HQ-PET images. In addition, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed to evaluate the model performance. Furthermore, two nuclear medicine doctors evaluated the image quality based on clinical readings. The generated PET images of the brain, lung, and abdomen were quantitatively and qualitatively compatible with the HQ-PET images. In particular, our method achieved PSNR values of 35.41 ± 5.45dB (p < 0.05), 33.77 ± 6.18dB (p < 0.05), and 38.58 ± 7.28dB (p < 0.05) for the three beds. The overall mean SSIM was greater than 0.94 for all patients who underwent testing. Moreover, the total subjective quality levels of the generated PET images for three beds were 3.74 ± 0.74, 3.69 ± 0.81, and 3.42 ± 0.99 (the highest possible score was 5, and the minimum score was 1) from two experienced nuclear medicine experts. Additionally, we evaluated the distribution of quantitative standard uptake values (SUV) in the region of interest (ROI). Both the SUV distribution and the peaks of the profile show that our results are consistent with the HQ-PET images, proving the superiority of our approach. The findings demonstrate the potential of the proposed technique for improving the image quality of a PET scanner with a 320mm or even shorter AFOV. Furthermore, this study explored the potential of utilizing uEXPLORER to achieve improved short-axis PET image quality at a limited economic cost, and computer-aided diagnosis systems that are related can help patients and radiologists.

Background Infectious diseases are the second leading cause of death worldwide. In order to better understand and treat them, an accurate evaluation using multi-modal imaging techniques for anatomical and functional characterizations is needed. For non-invasive imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), there have been many engineering improvements that have significantly enhanced the resolution and contrast of the images, but there are still insufficient computational algorithms available for researchers to use when accurately quantifying imaging data from anatomical structures and functional biological processes. Since the development of such tools may potentially translate basic research into the clinic, this study focuses on the development of a quantitative and qualitative image analysis platform that provides a computational radiology perspective for pulmonary infections in small animal models. Specifically, we designed (a) a fast and robust automated and semi-automated image analysis platform and a quantification tool that can facilitate accurate diagnostic measurements of pulmonary lesions as well as volumetric measurements of anatomical structures, and incorporated (b) an image registration pipeline to our proposed framework for volumetric comparison of serial scans. This is an important investigational tool for small animal infectious disease models that can help advance researchers’ understanding of infectious diseases.

Positron Emission Tomography Imaging of Adenoviral-Mediated Transgene Expression in Liver Cancer Patients

Positron emission tomography (PET) imaging has moved forward the development of medical diagnostics and research across various domains, including cardiology, neurology, infection detection, and oncology. The integration of machine learning (ML) algorithms into PET data analysis has further enhanced their capabilities of including disease diagnosis and classification, image segmentation, and quantitative analysis. ML algorithms empower researchers and clinicians to extract valuable insights from complex big PET datasets, which enabling automated pattern recognition, predictive health outcome modeling, and more efficient data analysis. This review explains the basic knowledge of PET imaging, statistical methods for PET image analysis, and challenges of PET data analysis. We also discussed the improvement of analysis capabilities by combining PET data with machine learning algorithms and the application of this combination in various aspects of PET image research. This review also highlights current trends and future directions in PET imaging, emphasizing the driving and critical role of machine learning and big PET image data analytics in improving diagnostic accuracy and personalized medical approaches. Integration between PET imaging will shape the future of medical diagnosis and research.