Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

Small-animal positron emission tomography as a tool for neuropharmacology

Multi-tracer positron emission tomography (PET), which assesses key neurological biomarkers such as tau pathology, neuroinflammatory, β -amyloid deposition, and glucose metabolism, plays a vital role in diagnosing neurological disorders by providing complementary insights into the brain's molecular and functional state. Acquiring multi-tracer PET scans remains challenging due to high costs, radiation exposure, and limited tracer availability. Recent studies have attempted to synthesize multi-tracer PET images from structural MRI. However, these approaches typically either rely on direct mappings to individual tracers or lack distributional constraints, leading to inconsistencies in image quality across tracers. To this end, we propose a normalized diffusion framework (NDF) to generate high-quality multi-tracer PET images from a single MRI through a distribution-guided class-conditioned weighted diffusion model. Specifically, a diffusion model conditioned on MRI and tracer-specific class labels is trained to synthesize PET images of multiple tracers, and a pre-trained normalizing flow model refines these outputs by mapping them into a shared distribution space. This mapping ensures that the subject-specific high-level features across different PET tracers are preserved, resulting in more consistent and accurate synthesis. Experiments on a total of 425 subjects with multi-tracer PET scans demonstrate that our NDF outperforms current state-of-the-art methods, indicating its potential for advancing multi-tracer PET synthesis.

Positron emission tomography (PET) and computed tomography (CT) play a vital role in tumor-related medical diagnosis, assessment, and treatment planning. However, full-dose PET and CT pose the risk of excessive radiation exposure to patients, whereas low-dose images compromise image quality, impacting subsequent tumor recognition and diseasediagnosis. To solve such problems, we propose a Noise-Assisted Hybrid Attention Network (NAHANet) to reconstruct full-dose PET and CT images from low-dose PET (LDPET) and CT (LDCT) images to reduce patient radiation risks while ensuring the performance of subsequent tumor recognition. NAHANet contains two branches: the noise feature prediction branch (NFPB) and the cascaded reconstruction branch. Among them, NFPB providing noise features for the cascade reconstruction branch. The cascaded reconstruction branch comprises a shallow feature extraction module and a reconstruction module which contains a series of cascaded noise feature fusion blocks (NFFBs). Among these, the NFFB fuses the features extracted from low-dose images with the noise features obtained by NFPB to improve the feature extraction capability. To validate the effectiveness of the NAHANet method, we performed experiments using two public available datasets: the Ultra-low Dose PET Imaging Challenge dataset and Low Dose CT Grand Challengedataset. As a result, the proposed NAHANet achieved higher performance on common indicators. For example, on the CT dataset, the PSNR and SSIM indicators were improved by 4.1dB and 0.06 respectively, and the rMSE indicator was reduced by 5.46 compared with the LDCT; on the PET dataset, the PSNR and SSIM was improved by 3.37dB and 0.02, and the rMSE was reduced by 9.04 compared with theLDPET. This paper proposes a transformer-based denoising algorithm, which utilizes hybrid attention to extract high-level features of low dose images and fuses noise features to optimize the denoising performance of the network, achieving good performance improvements on low-dose CT and PETdatasets.

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.

Purpose: Positron Emission Tomography (PET) images show physiological and biological information through the in vivo distribution of radioactive, positron‐emitting agents. PET imaging shows focal and distributed regions of cancer and its metastases. Initial PET uses in oncology include diagnosis and staging, which are important for determining treatment decisions. Current PET uses now include target definition for radiation planning followed by PET‐based assessment of treatment. Hybrid PET‐CT devices are beginning to be used as radiation treatment simulators. Method and Materials: F‐18‐labled Fluoro‐deoxyglucose (F‐18 FDG) is the most commonly used PET imaging agent. FDG shows regions of active glucose metabolism, such as local cancer, metastases, non‐cancer inflammation, and normal glucose use. Non‐FDG PET agents can be more specific in cell targeting (binding), and can image different aspects of tumor biology, like hypoxia (FMISO, CuATSM) and cell proliferation (FLT). Results: PET imaging has coarse spatial resolution compared to CT and MR. PET's clinical use is valued because of its great sensitivity for cancer detection. Voxel intensity and image fidelity depend on equipment design, patient size, anatomic site, and imaging study parameters. PET‐CT units enable CT‐based attenuation corrections and inclusion of CT information in a registered PET‐CT dataset. The Standardized Uptake Value (SUV) is a normalized intensity measure for quantitative indication of disease, and can be used to identify disease or possibly for target delineation, with certain limitations. While FDG remains the most promising agent for tumor detection, other, biologically more specific agents (e.g., FMISO, CuATSM for hypoxia imaging or FLT for cell proliferation imaging) might be more appropriate for target delineation and treatment assessment. Conclusion: This course reviews PET‐CT hybrid scanning devices, uses of FDG and non‐FDG PET for oncology imaging, and quantitative aspects for PET‐based radiation target definition and treatment assessment. Example images demonstrate the potential contributions and limitations of FDG and non‐FDG PET oncology imaging. This review course is intended for both imaging and radiation oncology physicists.Educational Objectives:1. Describe FDG PET imaging and oncologic indications.2. Review the uses and limitations of PET images in radiation treatment.3. Describe the SUV and other threshold parameters for target delineation.4. Review non‐FDG PET imaging of tumor biology.5. Discuss quantitative aspects of PET imaging for treatment assessment.

Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read. This paper proposes the use of a deep learning model that takes specific image features into account in the loss function to denoise low-dose PET image slices and estimate their full-dose image quality equivalent. Testing on low-dose image slices indicates a significant improvement in image quality that is comparable to the ground truth full-dose image slices. Additionally, this approach can lower the cost of conducting a PET scan since less radioactive material is required per scan, which may promote the usage of PET scans for medical diagnosis.

An important aspect of positron emission tomography (PET) imaging in a clinical application is the localization and detection of tumors and lesions by administering a predetermined amount of radiotracer. This allows detailed 3D imaging of a wide range of molecular processes in the human body. The quality of the PET image is dependent on the amount of radiotracer administrated and the patient’s body parameters. As the amount of injected radiotracer increases, an overall improvement in the quality of the reconstructed PET images and lesion detectability is expected, but it is accepted that any radiotracer doses are associated with the risk of radiation and it could be harmful to the patient if essential PET imaging is not performed because of the fear of radiation risk. To ensure the highest-quality diagnosis and the smallest radiation risk, the patient should receive the smallest amount of radiotracer that provides an image of sufficient quality. Our study proposed a PET simulation tool to predict the smallest amount of radiotracer that allows for a reliable diagnosis based on patients’ significant body parameters (weight, age) within a fixed total scan time to improve diagnostic processes for detecting and localizing tumors. We built a model of a particular PET scanner and patient, based on real MRI images and a digital anthropomorphic phantom of the brain. We performed Monte Carlo simulations of PET data acquisitions. A dataset of 60 patients was used, and 11 independent dose prediction simulations were performed for each patient. We concluded that our simulator estimated injected radiotracer doses 28% smaller than the standard clinical doses that yielded PET images of clinically acceptable quality. We also found that the total injected radiotracer dose for adult patients was affected by considering the patient’s weight rather than age.

To assess and account for the impact of respiratory motion on the variability of activity and volume determination of liver tumor in positron emission tomography (PET) through a comparison between free-breathing (FB) and respiration-suspended (RS) PET images. As part of a PET/computed tomography (CT) guided percutaneous liver ablation procedure performed on a PET/CT scanner, a patient's breathing is suspended on a ventilator, allowing the acquisition of a near-motionless PET and CT reference images of the liver. In this study, baseline RS and FB PET/CT images of 20 patients undergoing thermal ablation were acquired. The RS PET provides near-motionless reference in a human study, and thereby allows a quantitative evaluation of the effect of respiratory motion on PET images obtained under FB conditions. Two methods were applied to calculate tumor activity and volume: (1) threshold-based segmentation (TBS), estimating the total lesion glycolysis (TLG) and the segmented volume and (2) histogram-based estimation (HBE), yielding the background-subtracted lesion (BSL) activity and associated volume. The TBS method employs 50% of the maximum standardized uptake value (SUVmax) as the threshold for tumors with SUVmax≥2× SUVliver-bkg, and tumor activity above this threshold yields TLG50%. The HBE method determines local PET background based on a Gaussian fit of the low SUV peak in a SUV-volume histogram, which is generated within a user-defined and optimized volume of interest containing both local background and lesion uptakes. Voxels with PET intensity above the fitted background were considered to have originated from the tumor and used to calculate the BSL activity and its associated lesion volume. Respiratory motion caused SUVmax to decrease from RS to FB by -15%±11% (p=0.01). Using TBS method, there was also a decrease in SUVmean (-18%±9%, p=0.01), but an increase in TLG50% (18%±36%) and in the segmented volume (47%±52%, p=0.01) from RS to FB PET images. The background uptake in normal liver was stable, 1%±9%. In contrast, using the HBE method, the differences in both BSL activity and BSL volume from RS to FB were -8%±10% (p=0.005) and 0%±16% (p=0.94), respectively. This is the first time that almost motion-free PET images of the human liver were acquired and compared to free-breathing PET. The BSL method's results are more consistent, for the calculation of both tumor activity and volume in RS and FB PET images, than those using conventional TBS. This suggests that the BSL method might be less sensitive to motion blurring and provides an improved estimation of tumor activity and volume in the presence of respiratory motion.

Whole-body positron emission tomography (PET) with F-fluorodeoxyglucose (FDG) has proven to be a very effective staging imaging modality in manymalignant tumours, particularly in lung cancer, malignant melanoma, lymphoma, colorectal cancer and head and neck tumours [1–7]. Since PET images have a fairly high resolution (,6 mm), even small lesions with an increased FDG uptake can be detected. This represents a critical advantage of PET over the conventional cross-sectional imaging modalities CT and MRI. PET can be used to identify tumour tissue on the basis of FDG uptake, rather than on the basis of morphological appearance or size, and thus also in lesions with a size less than 1 cm. Unfortunately, FDG–PET provides little anatomical information, and therefore it is sometimes difficult to determine where the lesions detected are located and what they represent. It is important for the surgeon to know precisely where lesions are located, especially for small tumours. Currently, comparison and juxtaposition of PET images and CT images serves to define the localization of the lesions. Owing to the different image slice thickness, the different positioning of the patient during the two examinations and the different states of nutrition, i.e. fasting for 4 h prior to FDG–PET, it is often very difficult and time consuming to match the images for exact anatomical localization. Image co-registration has been used to overcome these problems [8]. It has been recognized that proper image co-registration is rather difficult for whole-body examinations with separate scanners. For identical patient positioning during PET and CT scanning, special ‘‘cast forming’’ vacuum mattresses or other immobilization methods should be used. However, patients frequently find these methods of immobilization uncomfortable. Logistic problems also limit the use of image co-registration of PET and CT with separate scanners. Scheduling problems arise when performing the two examinations one after the other. These limitations restrict image co-registration with separate scanners to individual cases. In our experience, the exact localization of lesions is often not possible when juxtaposing PET and CT images. Examinations had to be repeated for exact image co-registration. As a result, image co-registration with separate scanners has never become routine in our practice. To overcome the problems related to software fusion, various prototype systems combining molecular and morphologic imaging have been developed. Hasegawa and colleagues proposed an integrated SPECT/CT scanner [9], and in 1999 Townsend and his colleagues at the University of Pittsburgh presented a prototype integrated PET/CT scanner by combining a CT scanner and a partial ring, rotating PET scanner in a single gantry [10, 11]. Such systems are attractive not only because of the ‘‘hardware’’ image coregistration capabilities but also because the CT data can be used to correct PET scans for patient self-absorption of the PET emission rays. This decreases the acquisition time for the correcting attenuation scans by an order of magnitude compared with the conventional techniques used in PET for this purpose, and decreases the overall imaging time by up to 30% depending on the CT system used. In March 2001, the first clinical integrated PET/ CT device (Discovery LS, GE Medical Systems, Milwaukee, USA) was installed at the University Hospital of Zurich in Switzerland. This novel inline system consists of a dedicated high resolution/ high sensitivity full-ring PET scanner (Advance Nxi) and a very fast high-end multi-detector spiral CT scanner (Light Speed Plus). The axes of both systems are mechanically aligned so that simple translation of the patient table by 60 cm moves the patient from the CT into the PET gantry. The resulting PET and CT images are ‘‘hardware’’ coregistered to an accuracy in the range of 1 mm. Data acquisition in the combined system is as follows. At 45–55 min post-injection of FDG and after bladder emptying a multidetector computed tomography scan is performed from the pelvic floor to the head (scan length, 86.7 cm) and normal end-expiration. Immediately after CT scanning, a PET emission scan is obtained covering the identical axial field of view using six PET fields of view. The acquisition time is 4 min at each table position. With this protocol it is The British Journal of Radiology, 75 (2002), S36–S38 E 2002 The British Institute of Radiology

Cardiac positron emission tomography imaging

Positive Positron Emission Tomography (PET) Scan in Non-Hodgkin Lymphoma Pre-Transplant Had No Impact On Relapse and Survival After Allogenenic Transplantation

Given the ability of positron emission tomography (PET) imaging to localize malignancies in heterogeneous tumors and tumors that lack an X-ray computed tomography (CT) correlate, combined PET/CT-guided biopsy may improve the diagnostic yield of biopsies. However, PET and CT images are naturally susceptible to problems due to respiratory motion, leading to imprecise tumor localization and shape distortion. To facilitate PET/CT-guided needle biopsy, we developed and investigated the feasibility of a workflow that allows to bring PET image guidance into interventional CT suite while accounting for respiratory motion. The performance of PET/CT respiratory motion correction using registered and summed phases method was evaluated through computer simulations using the mathematical 4D extended cardiac-torso phantom, with motion simulated from real respiratory traces. The performance of PET/CT-guided biopsy procedure was evaluated through operation on a physical anthropomorphic phantom. Vials containing radiolabeled 18F-fluorodeoxyglucose were placed within the physical phantom thorax as biopsy targets. We measured the average distance between target center and the simulated biopsy location among multiple trials to evaluate the biopsy localization accuracy. The computer simulation results showed that the RASP method generated PET images with a significantly reduced noise of 0.10 ± 0.01 standardized uptake value (SUV) as compared to an end-of-expiration image noise of 0.34 ± 0.04 SUV. The respiratory motion increased the apparent liver lesion size from 5.4 ± 1.1 to 35.3 ± 3.0cc. The RASP algorithm reduced this to 15.7 ± 3.7cc. The distances between the centroids for the static image lesion and two moving lesions in the liver and lung, when reconstructed with the RASP algorithm, were 0.83 ± 0.72mm and 0.42 ± 0.72mm. For the ungated imaging, these values increased to 3.48 ± 1.45mm and 2.5 ± 0.12mm, respectively. For the ungated imaging, this increased to 1.99 ± 1.72mm. In addition, the lesion activity estimation (e.g., SUV) was accurate and constant for images reconstructed using the RASP algorithm, whereas large activity bias and variations (± 50%) were observed for lesions in the ungated images. The physical phantom studies demonstrated a biopsy needle localization error of 2.9 ± 0.9mm from CT. Combined with the localization errors due to respiration for the PET images from simulations, the overall estimated lesion localization error would be 3.08mm for PET-guided biopsies images using RASP and 3.64mm when using ungated PET images. In other words, RASP reduced the localization error by approximately 0.6mm. The combined error analysis showed that replacing the standard end-of-expiration images with the proposed RASP method in PET/CT-guided biopsy workflow yields comparable lesion localization accuracy and reduced image noise. The RASP method can produce PET images with reduced noise, attenuation artifacts and respiratory motion, resulting in more accurate lesion localization. Testing the PET/CT-guided biopsy workflow using computer simulation and physical phantoms with respiratory motion, we demonstrated that guided biopsy procedure with the RASP method can benefit from improved PET image quality due to noise reduction, without compromising the accuracy of lesion localization.

Multiplexed Positron Emission Tomography (PET) imaging allows simultaneous acquisition of multiple radiotracer signals, thus enhancing diagnostic capabilities, reducing scan times, and improving patient comfort. Traditional methods often require significant delays between tracer injections, leading to physiological changes and noise interference. Recent advancements, including multi-tracer compartment modeling and machine learning, provide promising solutions. This study explores the deep learning (DL)-based single-session triple-tracer brain PET imaging protocol, aiming at simplifying multi-tracer PET imaging, while reducing radiation exposure. The study uses the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which includes cognitively normal (CN) patients, as well as patients with mild cognitive impairment (MCI) and dementia. The dataset also includes PET scans acquired with amyloid (18F-florbetaben [FBB] or 18F-florbetapir [FBP]), 18F-Fluorodeoxyglucose (FDG), and tau 18F-flortaucipir (FTP). To mimic the effect of simultaneous acquisition of multiple PET tracers, we generated synthetic dual- and triple-tracer images by summing FBP/FBB, FTP, and FDG scans. A DL model based on Swin Transformer architecture was developed to separate these signals, using five-fold cross-validation and mean squared error (MSE) loss. The synthetic PET images were evaluated using established image quality metrics, including MSE, structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). In addition, clinical evaluation was conducted by two nuclear medicine specialists to assess the amyloid and tau status in the synthetic and reference images. The proposed DL model effectively synthesized realistic FBB/FBP and FDG images from dual- and triple-tracer PET images. Although the proposed DL model's performance in generating FTP images was less successful, it remains promising. The clinical evaluation revealed that the amyloid status estimated from the synthetic images led to a sensitivity of 92% and specificity of 86% for FBB, while it showed a sensitivity of 93% and specificity of 67% for tau status using FBP extracted from the triple-tracer images. The calculated quantitative metrics showed that the mean error for synthetic amyloid images (FBB: 0.03 SUV, FBP: 0.00 SUV) was higher than FDG for FBB (0.02 SUV) but lower than FDG for FBP (-0.01 SUV), and comparable to FTP (FBB: 0.03 SUV, FBP: 0.00 SUV). Voxel-wise correlation analysis demonstrated strong correlation between synthetic and reference images, particularly for amyloid images (FBB: y = 0.98x + 0.00, R² = 0.85; FBP: y = 1.11x + 0.04, R² = 0.73), while FTP (FBB: y = 0.87x + 0.14, R² = 0.51; FBP: y = 0.98x + 0.09, R² = 0.59) and FDG images (FBB: y = 1.01x + 0.18, R² = 0.85; FBP: y = 0.96x + 1.37, R² = 0.77) showed moderate correlations. Our study demonstrates that the suggested DL model can separate the signals belonging to three different radiotracers from simultaneous triple-tracer PET scans. This method may make multiplex scanning feasible in the clinic, hence reducing the scanning time, radiation hazard and improving patient comfort.

Compton imaging or Compton camera imaging has been studied well, but its advantages in nuclear medicine and molecular imaging have not been demonstrated yet. Therefore, the aim of this work was to compare Compton imaging with positron emission tomography (PET) by using the same imaging platform of whole gamma imaging (WGI). WGI is a concept that combines PET with Compton imaging by inserting a scatterer ring into a PET ring. This concept utilizes diverse types of gamma rays for 3D tomographic imaging. In this paper, we remodeled our previous WGI prototype for small animal imaging, and we developed an image reconstruction method based on a list-mode ordered subset expectation maximization algorithm incorporating detector response function modeling, random correction and normalization (sensitivity correction) for either PET and Compton imaging. To the best of our knowledge, this is the world’s first realization of a full-ring Compton imaging system. We selected 89Zr as an imaging target because a 89Zr nuclide emits a 909 keV single-gamma ray as well as a positron, and we can directly compare Compton imaging of 909 keV photons with PET, a well-established modality. We measured a cylindrical phantom and a small rod phantom filled with 89Zr solutions of 10.3 MBq and 10.2 MBq activity, respectively, for 1 h each. The uniform radioactivity distribution of the cylindrical phantom was reconstructed with normalization in both PET and Compton imaging. Coefficients of variation for region-of-interest values were 4.2% for Compton imaging and 3.3% for PET; the difference might be explained by the difference in the detected count number. The small rod phantom experiment showed that the WGI Compton imaging had spatial resolution better than 3.0 mm at the peripheral region although the center region had lower resolution. PET resolved 2.2 mm rods clearly at any location. We measured a mouse for 1 h, 1 d after injection of 9.8 MBq 89Zr oxalate. The 89Zr assimilated in the mouse bony structures was clearly depicted, and Compton imaging results agreed well with PET images, especially for the region inside the scatterer ring. In conclusion, we demonstrated the performance of WGI using the developed Compton image reconstruction method. We realized Compton imaging with a quality approaching that of PET, which is supporting a future expectation that Compton imaging outperforms PET.

Inter-crystal scattering (ICS) events in Positron Emission Tomography (PET) present challenges affecting system sensitivity and image quality. Understanding the physics and factors influencing ICS occurrence is crucial for developing strategies to mitigate its impact. This review paper explores the physics behind ICS events and their occurrence within PET detectors. Various methodologies, including energy-based comparisons, Compton kinematics-based approaches, statistical methods, and Artificial Intelligence (AI) techniques, which have been proposed for identifying and recovering ICS events accurately are introduced. Energy-based methods offer simplicity by comparing energy depositions in crystals. Compton kinematics-based approaches utilize trajectory information for first interaction position estimation, yielding reasonably good results. Additionally, statistical approach and AI algorithms contribute by optimizing likelihood analysis and neural network models for improved positioning accuracy. Experimental validations and simulation studies highlight the potential of recovering ICS events and enhancing PET sensitivity and image quality. Especially, AI technologies offers a promising avenue for addressing ICS challenges and improving PET image accuracy and resolution. These methods offer promising solutions for overcoming the challenges posed by ICS events and enhancing the accuracy and resolution of PET imaging, ultimately improving diagnostic capabilities and patient outcomes. Further studies applying these approaches to real PET systems are needed to validate theoretical results and assess practical implementation feasibility.