Scatter modelling and compensation in emission tomography

Technological Advances in SPECT and SPECT/CT Imaging

Single photon emission tomography/computed tomography (SPECT/CT) is a mature imaging technology with a dynamic role in the diagnosis and monitoring of a wide array of diseases. This paper reviews the technological advances, clinical impact, and future directions of SPECT and SPECT/CT imaging. The focus of this review is on signal amplifier devices, detector materials, camera head and collimator designs, image reconstruction techniques, and quantitative methods. Bulky photomultiplier tubes (PMTs) are being replaced by position-sensitive PMTs (PSPMTs), avalanche photodiodes (APDs), and silicon PMs to achieve higher detection efficiency and improved energy resolution and spatial resolution. Most recently, new SPECT cameras have been designed for cardiac imaging. The new design involves using specialised collimators in conjunction with conventional sodium iodide detectors (NaI(Tl)) or an L-shaped camera head, which utilises semiconductor detector materials such as CdZnTe (CZT: cadmium-zinc-telluride). The clinical benefits of the new design include shorter scanning times, improved image quality, enhanced patient comfort, reduced claustrophobic effects, and decreased overall size, particularly in specialised clinical centres. These noticeable improvements are also attributed to the implementation of resolution-recovery iterative reconstructions. Immense efforts have been made to establish SPECT and SPECT/CT imaging as quantitative tools by incorporating camera-specific modelling. Moreover, this review includes clinical examples in oncology, neurology, cardiology, musculoskeletal, and infection, demonstrating the impact of these advancements on clinical practice in radiology and molecular imaging departments.

Development of a Monte Carlo-based scatter correction method for total-body PET using the uEXPLORER PET/CT scanner

Objective.This study presents and evaluates a robust Monte Carlo-based scatter correction (SC) method for long axial field of view (FOV) and total-body positron emission tomography (PET) using the uEXPLORER total-body PET/CT scanner.Approach.Our algorithm utilizes the Monte Carlo (MC) tool SimSET to compute SC factors in between individual image reconstruction iterations within our in-house list-mode and time-of-flight-based image reconstruction framework. We also introduced a unique scatter scaling technique at the detector block-level for optimal estimation of the scatter contribution in each line of response. First image evaluations were derived from phantom data spanning the entire axial FOV along with image data from a human subject with a large body mass index. Data was evaluated based on qualitative inspections, and contrast recovery, background variability, residual scatter removal from cold regions, biases and axial uniformity were quantified and compared to non-scatter-corrected images.Main results.All reconstructed images demonstrated qualitative and quantitative improvements compared to non-scatter-corrected images: contrast recovery coefficients improved by up to 17.2% and background variability was reduced by up to 34.3%, and the residual lung error was between 1.26% and 2.08%. Low biases throughout the axial FOV indicate high quantitative accuracy and axial uniformity of the corrections. Up to 99% of residual activity in cold areas in the human subject was removed, and the reliability of the method was demonstrated in challenging body regions like in the proximity of a highly attenuating knee prosthesis.Significance.The MC SC method employed was demonstrated to be accurate and robust in TB-PET. The results of this study can serve as a benchmark for optimizing the quantitative performance of future SC techniques.

Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks

In positron emission tomography (PET), attenuation and scatter corrections are necessary steps toward accurate quantitative reconstruction of the radiopharmaceutical distribution. Inspired by recent advances in deep learning, many algorithms based on convolutional neural networks have been proposed for automatic attenuation and scatter correction, enabling applications to CT-less or MR-less PET scanners to improve performance in the presence of CT-related artifacts. A known characteristic of PET imaging is to have varying tracer uptakes for various patients and/or anatomical regions. However, existing deep learning-based algorithms utilize a fixed model across different subjects and/or anatomical regions during inference, which could result in spurious outputs. In this work, we present a novel deep learning-based framework for the direct reconstruction of attenuation and scatter-corrected PET from non-attenuation-corrected images in the absence of structural information in the inference. To deal with inter-subject and intra-subject uptake variations in PET imaging, we propose a novel model to perform subject- and region-specific filtering through modulating the convolution kernels in accordance to the contextual coherency within the neighboring slices. This way, the context-aware convolution can guide the composition of intermediate features in favor of regressing input-conditioned and/or region-specific tracer uptakes. We also utilized a large cohort of 910 whole-body studies for training and evaluation purposes, which is more than one order of magnitude larger than previous works. In our experimental studies, qualitative assessments showed that our proposed CT-free method is capable of producing corrected PET images that accurately resemble ground truth images corrected with the aid of CT scans. For quantitative assessments, we evaluated our proposed method over 112 held-out subjects and achieved an absolute relative error of 14.30±3.88% and a relative error of -2.11%±2.73% in whole-body.

Whole Gamma Imaging: Challenges and Opportunities

Compton imaging has been recognized as a possible nuclear medicine imaging method following the establishment of SPECT and PET. Whole gamma imaging (WGI), a combination of PET and Compton imaging, could be the first practical method to bring out the potential of Compton imaging in nuclear medicine. With the use of such positron emitters as 89Zr and 44Sc, WGI may enable highly sensitive imaging of antibody drugs for early tumor detection and quantitative hypoxia imaging for effective tumor treatment. Some of these concepts have been demonstrated preliminarily in physics experiments and small animal imaging tests with a developed WGI prototype.

Energy-based scatter estimation in clinical PET

BACKGROUND

Scatter correction (SC) is essential in PET for accurate quantitative imaging. The state-of-the-art SC method is single-scatter simulation (SSS). Although this method is usually robust and accurate, it can fail in some situations, for example when there is motion between the CT and PET scans in PET/CT. Therefore, it is of interest to consider other SC methods.

PURPOSE

In this work, an energy-based scatter estimation (EBS) method is described in detail, tested in phantoms and patients, and compared to SSS.

METHODS

This version of EBS was developed for list-mode data from Biograph Vision-600 PET/CT scanner. EBS is based on digitized 2D energy histograms in each bin of a coarsely sampled PET sinogram, either with or without time of flight (TOF). The histograms are modeled as a noisy realization of a linear combination of nine basis functions whose parameters were derived from a measurement of the 511-keV photopeak spectrum as well as Monte-Carlo simulations of the scattering process. EBS uses an iterative expectation maximization approach to determine the coefficients in the linear combination, and from this estimates the scatter. The investigation was restricted to 18 F-based PET data in which the acquired number of counts was similar to the levels seen in oncological whole-body PET/CT scans. To evaluate the performance, phantom scans were used that involved the NEMA NU2-2018 protocol, a slab phantom, an NU 2-1994 phantom, a cardiac phantom in an anthropomorphic chest phantom, and a uniformly-filled torso phantom with a bladder phantom slightly outside the axial field of view. Contrast recovery (CR) and other parameters were evaluated in images reconstructed with SSS and EBS. Furthermore, FDG PET scans of seven lung cancer patients were used in the evaluation. Standardized uptake values (SUV) based on SSS and EBS were compared in 27 lesions.

RESULTS

EBS and SSS images were visually similar in all cases except the torso + bladder phantom, where the EBS was much closer to the expected uniform image. The NU2-2018 analysis indicated a 2% scatter residual in EBS images compared to 3% with SSS, and 10% higher background variability, which is a surrogate for image noise. The cardiac phantom scan showed that CR was 98.2% with EBS and 99.6% with SSS, and that the SSS sinogram had values greater than the net-true emission sinogram, indicating a slight overcorrection in the case of SSS. In the lesion SUV comparison in patient scans, EBS correlated strongly (R2  = 0.9973) with SSS, and SUV based on EBS were systematically 0.1 SUV lower. In the case of the torso + bladder phantom portion, the SSS image of the torso + bladder phantom was 299% times hotter than expected in one area, due to scatter estimation error, compared to 16% colder with EBS.

CONCLUSIONS

In evaluating clinically relevant parameters such as SUV in focal lesions, EBS and SSS give almost the same results. In phantoms, some scatter figures of merit were slightly improved by use of EBS, though an image variability figure of merit was slightly degraded. In typical oncological whole-body PET/CT, EBS may be a suitable replacement for SSS, especially when SSS fails due to technical problems during the scan.

Differential privacy preserved federated transfer learning for multi-institutional 68Ga-PET image artefact detection and disentanglement

PURPOSE

Image artefacts continue to pose challenges in clinical molecular imaging, resulting in misdiagnoses, additional radiation doses to patients and financial costs. Mismatch and halo artefacts occur frequently in gallium-68 (68Ga)-labelled compounds whole-body PET/CT imaging. Correcting for these artefacts is not straightforward and requires algorithmic developments, given that conventional techniques have failed to address them adequately. In the current study, we employed differential privacy-preserving federated transfer learning (FTL) to manage clinical data sharing and tackle privacy issues for building centre-specific models that detect and correct artefacts present in PET images.

METHODS

Altogether, 1413 patients with 68Ga prostate-specific membrane antigen (PSMA)/DOTA-TATE (TOC) PET/CT scans from 3 countries, including 8 different centres, were enrolled in this study. CT-based attenuation and scatter correction (CT-ASC) was used in all centres for quantitative PET reconstruction. Prior to model training, an experienced nuclear medicine physician reviewed all images to ensure the use of high-quality, artefact-free PET images (421 patients' images). A deep neural network (modified U2Net) was trained on 80% of the artefact-free PET images to utilize centre-based (CeBa), centralized (CeZe) and the proposed differential privacy FTL frameworks. Quantitative analysis was performed in 20% of the clean data (with no artefacts) in each centre. A panel of two nuclear medicine physicians conducted qualitative assessment of image quality, diagnostic confidence and image artefacts in 128 patients with artefacts (256 images for CT-ASC and FTL-ASC).

RESULTS

The three approaches investigated in this study for 68Ga-PET imaging (CeBa, CeZe and FTL) resulted in a mean absolute error (MAE) of 0.42 ± 0.21 (CI 95%: 0.38 to 0.47), 0.32 ± 0.23 (CI 95%: 0.27 to 0.37) and 0.28 ± 0.15 (CI 95%: 0.25 to 0.31), respectively. Statistical analysis using the Wilcoxon test revealed significant differences between the three approaches, with FTL outperforming CeBa and CeZe (p-value < 0.05) in the clean test set. The qualitative assessment demonstrated that FTL-ASC significantly improved image quality and diagnostic confidence and decreased image artefacts, compared to CT-ASC in 68Ga-PET imaging. In addition, mismatch and halo artefacts were successfully detected and disentangled in the chest, abdomen and pelvic regions in 68Ga-PET imaging.

CONCLUSION

The proposed approach benefits from using large datasets from multiple centres while preserving patient privacy. Qualitative assessment by nuclear medicine physicians showed that the proposed model correctly addressed two main challenging artefacts in 68Ga-PET imaging. This technique could be integrated in the clinic for 68Ga-PET imaging artefact detection and disentanglement using multicentric heterogeneous datasets.

Artificial Intelligence-Driven Single-Shot PET Image Artifact Detection and Disentanglement: Toward Routine Clinical Image Quality Assurance

PURPOSE

Medical imaging artifacts compromise image quality and quantitative analysis and might confound interpretation and misguide clinical decision-making. The present work envisions and demonstrates a new paradigm PET image Quality Assurance NETwork (PET-QA-NET) in which various image artifacts are detected and disentangled from images without prior knowledge of a standard of reference or ground truth for routine PET image quality assurance.

METHODS

The network was trained and evaluated using training/validation/testing data sets consisting of 669/100/100 artifact-free oncological 18 F-FDG PET/CT images and subsequently fine-tuned and evaluated on 384 (20% for fine-tuning) scans from 8 different PET centers. The developed DL model was quantitatively assessed using various image quality metrics calculated for 22 volumes of interest defined on each scan. In addition, 200 additional 18 F-FDG PET/CT scans (this time with artifacts), generated using both CT-based attenuation and scatter correction (routine PET) and PET-QA-NET, were blindly evaluated by 2 nuclear medicine physicians for the presence of artifacts, diagnostic confidence, image quality, and the number of lesions detected in different body regions.

RESULTS

Across the volumes of interest of 100 patients, SUV MAE values of 0.13 ± 0.04, 0.24 ± 0.1, and 0.21 ± 0.06 were reached for SUV mean , SUV max , and SUV peak , respectively (no statistically significant difference). Qualitative assessment showed a general trend of improved image quality and diagnostic confidence and reduced image artifacts for PET-QA-NET compared with routine CT-based attenuation and scatter correction.

CONCLUSION

We developed a highly effective and reliable quality assurance tool that can be embedded routinely to detect and correct for 18 F-FDG PET image artifacts in clinical setting with notably improved PET image quality and quantitative capabilities.

Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT

Accurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a 100× shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for 177Lu SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing.

Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning

PURPOSE

Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, trustworthy, and generalizable DL models commonly require well-curated, heterogeneous, and large datasets from multiple clinical centers. At the same time, owing to legal/ethical issues and privacy concerns, forming a large collective, centralized dataset poses significant challenges. In this work, we aimed to develop a DL-based model in a multicenter setting without direct sharing of data using federated learning (FL) for AC/SC of PET images.

METHODS

Non-attenuation/scatter corrected and CT-based attenuation/scatter corrected (CT-ASC) ¹⁸F-FDG PET images of 300 patients were enrolled in this study. The dataset consisted of 6 different centers, each with 50 patients, with scanner, image acquisition, and reconstruction protocols varying across the centers. CT-based ASC PET images served as the standard reference. All images were reviewed to include high-quality and artifact-free PET images. Both corrected and uncorrected PET images were converted to standardized uptake values (SUVs). We used a modified nested U-Net utilizing residual U-block in a U-shape architecture. We evaluated two FL models, namely sequential (FL-SQ) and parallel (FL-PL) and compared their performance with the baseline centralized (CZ) learning model wherein the data were pooled to one server, as well as center-based (CB) models where for each center the model was built and evaluated separately. Data from each center were divided to contribute to training (30 patients), validation (10 patients), and test sets (10 patients). Final evaluations and reports were performed on 60 patients (10 patients from each center).

RESULTS

In terms of percent SUV absolute relative error (ARE%), both FL-SQ (CI:12.21-14.81%) and FL-PL (CI:11.82-13.84%) models demonstrated excellent agreement with the centralized framework (CI:10.32-12.00%), while FL-based algorithms improved model performance by over 11% compared to CB training strategy (CI: 22.34-26.10%). Furthermore, the Mann-Whitney test between different strategies revealed no significant differences between CZ and FL-based algorithms (p-value > 0.05) in center-categorized mode. At the same time, a significant difference was observed between the different training approaches on the overall dataset (p-value < 0.05). In addition, voxel-wise comparison, with respect to reference CT-ASC, exhibited similar performance for images predicted by CZ (R² = 0.94), FL-SQ (R² = 0.93), and FL-PL (R² = 0.92), while CB model achieved a far lower coefficient of determination (R² = 0.74). Despite the strong correlations between CZ and FL-based methods compared to reference CT-ASC, a slight underestimation of predicted voxel values was observed.

CONCLUSION

Deep learning-based models provide promising results toward quantitative PET image reconstruction. Specifically, we developed two FL models and compared their performance with center-based and centralized models. The proposed FL-based models achieved higher performance compared to center-based models, comparable with centralized models. Our work provided strong empirical evidence that the FL framework can fully benefit from the generalizability and robustness of DL models used for AC/SC in PET, while obviating the need for the direct sharing of datasets between clinical imaging centers.

SPECT/CT and PET/CT, related radiopharmaceuticals, and areas of application and comparison

The paper begins by identifying the key historical elements in the development of nuclear medicine imaging, focusing on the Anger camera and single photon emission computed tomography (SPECT) technologies. In this context, key reference is made to the physics of detection in Anger camera systems, especially key components such as the sodium iodide crystal, the function and performance of photomultiplier tubes, and the collimator design. It is discovered that within each component of technology, there are fundamental physical relationships that govern the performance of each component, and that overall image quality is the result of the complex interaction of all such elements. The increasing use of SPECT/CT imaging is described and illustrated with a range of typical clinical applications, which include brain, spinal, cardiac, and cancer studies. The use of CT imaging functionality allows for SPECT image correction based on compensation for absorption within tissue. Reference is also made to the basics of positron emission tomography (PET) imaging and, in particular, to the integration of PET/CT systems where the anatomy profile of the CT image is used to provide correction for photon absorption. A summary is provided of the radionuclides and radiopharmaceuticals commonly used in PET/CT imaging and a range of image studies referenced includes those of nasopharyngeal carcinoma, lung cancer investigation, brain investigation (cancer detection and dementia) and cardiac function. Reference is made to the development of "time of flight" (TOF) technology for improving of image resolution in PET/CT systems. Furthermore, SPECT/CT and PET/CT imaging systems are compared, where a key factor identified is the significantly higher number of photons detected with PET/CT technology and improved image resolution.

Fast Energy Dependent Scatter Correction for List-Mode PET Data

Improvements in energy resolution of modern positron emission tomography (PET) detectors have created opportunities to implement energy-based scatter correction algorithms. Here, we use the energy information of auxiliary windows to estimate the scatter component. Our method is directly implemented in an iterative reconstruction algorithm, generating a scatter-corrected image without the need for sinograms. The purpose was to implement a fast energy-based scatter correction method on list-mode PET data, when it was not possible to use an attenuation map as a practical approach for the scatter degradation. The proposed method was evaluated using Monte Carlo simulations of various digital phantoms. It accurately estimated the scatter fraction distribution, and improved the image contrast in the simulated studied cases. We conclude that the proposed scatter correction method could effectively correct the scattered events, including multiple scatters and those originated in sources outside the field of view.

Simultaneous SPECT imaging with 123I and 125I - a practical approach to assessing a drug and its carrier at the same time with dual imaging

Radiolabeling of a drug with radioactive iodine is a good method to determine its pharmacokinetics and biodistribution in vivo that only minimally alters its physicochemical properties. With dual labeling, using the two radioactive iodine isotopes ¹²³I and ¹²⁵I, two different drugs can be evaluated at the same time, or one can follow both a drug and its drug delivery system using a single photon emission computed tomography (SPECT) imager. One difficulty is that the two radioisotopes have overlapping gamma spectra. Our aim was therefore to develop a technique that overcomes this problem and allows for quantitative analysis of the two radioisotopes present at varied isotope ratios. For this purpose, we developed a simple method that included scatter and attenuation corrections and fully compensated for ¹²³I/¹²⁵I crosstalk, and then tested it in phantom measurements. The method was applied to the study of an orally administered lipid formulation for the delivery of fenofibrate in rats. To directly compare a traditional study, where fenofibrate was determined in plasma samples to SPECT imaging with ¹²³I-labeled fenofibrate and ¹²⁵I-labeled triolein over 24 h, the drug concentrations were converted to standardized uptake values (SUVs), an unusual unit for pharmaceutical scientists, but the standard unit for radiologists. A generally good agreement between the traditional and the radioactive imaging method was found in the pharmacokinetics and biodistribution results. Small differences are discussed in detail. Overall, SPECT imaging is an excellent method to pilot a new formulation with just a few animals, replaces blood sampling, and can very quickly highlight potential administration problems, the excretion pathways and the kinetics. Furthermore, dual labeling with the two radioisotopes ¹²³I and ¹²⁵I clearly shows if a drug and its drug delivery system stay together when traveling through the body, if slow drug release takes place, and where degradation/excretion of the components occurs.

Photon quantum entanglement in the MeV regime and its application in PET imaging

Positron Emission Tomography (PET) is a widely-used imaging modality for medical research and clinical diagnosis. Imaging of the radiotracer is obtained from the detected hit positions of the two positron annihilation photons in a detector array. The image is degraded by backgrounds from random coincidences and in-patient scatter events which require correction. In addition to the geometric information, the two annihilation photons are predicted to be produced in a quantum-entangled state, resulting in enhanced correlations between their subsequent interaction processes. To explore this, the predicted entanglement in linear polarisation for the two photons was incorporated into a simulation and tested by comparison with experimental data from a cadmium zinc telluride (CZT) PET demonstrator apparatus. Adapted apparati also enabled correlation measurements where one of the photons had undergone a prior scatter process. We show that the entangled simulation describes the measured correlations and, through simulation of a larger preclinical PET scanner, illustrate a simple method to quantify and remove the unwanted backgrounds in PET using the quantum entanglement information alone.

Quantitative Molecular Positron Emission Tomography Imaging Using Advanced Deep Learning Techniques

The widespread availability of high-performance computing and the popularity of artificial intelligence (AI) with machine learning and deep learning (ML/DL) algorithms at the helm have stimulated the development of many applications involving the use of AI-based techniques in molecular imaging research. Applications reported in the literature encompass various areas, including innovative design concepts in positron emission tomography (PET) instrumentation, quantitative image reconstruction and analysis techniques, computer-aided detection and diagnosis, as well as modeling and prediction of outcomes. This review reflects the tremendous interest in quantitative molecular imaging using ML/DL techniques during the past decade, ranging from the basic principles of ML/DL techniques to the various steps required for obtaining quantitatively accurate PET data, including algorithms used to denoise or correct for physical degrading factors as well as to quantify tracer uptake and metabolic tumor volume for treatment monitoring or radiation therapy treatment planning and response prediction.This review also addresses future opportunities and current challenges facing the adoption of ML/DL approaches and their role in multimodality imaging.

A Monte Carlo simulation study of scatter fraction and the impact of patient BMI on scatter in long axial field-of-view PET scanners

The NEMA NU-2 standard describes a protocol for measurement of scatter fraction (SF) using an axially-aligned line source, offset at 45mm from the central axis, in a cylindrical polyethylene phantom. In this work, which is an extension of our preliminary results previuosly published in the Proceedings of IEEE NSS/MIC 2018 [1], we aim to evaluate the performance of the NEMA NU-2 SF protocol in a Siemens Biograph mCT PET/CT whole-body scanner and a long axial field-of-view (LAFOV) total-body PET scanner to determine whether modifications to the NEMA NU-2 SF protocol are needed for the characterisation of scatter in such scanners. In addition, we evaluate the impact of patient body mass index (BMI) on SF in a LAFOV scanner. The Siemens Biograph mCT and a typical LAFOV PET scanner were modelled in GATE. Monte Carlo simulations were performed to validate the mCT scanner model against published experimental results. SF was estimated using a modified NEMA NU-2 protocol with variable radial offsets on both scanners and compared to ground truth SF measurements obtained with a uniform-activity cylindrical phantom. Correlation between BMI and SF in the LAFOV scanner was evaluated by simulating anthropomorphic phantoms with different BMIs and realistic ¹⁸F-FDG distributions, together with uniformly-filled 200cm long cylindrical phantoms with equivalent effective diameters. The optimal offset was found to be either 60mm or 80mm, depending on the chosen optimality metric. We conclude that modifications to NEMA NU-2 are required for accurate SF characterisation in whole-body and LAFOV scanners. Finally, SF in anthropomorphic phantoms with realistic tissue concentrations of ¹⁸F-FDG was found to be strongly correlated with SF in an equivalent-volume cylindrical phantom for the LAFOV PET scanner; BMI was also found to strongly positively correlate with the SF.

Deep learning-based metal artefact reduction in PET/CT imaging

Objectives

The susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning–based metal artefact reduction (MAR) in quantitative PET/CT imaging.

Methods

Deep learning–based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty ¹⁸F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.

Results

The evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.

Conclusion

The DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.

Key Points

• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images.

• The aim of this work is to develop and evaluate a deep learning–based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging.

• Deep learning–based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-07709-z.

Small-angle Compton Scatter Artifact in Tc-99m-IDA Hepatobiliary Scintigraphy Resulting in the Breast Overlying the Liver in Planar Dynamic Imaging

Compton scatter photons are generally considered problematic in nuclear medicine imaging. Therefore, efforts are being made to minimize the involvement of these photons by employing some special strategies in daily practice. Basically, photons scattering at a small angle and traveling in the proper direction stand a chance of getting recorded and thereby participate in the image formation. These photons may create artifactual hot spots in the vicinity of a region with high concentration of radioactivity. The present study focuses on the negative impact of such photons during routine imaging in clinical setting, through an artifact detected in technetium-99m-IDA hepatobiliary scintigraphy, with the purpose of highlighting this issue to the nuclear medicine practitioners.

TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO

Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO-Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies

PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using 18 F-FDG, 18 F-DOPA, 18 F-Flortaucipir (targeting tau pathology), and 18 F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT-based AC (CTAC) to generate reference PET-CTAC and without AC to produce PET-nonAC images. A deep convolutional neural network was trained to generate PET attenuation corrected images (PET-DLAC) from PET-nonAC. The quantitative accuracy of this approach was investigated separately for each radiotracer considering the values obtained from PET-CTAC images as reference. A segmented AC map (PET-SegAC) containing soft-tissue and background air was also included in the evaluation. Quantitative analysis of PET images demonstrated superior performance of the DLAC approach compared to SegAC technique for all tracers. Despite the relatively low quantitative bias observed when using the DLAC approach, this approach appears vulnerable to outliers, resulting in noticeable local pseudo uptake and false cold regions. Direct AC in image-space using deep learning demonstrated quantitatively acceptable performance with less than 9% absolute SUV bias for the four different investigated neuroimaging radiotracers. However, this approach is vulnerable to outliers which result in large local quantitative bias.

Quantitative SPECT/CT-Technique and Clinical Applications

The continuous development of SPECT over the past 50 years has led to improved image quality and increased diagnostic confidence. The most influential developments include the realization of hybrid SPECT/CT devices, as well as the implementation of attenuation correction and iterative image reconstruction techniques. These developments have led to a preference for SPECT/CT devices over SPECT-only systems and to the widespread adoption of the former, strengthening the role of SPECT/CT as the workhorse of Nuclear Medicine imaging. New trends in the ongoing development of SPECT/CT are diverse. For example, whole-body SPECT/CT images, consisting of acquisitions from multiple consecutive bed positions in the manner of PET/CT, are increasingly performed. Additionally, in recent years, some interesting approaches in detector technology have found their way into commercial products. For example, some SPECT cameras dedicated to specific organs employ semiconductor detectors made of cadmium telluride or cadmium zinc telluride, which have been shown to increase the obtainable image quality by offering a higher sensitivity and energy resolution. However, the advent of quantitative SPECT/CT which, like PET, can quantify the amount of tracer in terms of Bq/mL or as a standardized uptake value could be regarded as most important development. It is a major innovation that will lead to increased diagnostic accuracy and confidence, especially in longitudinal studies and in the monitoring of treatment response. The current work comprises two main aspects. At first, physical and technical fundamentals of SPECT image formation are described and necessary prerequisites of quantitative SPECT/CT are reviewed. Additionally, the typically achievable quantitative accuracy based on reports from the literature is given. Second, an extensive list of studies reporting on clinical applications of quantitative SPECT/CT is provided and reviewed.

Denoising of iodine-131 images using a median filter

OBJECTIVE

An iodine-131 (I) image visually appears to be contaminated with impulse noise. The two-dimensional median filter removes noise without sacrificing the edge information. Its performance depends on the shape and size of the mask. In this study, we have compared the performance of a plus-shape and a square-shape median filter for I whole-body images and found the filter with optimum parameter that improves I image quality acceptable to nuclear medicine physicians.

MATERIALS AND METHODS

A total of 150 whole-body I images were exported in DICOM format. These images were converted into PNG format and processed with a plus-shape and a square-shape median filter, with each shape mask having sizes of 3, 5, 7, and 9 pixels. The quality of the processed images was assessed by visual assessment by two nuclear medicine physicians and also quantitatively by evaluating metrics: mutual information, mean square error, peak signal-to-noise ratio, and difference entropy. Nuclear medicine physicians assigned a score to each image on the scale 1 (lowest) to 5 (highest) for image quality on the basis of the noise removal, smoothness, and edge information available in the image. Student's t-test was carried out to find the significant difference in the image quality (α=0.05) between the processed images with square-shape and cross-shape mask with the same pixel size. All experiments including statistical analysis were conducted using R installed on a personal computer.

RESULTS

Both median filters improved the image quality of I images. The plus-shape median filter was found to show better performance in comparison with the square-shape median filter (P<0.001). The plus-shape median filter with a mask size of 7 pixels was found to be optimum for the processing of whole-body I images.

CONCLUSION

The plus-shape median filter with a mask size of 7 pixels can be used to process whole-body I scintigraphic images without loss of clinical information.

Implementation of GPU accelerated SPECT reconstruction with Monte Carlo-based scatter correction

OBJECTIVE

Statistical SPECT reconstruction can be very time-consuming especially when compensations for collimator and detector response, attenuation, and scatter are included in the reconstruction. This work proposes an accelerated SPECT reconstruction algorithm based on graphics processing unit (GPU) processing.

METHODS

Ordered subset expectation maximization (OSEM) algorithm with CT-based attenuation modelling, depth-dependent Gaussian convolution-based collimator-detector response modelling, and Monte Carlo-based scatter compensation was implemented using OpenCL. The OpenCL implementation was compared against the existing multi-threaded OSEM implementation running on a central processing unit (CPU) in terms of scatter-to-primary ratios, standardized uptake values (SUVs), and processing speed using mathematical phantoms and clinical multi-bed bone SPECT/CT studies.

RESULTS

The difference in scatter-to-primary ratios, visual appearance, and SUVs between GPU and CPU implementations was minor. On the other hand, at its best, the GPU implementation was noticed to be 24 times faster than the multi-threaded CPU version on a normal 128 × 128 matrix size 3 bed bone SPECT/CT data set when compensations for collimator and detector response, attenuation, and scatter were included.

CONCLUSIONS

GPU SPECT reconstructions show great promise as an every day clinical reconstruction tool.

Characterization of Compton-scatter imaging with an analytical simulation method

By collimating the photons scattered when a megavoltage therapy beam interacts with the patient, a Compton-scatter image may be formed without the delivery of an extra dose. To characterize and assess the potential of the technique, an analytical model for simulating scatter images was developed and validated against Monte Carlo (MC). For three phantoms, the scatter images collected during irradiation with a 6 MV flattening-filter-free therapy beam were simulated. Images, profiles, and spectra were compared for different phantoms and different irradiation angles. The proposed analytical method simulates accurate scatter images up to 1000 times faster than MC. Minor differences between MC and analytical simulated images are attributed to limitations in the isotropic superposition/convolution algorithm used to analytically model multiple-order scattering. For a detector placed at 90° relative to the treatment beam, the simulated scattered photon energy spectrum peaks at 140-220 keV, and 40-50% of the photons are the result of multiple scattering. The high energy photons originate at the beam entrance. Increasing the angle between source and detector increases the average energy of the collected photons and decreases the relative contribution of multiple scattered photons. Multiple scattered photons cause blurring in the image. For an ideal 5 mm diameter pinhole collimator placed 18.5 cm from the isocenter, 10 cGy of deposited dose (2 Hz imaging rate for 1200 MU min^-1 treatment delivery) is expected to generate an average 1000 photons per mm² at the detector. For the considered lung tumor CT phantom, the contrast is high enough to clearly identify the lung tumor in the scatter image. Increasing the treatment beam size perpendicular to the detector plane decreases the contrast, although the scatter subject contrast is expected to be greater than the megavoltage transmission image contrast. With the analytical method, real-time tumor tracking may be possible through comparison of simulated and acquired patient images.

Towards enhanced PET quantification in clinical oncology

Positron emission tomography (PET) has, since its inception, established itself as the imaging modality of choice for the in vivo quantitative assessment of molecular targets in a wide range of biochemical processes underlying tumour physiology. PET image quantification enables to ascertain a direct link between the time-varying activity concentration in organs/tissues and the fundamental parameters portraying the biological processes at the cellular level being assessed. However, the quantitative potential of PET may be affected by a number of factors related to physical effects, hardware and software system specifications, tracer kinetics, motion, scan protocol design and limitations in current image-derived PET metrics. Given the relatively large number of PET metrics reported in the literature, the selection of the best metric for fulfilling a specific task in a particular application is still a matter of debate. Quantitative PET has advanced elegantly during the last two decades and is now reaching the maturity required for clinical exploitation, particularly in oncology where it has the capability to open many avenues for clinical diagnosis, assessment of response to treatment and therapy planning. Therefore, the preservation and further enhancement of the quantitative features of PET imaging is crucial to ensure that the full clinical value of PET imaging modality is utilized in clinical oncology. Recent advancements in PET technology and methodology have paved the way for faster PET acquisitions of enhanced sensitivity to support the clinical translation of highly quantitative four-dimensional (4D) parametric imaging methods in clinical oncology. In this report, we provide an overview of recent advances and future trends in quantitative PET imaging in the context of clinical oncology. The pros/cons of the various image-derived PET metrics will be discussed and the promise of novel methodologies will be highlighted.

Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with 15O-Gas Inhalation

In 3-dimensional PET/CT imaging of the brain with ¹⁵O-gas inhalation, high radioactivity in the face mask creates cold artifacts and affects the quantitative accuracy when scatter is corrected by conventional methods (e.g., single-scatter simulation [SSS] with tail-fitting scaling [TFS-SSS]). Here we examined the validity of a newly developed scatter-correction method that combines SSS with a scaling factor calculated by Monte Carlo simulation (MCS-SSS). Methods: We performed phantom experiments and patient studies. In the phantom experiments, a plastic bottle simulating a face mask was attached to a cylindric phantom simulating the brain. The cylindric phantom was filled with ¹⁸F-FDG solution (3.8-7.0 kBq/mL). The bottle was filled with nonradioactive air or various levels of ¹⁸F-FDG (0-170 kBq/mL). Images were corrected either by TFS-SSS or MCS-SSS using the CT data of the bottle filled with nonradioactive air. We compared the image activity concentration in the cylindric phantom with the true activity concentration. We also performed ¹⁵O-gas brain PET based on the steady-state method on patients with cerebrovascular disease to obtain quantitative images of cerebral blood flow and oxygen metabolism. Results: In the phantom experiments, a cold artifact was observed immediately next to the bottle on TFS-SSS images, where the image activity concentrations in the cylindric phantom were underestimated by 18%, 36%, and 70% at the bottle radioactivity levels of 2.4, 5.1, and 9.7 kBq/mL, respectively. At higher bottle radioactivity, the image activity concentrations in the cylindric phantom were greater than 98% underestimated. For the MCS-SSS, in contrast, the error was within 5% at each bottle radioactivity level, although the image generated slight high-activity artifacts around the bottle when the bottle contained significantly high radioactivity. In the patient imaging with ¹⁵O₂ and C¹⁵O₂ inhalation, cold artifacts were observed on TFS-SSS images, whereas no artifacts were observed on any of the MCS-SSS images. Conclusion: MCS-SSS accurately corrected the scatters in ¹⁵O-gas brain PET when the 3-dimensional acquisition mode was used, preventing the generation of cold artifacts, which were observed immediately next to a face mask on TFS-SSS images. The MCS-SSS method will contribute to accurate quantitative assessments.

Gamma camera calibrations for the Italian multicentre study for lesion dosimetry in 223Ra therapy of bone metastases

PURPOSE

The aim was to calibrate gamma cameras in the framework of the Italian multicentre study for lesion dosimetry in ²²³Ra therapy of bone metastases. Equipments of several manufacturers and different models were used.

METHODS

Eleven gamma cameras (3/8- and 5/8-inch crystal) were used, acquiring planar static images with double-peak (82 and 154keV, 20% wide) and MEGP collimator. The sensitivity was measured in air, varying source-detector distance and source size. Transmission curves were measured, calculating the parameters used for attenuation/scatter correction with the pseudo-extrapolation number method, and assessing their variations with the source size.

RESULTS

Values of the calibration factor (geometric mean of both detector sensitivities) ranged from 41.1 to 113.9cps/MBq. For the smallest source (diameter of 3.5cm), the calibration factor decrease ranged from -30% to -4%, highlighting the importance of partial volume effects according to the equipment involved. The sensitivity variation with the source-detector distance, with respect to the 15cm-value, reached 10% (in absolute value) in the range 5-30cm, but fixing the distance between the two heads, the calibration factor variation with the distance from the midline was within 3.6%. Appreciable variation of the transmission curves with the source size were observed, examining the results obtained with six gamma cameras.

CONCLUSION

Assessments of sensitivity and transmission curve variations with source size should be regularly implemented in calibration procedures. The results of this study represent a useful compendium to check the obtained calibrations for dosimetric purposes.

Technical Pitfalls and Limitations of SPECT/CT

The synergy of functional and anatomic information in hybrid systems has undoubtedly enhanced the diagnostic potential of radionuclide imaging in recent years, contributing to the advancement of SPECT/CT in clinical practice. Since the introduction of commercial SPECT/CT in the late 1990 s, the field has seen rapid expansion and development toward multidetector CT subsystems, establishing the role of SPECT/CT as a routine imaging tool. It is, however, important to discuss possible challenges and technical limitations of such systems and how these influence imaging outcomes. In particular, the issues of patient motion and spatial misalignment of the SPECT and CT modalities, data corrections such as those for photon attenuation, and the choice of CT acquisition protocols in relation to radiation exposure are discussed in the article.

Uptake and retention of manganese contrast agents for PET and MRI in the rodent brain

Manganese-enhanced magnetic resonance imaging (MRI) is an established neuroimaging method for signal enhancement, tract tracing, and functional studies in rodents. Along with the increasing availability of combined positron emission tomography (PET) and MRI scanners, the recent development of the positron-emitting isotope ⁵² Mn has prompted interest in the use of Mn²⁺ as a dual-modality contrast agent. In this work, we characterized and compared the uptake of systemically delivered Mn²⁺ and radioactive ⁵² Mn²⁺ in the rat brain for MRI and PET, respectively. Additionally, we examined the biodistribution of two formulations of ⁵² Mn²⁺ in the rat. In MRI, maximum uptake was observed one day following delivery of the highest MnCl₂ dose tested (60 mg/kg), with some brain regions showing delayed maximum enhancement 2-4 days following delivery. In PET, we observed low brain uptake after systemic delivery, with a maximum of approximately 0.2% ID/g. We also studied the effect of final formulation vehicle (saline compared with MnCl₂ ) on ⁵² Mn²⁺ organ biodistribution and brain uptake. We observed that the addition of bulk Mn²⁺ carrier to ⁵² Mn²⁺ in solution resulted in significantly reduced ⁵² Mn²⁺ uptake in the majority of organs, including the brain. These results lay the groundwork for further development of ⁵² Mn PET or dual Mn-enhanced PET-MR neuroimaging in rodents, and indicate several interesting potential applications of ⁵² Mn PET in other organs and systems. Copyright © 2016 John Wiley & Sons, Ltd.

The Use of Anatomical Information for Molecular Image Reconstruction Algorithms: Attenuation/Scatter Correction, Motion Compensation, and Noise Reduction

PET and SPECT are important tools for providing valuable molecular information about patients to clinicians. Advances in nuclear medicine hardware technologies and statistical image reconstruction algorithms enabled significantly improved image quality. Sequentially or simultaneously acquired anatomical images such as CT and MRI from hybrid scanners are also important ingredients for improving the image quality of PET or SPECT further. High-quality anatomical information has been used and investigated for attenuation and scatter corrections, motion compensation, and noise reduction via post-reconstruction filtering and regularization in inverse problems. In this article, we will review works using anatomical information for molecular image reconstruction algorithms for better image quality by describing mathematical models, discussing sources of anatomical information for different cases, and showing some examples.

Attenuation correction in emission tomography using the emission data--A review

The problem of attenuation correction (AC) for quantitative positron emission tomography (PET) had been considered solved to a large extent after the commercial availability of devices combining PET with computed tomography (CT) in 2001; single photon emission computed tomography (SPECT) has seen a similar development. However, stimulated in particular by technical advances toward clinical systems combining PET and magnetic resonance imaging (MRI), research interest in alternative approaches for PET AC has grown substantially in the last years. In this comprehensive literature review, the authors first present theoretical results with relevance to simultaneous reconstruction of attenuation and activity. The authors then look back at the early history of this research area especially in PET; since this history is closely interwoven with that of similar approaches in SPECT, these will also be covered. We then review algorithmic advances in PET, including analytic and iterative algorithms. The analytic approaches are either based on the Helgason-Ludwig data consistency conditions of the Radon transform, or generalizations of John's partial differential equation; with respect to iterative methods, we discuss maximum likelihood reconstruction of attenuation and activity (MLAA), the maximum likelihood attenuation correction factors (MLACF) algorithm, and their offspring. The description of methods is followed by a structured account of applications for simultaneous reconstruction techniques: this discussion covers organ-specific applications, applications specific to PET/MRI, applications using supplemental transmission information, and motion-aware applications. After briefly summarizing SPECT applications, we consider recent developments using emission data other than unscattered photons. In summary, developments using time-of-flight (TOF) PET emission data for AC have shown promising advances and open a wide range of applications. These techniques may both remedy deficiencies of purely MRI-based AC approaches in PET/MRI and improve standalone PET imaging.

Evaluation of scatter limitation correction: a new method of correcting photopenic artifacts caused by patient motion during whole-body PET/CT imaging

OBJECTIVE

Overcorrection of scatter caused by patient motion during whole-body PET/computed tomography (CT) imaging can induce the appearance of photopenic artifacts in the PET images. The present study aimed to quantify the accuracy of scatter limitation correction (SLC) for eliminating photopenic artifacts.

METHODS

This study analyzed photopenic artifacts in (18)F-fluorodeoxyglucose ((18)F-FDG) PET/CT images acquired from 12 patients and from a National Electrical Manufacturers Association phantom with two peripheral plastic bottles that simulated the human body and arms, respectively. The phantom comprised a sphere (diameter, 10 or 37 mm) containing fluorine-18 solutions with target-to-background ratios of 2, 4, and 8. The plastic bottles were moved 10 cm posteriorly between CT and PET acquisitions. All PET data were reconstructed using model-based scatter correction (SC), no scatter correction (NSC), and SLC, and the presence or absence of artifacts on the PET images was visually evaluated. The SC and SLC images were also semiquantitatively evaluated using standardized uptake values (SUVs).

RESULTS

Photopenic artifacts were not recognizable in any NSC and SLC image from all 12 patients in the clinical study. The SUVmax of mismatched SLC PET/CT images were almost equal to those of matched SC and SLC PET/CT images. Applying NSC and SLC substantially eliminated the photopenic artifacts on SC PET images in the phantom study. SLC improved the activity concentration of the sphere for all target-to-background ratios. The highest %errors of the 10 and 37-mm spheres were 93.3 and 58.3%, respectively, for mismatched SC, and 73.2 and 22.0%, respectively, for mismatched SLC.

CONCLUSION

Photopenic artifacts caused by SC error induced by CT and PET image misalignment were corrected using SLC, indicating that this method is useful and practical for clinical qualitative and quantitative PET/CT assessment.

Evaluation of Compton attenuation and photoelectric absorption coefficients by convolution of scattering and primary functions and counts ratio on energy spectra

Purpose:

Estimation of Compton attenuation and the photoelectric absorption coefficients were explored at various depths.

Methods:

A new method was proposed for estimating the depth based on the convolution of two exponential functions, namely convolution of scattering and primary functions (CSPF), which the convolved result will conform to the photopeak region of energy spectrum with the variable energy-window widths (EWWs) and a theory on the scattering cross-section. The triple energy-windows (TEW) and extended triple energy-windows scatter correction (ETEW) methods were used to estimate the scattered and primary photons according to the energy spectra at various depths due to a better performance than the other methods in nuclear medicine. For this purpose, the energy spectra were employed, and a distinct phantom along with a technetium-99 m source was simulated by Monte Carlo method.

Results:

The simulated results indicate that the EWW, used to calculate the scattered and primary counts in terms of the integral operators on the functions, was proportional to the depth as an exponential function. The depth will be calculated by the combination of either TEW or ETEW and proposed method resulting in the distinct energy-window. The EWWs for primary photons were in good agreement with those of scattered photons at the same as depths. The average errors between these windows for both methods TEW, and ETEW were 7.25% and 6.03% at different depths, respectively. The EWW value for functions of scattered and primary photons was reduced by increasing the depth in the CSPF method.

Conclusions:

This coefficient may be an index for the scattering cross-section.

Scatter characterization and correction for simultaneous multiple small-animal PET imaging

PURPOSE

The rapid growth and usage of small-animal positron emission tomography (PET) in molecular imaging research has led to increased demand on PET scanner's time. One potential solution to increase throughput is to scan multiple rodents simultaneously. However, this is achieved at the expense of deterioration of image quality and loss of quantitative accuracy owing to enhanced effects of photon attenuation and Compton scattering. The purpose of this work is, first, to characterize the magnitude and spatial distribution of the scatter component in small-animal PET imaging when scanning single and multiple rodents simultaneously and, second, to assess the relevance and evaluate the performance of scatter correction under similar conditions.

METHODS

The LabPET™-8 scanner was modelled as realistically as possible using Geant4 Application for Tomographic Emission Monte Carlo simulation platform. Monte Carlo simulations allow the separation of unscattered and scattered coincidences and as such enable detailed assessment of the scatter component and its origin. Simple shape-based and more realistic voxel-based phantoms were used to simulate single and multiple PET imaging studies. The modelled scatter component using the single-scatter simulation technique was compared to Monte Carlo simulation results. PET images were also corrected for attenuation and the combined effect of attenuation and scatter on single and multiple small-animal PET imaging evaluated in terms of image quality and quantitative accuracy.

RESULTS

A good agreement was observed between calculated and Monte Carlo simulated scatter profiles for single- and multiple-subject imaging. In the LabPET™-8 scanner, the detector covering material (kovar) contributed the maximum amount of scatter events while the scatter contribution due to lead shielding is negligible. The out-of field-of-view (FOV) scatter fraction (SF) is 1.70, 0.76, and 0.11% for lower energy thresholds of 250, 350, and 400 keV, respectively. The increase in SF ranged between 25 and 64% when imaging multiple subjects (three to five) of different size simultaneously in comparison to imaging a single subject. The spill-over ratio (SOR) increases with increasing the number of subjects in the FOV. Scatter correction improved the SOR for both water and air cold compartments of single and multiple imaging studies. The recovery coefficients for different body parts of the mouse whole-body and rat whole-body anatomical models were improved for multiple imaging studies following scatter correction.

CONCLUSIONS

The magnitude and spatial distribution of the scatter component in small-animal PET imaging of single and multiple subjects simultaneously were characterized, and its impact was evaluated in different situations. Scatter correction improves PET image quality and quantitative accuracy for single rat and simultaneous multiple mice and rat imaging studies, whereas its impact is insignificant in single mouse imaging.

Quantitative assessment of rest and acetazolamide CBF using quantitative SPECT reconstruction and sequential administration of (123)I-iodoamphetamine: comparison among data acquired at three institutions

PURPOSE

A recently developed technique which reconstructs quantitative images from original projection data acquired using existing single-photon emission computed tomography (SPECT) devices enabled quantitative assessment of cerebral blood flow (CBF) at rest and after acetazolamide challenge. This study was intended to generate a normal database and to investigate its inter-institutional consistency.

METHODS

The three institutions carried out a series of SPECT scanning on 32 healthy volunteers, following a recently proposed method that involved dual administration of (123)I-iodoamphetamine during a single SPECT scan. Intra-institute and inter-institutional variations of regional CBF values were evaluated both at rest and after acetazolamide challenge. Functional images were pooled for both rest and acetazolamide CBF, and inter-institutional difference was evaluated among these images using two independent software programs.

RESULTS

Quantitative assessment of CBF images at rest and after acetazolamide was successfully achieved with the given protocol in all institutions. Intra-institutional variation of CBF values at rest and after acetazolamide was consistent with previously reported values. Quantitative CBF values showed no significant difference among institutions in all regions, except for a posterior cerebral artery region after acetazolamide challenge in one institution which employed SPECT device with lowest spatial resolution. Pooled CBF images at rest and after acetazolamide generated using two software programs showed no institutional differences after equalization of the spatial resolution.

CONCLUSIONS

SPECT can provide reproducible images from projection data acquired using different SPECT devices. A common database acquired at different institutions may be shared among institutions, if images are reconstructed using a quantitative reconstruction program, and acquired by following a standardized protocol.

Quantification of rat brain SPECT with (123)I-ioflupane: evaluation of different reconstruction methods and image degradation compensations using Monte Carlo simulation

SPECT studies with (123)I-ioflupane facilitate the diagnosis of Parkinson's disease (PD). The effect on quantification of image degradations has been extensively evaluated in human studies but their impact on studies of experimental PD models is still unclear. The aim of this work was to assess the effect of compensating for the degrading phenomena on the quantification of small animal SPECT studies using (123)I-ioflupane. This assessment enabled us to evaluate the feasibility of quantitatively detecting small pathological changes using different reconstruction methods and levels of compensation for the image degrading phenomena. Monte Carlo simulated studies of a rat phantom were reconstructed and quantified. Compensations for point spread function (PSF), scattering, attenuation and partial volume effect were progressively included in the quantification protocol. A linear relationship was found between calculated and simulated specific uptake ratio (SUR) in all cases. In order to significantly distinguish disease stages, noise-reduction during the reconstruction process was the most relevant factor, followed by PSF compensation. The smallest detectable SUR interval was determined by biological variability rather than by image degradations or coregistration errors. The quantification methods that gave the best results allowed us to distinguish PD stages with SUR values that are as close as 0.5 using groups of six rats to represent each stage.

An introduction to molecular imaging in radiation oncology: a report by the AAPM Working Group on Molecular Imaging in Radiation Oncology (WGMIR)

Molecular imaging is the direct or indirect noninvasive monitoring and recording of the spatial and temporal distribution of in vivo molecular, genetic, and/or cellular processes for biochemical, biological, diagnostic, or therapeutic applications. Molecular images that indicate the presence of malignancy can be acquired using optical, ultrasonic, radiologic, radionuclide, and magnetic resonance techniques. For the radiation oncology physicist in particular, these methods and their roles in molecular imaging of oncologic processes are reviewed with respect to their physical bases and imaging characteristics, including signal intensity, spatial scale, and spatial resolution. Relevant molecular terminology is defined as an educational assist. Current and future clinical applications in oncologic diagnosis and treatment are discussed. National initiatives for the development of basic science and clinical molecular imaging techniques and expertise are reviewed, illustrating research opportunities in as well as the importance of this growing field.

Quantitative simultaneous 111In∕99mTc SPECT-CT of osteomyelitis

PURPOSE

A well-established approach for diagnostic imaging of osteomyelitis (OM), a bone infection, is simultaneous SPECT-CT of 99mTc sulfur colloid (SC) and 111In white blood cells (WBC). This method provides essentially perfect spatial registration of the tracers within anatomic sites of interest. Currently, diagnosis is based purely on a visual assessment-where relative discordance between 99mTc and 111In uptake in bone, i.e., high 111In and low 99mTc, suggests OM. To achieve more quantitative images, noise, scatter, and crosstalk between radionuclides must be addressed through reconstruction. Here the authors compare their Monte Carlo-based joint OSEM (MC-JOSEM) algorithm, which reconstructs both radionuclides simultaneously, to a more conventional triple-energy window-based reconstruction (TEW-OSEM), and to iterative reconstruction with no compensation for scatter (NC-OSEM).

METHODS

The authors created numerical phantoms of the foot and torso. Multiple bone-infection sites were modeled using high-count Monte Carlo simulation. Counts per voxel were then scaled to values appropriate for 111In WBC and 99mTc SC imaging. Ten independent noisy projection image sets were generated by drawing random Poisson deviates from these very low-noise images. Data were reconstructed using the two iterative scatter-compensation methods, TEW-OSEM and MC-JOSEM, as well as the uncorrected method (NC-OSEM). Mean counts in volumes of interest (VOIs) were used to evaluate the bias and precision of each method. Data were also acquired using a phantom, approximately the size of an adult ankle, consisting of regions representing infected and normal bone marrow, within a bone-like attenuator and surrounding soft tissue; each compartment contained a mixture of 111In and 99mTc. Low-noise data were acquired during multiple short scans over 29 h on a Siemens Symbia T6 SPECT-CT with medium-energy collimators. Pure 99mTc and 111In projection datasets were derived by fitting the acquired projections to the sum of 99mTc and 111In contributions, using the known half-lives. Uncontaminated data were scaled and recombined into six datasets with different activity ratios; ten Poisson noise realizations were then generated for each ratio. VOIs in each of the compartments were used to evaluate the bias and precision of each method with respect to reconstructions of uncontaminated datasets. In addition to the simulated and acquired phantom images, the authors reconstructed patient images with MC-JOSEM and TEW-OSEM. Patient reconstructions were assessed qualitatively for lesion contrast, spatial definition, and scatter.

RESULTS

For all simulated and acquired infection phantoms, the root-mean squared-error of measured 99mTc activity was significantly improved with MC-JOSEM and TEW-OSEM in comparison to NC-OSEM reconstructions. While MC-JOSEM trended toward outperforming TEW-OSEM, the improvement was only found to be significant (p<0.001) for the acquired bone phantom in which a wide range of 111In∕99mTc concentration ratios were tested. In all cases, scatter correction did not significantly improve 111In quantitation.

CONCLUSIONS

Compensation for scatter and crosstalk is useful for improving quality, bias, and precision of 99mTc activity estimates in simultaneous dual-radionuclide imaging of OM. The use of the more rigorous MC-based estimates provided marginal improvements over TEW. While the phantom results were encouraging, more subjects are needed to evaluate the usefulness of quantitative 111In∕99mTc SPECT-CT in the clinic.

Comparison of attenuation, dual-energy-window, and model-based scatter correction of low-count SPECT to 82Rb PET/CT quantified myocardial perfusion scores

BACKGROUND

New reconstruction algorithms allow reduction in acquisition times or the amount of injected radioactivity. We examined the impact of different corrections on low-count clinical SPECT myocardial perfusion images (MPI) and compared to (82)Rb PET/CT. We compared no corrections (NC) to attenuation correction (AC) with and without scatter correction by either a dual-energy-window (AC-DEW) or model-based (AC-ESSE) approach. All reconstructions included resolution recovery.

METHODS

56 patients were imaged using a standard rest/stress Tc-99m-tetrofosmin MPI SPECT/CT protocol with an additional half-time acquisition. A (82)Rb-rest/stress PET/CT MPI was acquired within 4 weeks. Reconstruction methods were compared using summed rest/stress/difference scores from an objective algorithm (SRS/SSS/SDS).

RESULTS

The SRS and SSS for NC were significantly (P < .01) higher than for AC, but well correlated (r ≥ 0.87). The correlation in SRS/SSS among AC, AC-DEW, and AC-ESSE was excellent (r ≥ 0.98). AC-ESSE and AC-DEW had higher SRS (P ≤ .05) than AC, but the SDS values were not significantly different. Concordance with PET normal/abnormal classification was 76% for NC and ≥85% for the AC methods.

CONCLUSION

AC significantly improves the accuracy of low-count myocardial perfusion SPECT half-time imaging for the detection of disease compared to NC. Compared to PET, there was no significant difference among AC, AC-DEW, and AC-ESSE.

Attenuation correction for small animal PET images: a comparison of two methods

In order to extract quantitative parameters from PET images, several physical effects such as photon attenuation, scatter, and partial volume must be taken into account. The main objectives of this work were the evaluation of photon attenuation in small animals and the implementation of two attenuation correction methods based on X-rays CT and segmentation of emission images. The accuracy of the first method with respect to the beam hardening effect was investigated by using Monte Carlo simulations. Mouse- and rat-sized phantoms were acquired in order to evaluate attenuation correction in terms of counts increment and recovery of uniform activity concentration. Both methods were applied to mice and rat images acquired with several radiotracers such as(18)F-FDG, (11)C-acetate, (68)Ga-chloride, and (18)F-NaF. The accuracy of the proposed methods was evaluated in heart and tumour tissues using (18)F-FDG images and in liver, kidney, and spinal column tissues using (11)C-acetate, (68)Ga-chloride, and (18)F-NaF images, respectively. In vivo results from animal studies show that, except for bone scans, differences between the proposed methods were about 10% in rats and 3% in mice. In conclusion, both methods provide equivalent results; however, the segmentation-based approach has several advantages being less time consuming and simple to implement.

Evaluation of the Feasibility and Quantitative Accuracy of a Generalized Scatter 2D PET Reconstruction Method

Scatter degrades the contrast and quantitative accuracy of positron emission tomography (PET) images, and most methods for estimating and correcting scattered coincidences in PET subtract scattered events from the measured data. Compton scattering kinematics can be used to map out the locus of possible scattering locations. These curved lines (2D) or surfaces (3D), which connect the coincidence detectors, encompass the surface (2D) or volume (3D) where the decay occurs. In the limiting case where the scattering angle approaches zero, the scattered coincidence approaches the true coincidence. Therefore, both true and scattered coincidences can be considered similarly in a generalized scatter maximum-likelihood expectation-maximization reconstruction algorithm. The proposed method was tested using list-mode data obtained from a GATE simulation of a Jaszczak-type phantom. For scatter fractions from 10% to 60%, this approach reduces noise and improves the contrast recovery coefficients by 0.5–3.0% compared with reconstructions using true coincidences and by 3.0–24.5% with conventional reconstruction methods. The results demonstrate that this algorithm is capable of producing images entirely from scattered photons, eliminates the need for scatter corrections, increases image contrast, and reduces noise. This could be used to improve diagnostic quality and/or to reduce patient dose and radiopharmaceutical cost.