An MRI-guided PET partial volume correction method

The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using 18 F-fluoro-2-deoxy-D-glucose ([18 F]FDG) and 11 C-labeled Pittsburg compound B ([11 C]PiB). Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/computed tomography (CT) 710 and reconstructed using OSEM+time-of-flight (TOF) under clinical conditions and BPL+TOF (β=20-1000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [18 F]FDG and [11 C]PiB PET to validate the findings from the phantom study. The β values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal β values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent). The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥55% and exceeded that of OSEM at ranges of β=20-450 and 20-600 for [18 F]FDG and [11 C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤15% and was below that in OSEM when β=150-1000 and 400-1000 for [18 F]FDG and [11 C]PiB, respectively. The phantom study restricted the ranges of β values to 100-300 and 300-500 for [18 F]FDG and [11 C]PiB, respectively. The BPL scores for gray-white matter contrast and image noise, exceeded those of OSEM in [18 F]FDG and [11 C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β values were 200 and 450 for [18 F]FDG and [11 C]PiB, respectively. The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative standardized uptake value ratios (SUVR) due to full convergence, more rigorous noise control, and edge preservation. The optimal β values for [18 F]FDG and [11 C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal β values in BPL for brain PET imaging.

Small-animal positron emission tomography as a tool for neuropharmacology

Background: Positron emission tomography (PET) imaging in epilepsy is an in vivo technique that allows the localization of a possible seizure onset zone (SOZ) during the interictal period. Stereo-electro-encephalography (SEEG) is the gold standard to define the SOZ. The objective of this research was to evaluate the accuracy of PET imaging in localizing the site of SOZ compared with SEEG. Methods: Seven patients with refractory temporal lobe epilepsy (Ep) and 2 healthy controls (HC) underwent 2 PET scans, one with 2-[¹⁸F]-fluoro-2-deoxy-D-glucose (FDG) and another with 2’-[¹⁸F]fluoroflumazenil (FFMZ), acquired 1 day apart. FDG was acquired for 10 min (static scan) 1 h after administration. An FFMZ scan was acquired for 60 min from radiopharmaceutical administration in a dynamic mode. Each brain PET image was segmented using a standard template implemented in PMOD 3.8. The pons was used as the reference region for modeling of the nondisplaceable binding potential (BP_ND)for FFMZ, and to obtain uptake ratios for FDG. SEEG studies of patients were performed as a part of their surgical evaluation to define the SOZ. Results: Well-defined differences between HC and Ep were found with both radiopharmaceuticals, showing the utility to identify abnormal brain regions using quantitative PET imaging. Lateralization of the SOZ findings by PET (lower uptake/binding in a specific brain hemisphere) matched in 86% for FFMZ and 71% for FDG with SEEG data. Conclusion: Quantitative PET imaging is an excellent complementary tool that matches reasonably well with SEEG to define SOZ in presurgical evaluation.

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.

Chapter 32 - Dissolution of Partial Volume Effect in PET by an Inversion Technique with the MR-Embedded Neural Network Model

Partial volume effect (PVE) in positron emission tomography (PET) leads to inaccurate estimation of regional metabolic activities among neighbouring tissues with different tracer concentration. This may be one of the main limiting factors in the utilization of PET in clinical practice. Partial volume correction (PVC) methods have been widely studied to address this issue. MRI based PVC methods are well-established.1 Their performance depend on the quality of the co-registration of the MR and PET dataset, on the correctness of the estimated point-spread function (PSF) of the PET scanner and largely on the performance of the segmentation method that divide the brain into brain tissue compartments.1, 2 In the present study a method for PVC is suggested, that utilizes cortical surfaces, to obtain detailed anatomical information. The objectives are to improve the performance of PVC, facilitate a study of the relationship between metabolic activity in the cerebral cortex and cortical thicknesses, and to obtain an improved visualization of PET data. The gray matter metabolic activity after performing PVC was recovered by 99.7 - 99.8 % , in relation to the true activity when testing on simple simulated data with different PSFs and by 97.9 - 100 % when testing on simulated brain PET data at different cortical thicknesses. When studying the relationship between metabolic activities and anatomical structures it was shown on simulated brain PET data, that it is important to correct for PVE in order to get the true relationship.

Lung cancer is responsible for 32% of all cancer deaths in men and 25% in women in the United States It also ranked first in women(17.3%)and ranked second in men(23.2%)of cancer death here in Taiwan. The incidence of lung cancer is still increasing globally. In United States, despite the curative intention to treat the disease, about 6% of patients are unresectable at surgery and 14% die in one year. Improvement of the diagnostic strategy in lung cancer is thus needed. Positron emission tomography(PET)imaging can reflect the biochemical processes in cancers. Due to the high cancer-to-background ratio in lung parenchyma, 2-[18F]fluoro-2-deoxy-D-glucose(FDG)has become the most commonly used radiopharmaceutical in the clinical oncology. The clinical applications of FDG PET imaging has moved beyond the clinical diagnosis and initial staging for lung cancer. The current applications of PET and especially PET-CT focus on the treatment planning for lung cancer. Continued advances in computer, electronic and crystal technology and development of new biotracers have resulted in improved management of lung cancer by functional PET imaging in the diagnosis, staging, treatment planning and evaluation of therapeutic responses. PET and PET-CT has moved into the main stream of clinical applications, which may prove to impact greatly on the patient survival in the years to come.

A wide array of positron emission tomography (PET) ligands have been developed that provide information relevant to multiple aspects of addiction (1-3).Locations of targets are approximated on this diagram, which is color-coded by functional roles.On the presynaptic side (pink), targets include neurotransmitter precursors (orange, example [ 18 F]-fluorodopa, FDOPA), modulatory receptors (yellow, examples [ 11 C] GR103545, [ 11 C] LY27950509 and [ 18 F] LY2459989 for kappa opioid receptors, KOR; [ 18 F]FMPEP for cannabinoid 1 receptors, CB1), and neurotransmitter reuptake transporters (light green, examples [ 11 C] PE2I and [ 18 F] FE-PE2I for the dopamine transporter, DAT).On the postsynaptic side (purple), targets include neurotransmitter receptors (blue, examples [ 11 C] raclopride and [ 11 C]-(1)-PHNO for dopamine D 2 and D 3 receptors, D 2 R; [ 11 C] carfentanil for mu opioid receptors, MOR; [ 18 F] LY2459989 for both mu and kappa opioid receptors; 2-[ 18 F]fluoro-3-(2(S)-azetidinylmethoxy) pyridine for nicotinic acetylcholine receptors, nAChR).Some PET ligands target more generic functions.[ 18 F]-fluoro-deoxy-glucose (FDG) PET provides a measure of cerebral metabolic rate.

The partial volume effect (PVE), caused by the limited spatial resolution of positron emission tomography (PET), degrades images both qualitatively and quantitatively. Anatomical information provided by magnetic resonance (MR) images has the potential to play an important role in partial volume correction (PVC) methods. Post-reconstruction MR-guided PVC methods typically use segmented MR tissue maps, and further, assume that PET activity distribution is uniform in each region, imposing considerable constraints through anatomical guidance. In this work, we present a post-reconstruction PVC method based on deconvolution with parallel level set (PLS) regularization. We frame the problem as an iterative deconvolution task with PLS regularization that incorporates anatomical information without requiring MR segmentation or assuming uniformity of PET distributions within regions. An efficient algorithm for non-smooth optimization of the objective function (invoking split Bregman framework) is developed so that the proposed method can be feasibly applied to 3D images and produces sharper images compared to PLS method with smooth optimization. The proposed method was evaluated together with several other PVC methods using both realistic simulation experiments based on the BrainWeb phantom as well as in vivo human data. Our proposed method showed enhanced quantitative performance when realistic MR guidance was provided. Further, the proposed method is able to reduce image noise while preserving structure details on in vivo human data, and shows the potential to better differentiate amyloid positive and amyloid negative scans. Overall, our results demonstrate promise to provide superior performance in clinical imaging scenarios.

Positron emission tomography (PET) is a powerful imaging modality that has the ability to yield quantitative images of tracer activity. Physical phenomena such as photon scatter, photon attenuation, random coincidences and spatial resolution limit quantification potential and must be corrected to preserve the accuracy of reconstructed images. This study focuses on correcting the partial volume effects that arise in mouse heart imaging when resolution is insufficient to resolve the true tracer distribution in the myocardium. The correction algorithm is based on fitting 1D profiles through the myocardium in gated PET images to derive myocardial contours along with blood, background and myocardial activity. This information is interpolated onto a 2D grid and convolved with the tomograph's point spread function to derive regional recovery coefficients enabling partial volume correction. The point spread function was measured by placing a line source inside a small animal PET scanner. PET simulations were created based on noise properties measured from a reconstructed PET image and on the digital MOBY phantom. The algorithm can estimate the myocardial activity to within 5% of the truth when different wall thicknesses, backgrounds and noise properties are encountered that are typical of healthy FDG mouse scans. The method also significantly improves partial volume recovery in simulated infarcted tissue. The algorithm offers a practical solution to the partial volume problem without the need for co-registered anatomic images and offers a basis for improved quantitative 3D heart imaging.

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.

Gray matter segmentation and delineation from positron emission tomography (PET) are a very essential requirement in medical applications due to low spatial resolution, presence of noise, and larger variability in the shape as well as in texture. PET images provide the functional details of the brain which are very much essential to detect brain disorders. The diagnosis of dementia, particularly in the early stages, are very much helpful with PET image processing. In this present work, we implement three essential steps, which significantly help during dementia analysis. The first step is skull stripping from PET image based on K-means and connected component analysis, the second step is to segment gray matter from skull-stripped PET image using fuzzy possibilistic C-means (FPCM), and the last step is to delineate the gray matter based on cellular automata. The Indian PET dataset collected from Apollo Gleneagles Hospitals, Kolkata, India, has been used in our experiment as a standard dataset. Experimental results show that the proposed method has achieved promising results for automatic segmentation and delineation of gray matter on PET brain images.

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.

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.

Positron emission tomography (PET) and single photon emission computed tomography (SPECT) brain imaging are widely used as diagnostic tools for suspected dementia but no studies have directly compared participant views of the two procedures. We used a range of methods to explore preferences for PET and SPECT. Patients and controls (and accompanying carers) completed questionnaires immediately after undergoing PET and SPECT brain scans. Pulse rate data were collected during each scan. Scan attributes were prioritized using a card sorting exercise; carers and controls additionally answered willingness to pay (WTP) questions. Few differences were found either between the scans or groups of participants, although carers marginally preferred SPECT. Diagnostic accuracy was prioritized over other scan characteristics. Mean heart rate during both scans was lower than baseline heart rate measured at home (p < 0.001). Most participants viewed PET and SPECT scans as roughly equivalent and did not have a preference for either scan. Carer preference for SPECT is likely to reflect their desire to be with the patient (routine practice for SPECT but not for PET), suggesting that they should be able to accompany vulnerable patients throughout imaging procedures wherever possible. Pulse rate data indicated that brain imaging was no more stressful than a home visit (HV) from a researcher. The data do not support the anecdotal view that PET is a more burdensome procedure and the use of PET or SPECT scans in dementia should be based on diagnostic accuracy of the technique.