Image reconstruction for PET/CT scanners: past achievements and future challenges

Feasibility of Biology-guided Radiotherapy (BgRT) Targeting Fluorodeoxyglucose (FDG) avid liver metastases

INTRODUCTION

Biology-guided radiotherapy (BgRT) is a novel radiation delivery approach utilizing fluorodeoxyglucose (FDG) activity on positron emission tomography (PET) imaging performed in real-time to track and direct RT. Our institution recently acquired the RefleXion X1 BgRT system and sought to assess the feasibility of targeting metastatic sites in various organs, including the liver. However, in order for BgRT to function appropriate, adequate contrast in FDG activity between the tumor and the background tissue, referred to as the normalized SUV (NSUV), is necessary for optimal functioning of BgRT.

METHODS

We reviewed the charts of 50 lung adenocarcinoma patients with liver metastases. The following variables were collected: SUVmax and SUVmean for each liver metastasis, SUVmean and SUVmax at 5 and 10 mm radially from the lesion, and NSUV at 5 mm and 10 mm (SUVmax of the liver metastasis divided by SUV mean at 5 mm at 10 mm respectively).

RESULTS

82 measurable liver metastases were included in the final analysis. The average SUVbackground of liver was 2.26 (95% confidence interval [CI] 2.17-2.35); average SUVmean for liver metastases was 5.31 (95% CI 4.87-5.75), and average SUVmax of liver metastases was 9.19 (95% CI 7.59-10.78). The average SUVmean at 5 mm and 10 mm radially from each lesion were 3.08 (95% CI 3.00-2.16) and 2.60 (95% CI 2.52-2.68), respectively. The mean NSUV at 5 mm and 10 mm were 3.13 (95% CI 2.53-3.73) and 3.69 (95% CI 3.00-4.41) respectively. Furthermore, 90% of lesions had NSUV greater than 1.45 at 5 mm and greater than 1.77 at 10 mm.

CONCLUSIONS

This is the first study to comprehensively characterize FDG contrast between the liver tumor and background, referred to as NSUV. Due to the high background SUV normally found in the liver, this work will be valuable for guiding optimization of BgRT for treating liver metastases in the future using the RefleXion® X1 and potentially other similar BgRT platforms.

Noninvasive Quantification of Glucose Metabolism in Mice Myocardium Using the Spline Reconstruction Technique

The spline reconstruction technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study was to compare the SRT, filtered back-projection (FBP), and the Tera-Tomo 3D algorithm for various iteration numbers, using small-animal dynamic PET data obtained from a Mediso nanoScan® PET/CT scanner. For this purpose, Patlak graphical kinetic analysis was employed to noninvasively quantify the myocardial metabolic rate of glucose (MRGlu) in seven male C57BL/6 mice (n=7). All analytic reconstructions were performed via software for tomographic image reconstruction. The analysis of all PET-reconstructed images was conducted with PMOD software (version 3.506, PMOD Technologies LLC, Fällanden, Switzerland) using the inferior vena cava as the image-derived input function. Statistical significance was determined by employing the one-way analysis of variance test. The results revealed that the differences between the values of MRGlu obtained via SRT versus FBP, and the variants of he Tera-Tomo 3D algorithm were not statistically significant (p > 0.05). Overall, the SRT appears to perform similarly to the other algorithms investigated, providing a valid alternative analytic method for preclinical dynamic PET studies.

A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis

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

Robustness of radiomic features in 123I-ioflupane-dopamine transporter single-photon emission computer tomography scan

Radiomic features are usually used to predict target variables such as the absence or presence of a disease, treatment response, or time to symptom progression. One of the potential clinical applications is in patients with Parkinson's disease. Robust radiomic features for this specific imaging method have not yet been identified, which is necessary for proper feature selection. Thus, we are assessing the robustness of radiomic features in dopamine transporter imaging (DaT). For this study, we made an anthropomorphic head phantom with tissue heterogeneity using a personal 3D printer (polylactide 82% infill); the bone was subsequently reproduced with plaster. A surgical cotton ball with radiotracer (123I-ioflupane) was inserted. Scans were performed on the two-detector hybrid camera with acquisition parameters corresponding to international guidelines for DaT single photon emission tomography (SPECT). Reconstruction of SPECT was performed on a clinical workstation with iterative algorithms. Open-source LifeX software was used to extract 134 radiomic features. Statistical analysis was made in RStudio using the intraclass correlation coefficient (ICC) and coefficient of variation (COV). Overall, radiomic features in different reconstruction parameters showed a moderate reproducibility rate (ICC = 0.636, p <0.01). Assessment of ICC and COV within CT attenuation correction (CTAC) and non-attenuation correction (NAC) groups and within particular feature classes showed an excellent reproducibility rate (ICC > 0.9, p < 0.01), except for an intensity-based NAC group, where radiomic features showed a good repeatability rate (ICC = 0.893, p <0.01). By our results, CTAC becomes the main threat to feature stability. However, many radiomic features were sensitive to the selected reconstruction algorithm irrespectively to the attenuation correction. Radiomic features extracted from DaT-SPECT showed moderate to excellent reproducibility rates. These results make them suitable for clinical practice and human studies, but awareness of feature selection should be held, as some radiomic features are more robust than others.

Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning

OBJECTIVE

To improve the PET image quality by a deep progressive learning (DPL) reconstruction algorithm and evaluate the DPL performance in lesion quantification.

METHODS

We reconstructed PET images from 48 oncological patients using ordered subset expectation maximization (OSEM) and deep progressive learning (DPL) methods. The patients were enrolled into three overlapped studies: 11 patients for image quality assessment (study 1), 34 patients for sub-centimeter lesion quantification (study 2), and 28 patients for imaging of overweight or obese individuals (study 3). In study 1, we evaluated the image quality visually based on four criteria: overall score, image sharpness, image noise, and diagnostic confidence. We also measured the image quality quantitatively using the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), contrast-to-background ratio (CBR), and contrast-to-noise ratio (CNR). To evaluate the performance of the DPL algorithm in quantifying lesions, we compared the maximum standardized uptake values (SUVmax), SBR, CBR, SNR and CNR of 63 sub-centimeter lesions in study 2 and 44 lesions in study 3.

RESULTS

DPL produced better PET image quality than OSEM did based on the visual evaluation methods when the acquisition time was 0.5, 1.0 and 1.5 min/bed. However, no discernible differences were found between the two methods when the acquisition time was 2.0, 2.5 and 3.0 min/bed. Quantitative results showed that DPL had significantly higher values of SBR, CBR, SNR, and CNR than OSEM did for each acquisition time. For sub-centimeter lesion quantification, the SUVmax, SBR, CBR, SNR, and CNR of DPL were significantly enhanced, compared with OSEM. Similarly, for lesion quantification in overweight and obese patients, DPL significantly increased these parameters compared with OSEM.

CONCLUSION

The DPL algorithm dramatically enhanced the quality of PET images and enabled more accurate quantification of sub-centimeters lesions in patients and lesions in overweight or obese patients. This is particularly beneficial for overweight or obese patients who usually have lower image quality due to the increased attenuation.

Improved diagnostic accuracy for coronary artery disease detection with quantitative 3D 82Rb PET myocardial perfusion imaging

PURPOSE

To establish requirements for normal databases for quantitative rubidium-82 (82Rb) PET MPI analysis with contemporary 3D PET/CT technology and reconstruction methods for maximizing diagnostic accuracy of total perfusion deficit (TPD), a combined metric of defect extent and severity, versus invasive coronary angiography.

METHODS

In total, 1571 patients with 82Rb PET/CT MPI on a 3D scanner and stress static images reconstructed with and without time-of-flight (TOF) modeling were identified. An additional eighty low pre-test probability of disease (PTP) patients reported as normal were used to form separate sex-stratified and sex-independent iterative and TOF normal databases. 3D normal databases were applied to matched patient reconstructions to quantify TPD. Per-patient and per-vessel performance of 3D versus 2D PET normal databases was assessed with receiver operator characteristic curve analysis. Diagnostic accuracy was evaluated at optimal thresholds established from PTP patients. Results were compared against logistic regression modeling of TPD adjusted for clinical variables, and standard clinical interpretation.

RESULTS

TPD diagnostic accuracy was significantly higher using 3D PET normal databases (per-patient: 80.1% for 3D databases, versus 74.9% and 77.7% for 2D database applied to iterative and TOF images respectively, p < 0.05). Differences in male and female normal distributions for 3D attenuation-corrected reconstructions were not clinically meaningful; therefore, sex-independent databases were used. Logistic regression modeling including TPD demonstrated improved performance over clinical reads.

CONCLUSIONS

Normal databases tailored to 3D PET images provide significantly improved diagnostic accuracy for PET MPI evaluation with automated quantitative TPD. Clinical application of these techniques should be considered to support accurate image interpretation.

Image reconstruction using UNET-transformer network for fast and low-dose PET scans

INTRODUCTION

Low-dose and fast PET imaging (low-count PET) play a significant role in enhancing patient safety, healthcare efficiency, and patient comfort during medical imaging procedures. To achieve high-quality images with low-count PET scans, effective reconstruction models are crucial for denoising and enhancing image quality. The main goal of this paper is to develop an effective and accurate deep learning-based method for reconstructing low-count PET images, which is a challenging problem due to the limited amount of available data and the high level of noise in the acquired images. The proposed method aims to improve the quality of reconstructed PET images while preserving important features, such as edges and small details, by combining the strengths of UNET and Transformer networks.

MATERIAL AND METHODS

The proposed TrUNET-MAPEM model integrates a residual UNET-transformer regularizer into the unrolled maximum a posteriori expectation maximization (MAPEM) algorithm for PET image reconstruction. A loss function based on a combination of structural similarity index (SSIM) and mean squared error (MSE) is utilized to evaluate the accuracy of the reconstructed images. The simulated dataset was generated using the Brainweb phantom, while the real patient dataset was acquired using a Siemens Biograph mMR PET scanner. We also implemented state-of-the-art methods for comparison purposes: OSEM, MAPOSEM, and supervised learning using 3D-UNET network. The reconstructed images are compared to ground truth images using metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and relative root mean square error (rRMSE) to quantitatively evaluate the accuracy of the reconstructed images.

RESULTS

Our proposed TrUNET-MAPEM approach was evaluated using both simulated and real patient data. For the patient data, our model achieved an average PSNR of 33.72 dB, an average SSIM of 0.955, and an average rRMSE of 0.39. These results outperformed other methods which had average PSNRs of 36.89 dB, 34.12 dB, and 33.52 db, average SSIMs of 0.944, 0.947, and 0.951, and average rRMSEs of 0.59, 0.49, and 0.42. For the simulated data, our model achieved an average PSNR of 31.23 dB, an average SSIM of 0.95, and an average rRMSE of 0.55. These results also outperformed other state-of-the-art methods, such as OSEM, MAPOSEM, and 3DUNET-MAPEM. The model demonstrates the potential for clinical use by successfully reconstructing smooth images while preserving edges. The comparison with other methods demonstrates the superiority of our approach, as it outperforms all other methods for all three metrics.

CONCLUSION

The proposed TrUNET-MAPEM model presents a significant advancement in the field of low-count PET image reconstruction. The results demonstrate the potential for clinical use, as the model can produce images with reduced noise levels and better edge preservation compared to other reconstruction and post-processing algorithms. The proposed approach may have important clinical applications in the early detection and diagnosis of various diseases.

The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection on 18F-FDG images using a BGO-based PET/CT scanner: a phantom and clinical study

BACKGROUND

The Q.Clear algorithm is a fully convergent iterative image reconstruction technique. We hypothesize that different PET/CT scanners with distinct crystal properties will require different optimal settings for the Q.Clear algorithm. Many studies have investigated the improvement of the Q.Clear reconstruction algorithm on PET/CT scanner with LYSO crystals and SiPM detectors. We propose an optimum penalization factor (β) for the detection of rectal cancer and its metastases using a BGO-based detector PET/CT system which obtained via accurate and comprehensive phantom and clinical studies.

METHODS

18F-FDG PET-CT scans were acquired from NEMA phantom with lesion-to-background ratio (LBR) of 2:1, 4:1, 8:1, and 15 patients with rectal cancer. Clinical lesions were classified into two size groups. OSEM and Q.Clear (β value of 100-500) reconstruction was applied. In Q.Clear, background variability (BV), contrast recovery (CR), signal-to-noise ratio (SNR), SUVmax, and signal-to-background ratio (SBR) were evaluated and compared to OSEM.

RESULTS

OSEM had 11.5-18.6% higher BV than Q.Clear using β value of 500. Conversely, RC from OSEM to Q.Clear using β value of 500 decreased by 3.3-7.7% for a sphere with a diameter of 10 mm and 2.5-5.1% for a sphere with a diameter of 37 mm. Furthermore, the increment of contrast using a β value of 500 was 5.2-8.1% in the smallest spheres compared to OSEM. When the β value was increased from 100 to 500, the SNR increased by 49.1% and 30.8% in the smallest and largest spheres at LBR 2:1, respectively. At LBR of 8:1, the relative difference of SNR between β value of 100 and 500 was 43.7% and 44.0% in the smallest and largest spheres, respectively. In the clinical study, as β increased from 100 to 500, the SUVmax decreased by 47.7% in small and 31.1% in large lesions. OSEM demonstrated the least SUVmax, SBR, and contrast. The decrement of SBR and contrast using OSEM were 13.6% and 12.9% in small and 4.2% and 3.4%, respectively, in large lesions.

CONCLUSIONS

Implementing Q.Clear enhances quantitative accuracies through a fully convergent voxel-based image approach, employing a penalization factor. In the BGO-based scanner, the optimal β value for small lesions ranges from 200 for LBR 2:1 to 300 for LBR 8:1. For large lesions, the optimal β value is between 400 for LBR 2:1 and 500 for LBR 8:1. We recommended β value of 300 for small lesions and β value of 500 for large lesions in clinical study.

Impact of list-mode reconstruction and image-space point spread function correction on PET image contrast and quantitative value using SiPM-based PET/CT system

We evaluate the effects of list-mode reconstruction and the image-space point spread function (iPSF) on the contrast and quantitative values of positron emission tomography (PET) images using a SiPM-PET/CT system. The evaluation is conducted on an NEMA body phantom and clinical images using a Cartesion Prime SiPM-PET/CT system. The signal-to-background ratio (SBR) of the phantom is set to 2, 4, 6, and 8, and all the PET image data are obtained and reconstructed using 3D-OSEM, time-of-flight, iPSF (-/ +), and a 4-mm Gaussian filter with several iterations. The evaluation criteria include % background variability (NB,10 mm), % contrast (QH,10 mm), iPSF change in QH,10 mm (ΔQH,10 mm) for edge artifact evaluation, profile curves, visual evaluation of edge artifacts, clinical imaging for the standardized uptake value (SUV) of lung nodules, and SNRliver. NB,10 mm demonstrates no significant difference in all SBRs with and without iPSF, whereas QH,10 mm is higher based on the SBR with and without iPSF. ΔQH,10 mm indicates increased iterations and a larger rate of change (> 5%) for small spheres of < 17 mm. The profile curves portrayed almost real concentrations, except for the 10-mm sphere of SBR2 without iPSF; however, with iPSF, an overshoot was observed in the 13-mm sphere of all SBRs. The degree of overshoot increased with increasing iteration and SBR. Edge artifacts were detected at values ≥ 17-22 mm in SBRs other than SBR2 with iPSF. Irrespective of the nodal size, SUV and SNRliver improved considerably after iPSF adjustment. Therefore, the effects of list-mode reconstruction and iPSF on PET image contrast were limited, and the overcorrection of the quantitative values was validated using iPSF.

Investigation and optimization of PET-guided SPECT reconstructions for improved radionuclide therapy dosimetry estimates

Introduction

To investigate and optimize the SPECTRE (Single Photon Emission Computed Theranostic REconstruction) reconstruction approach, using the hybrid kernelised expectation maximization (HKEM) algorithm implemented in the software for tomographic image reconstruction (STIR) software library, and to demonstrate the feasibility of performing algorithm exploration and optimization in 2D. Optimal SPECTRE parameters were investigated for the purpose of improving SPECT-based radionuclide therapy (RNT) dosimetry estimates.

Materials and Methods

Using the NEMA IEC body phantom as the test object, SPECT data were simulated to model an early and late imaging time point following a typical therapeutic dose of 8 GBq of 177Lu. A theranostic 68Ga PET-prior was simulated for the SPECTRE reconstructions. The HKEM algorithm parameter space was investigated for SPECT-unique and PET-SPECT mutual features to characterize optimal SPECTRE parameters for the simulated data. Mean and maximum bias, coefficient of variation (COV %), recovery, SNR and root-mean-square error (RMSE) were used to facilitate comparisons between SPECTRE reconstructions and OSEM reconstructions with resolution modelling (OSEM_RM). 2D reconstructions were compared to those performed in 3D in order to evaluate the utility of accelerated algorithm optimization in 2D. Segmentation accuracy was evaluated using a 42% fixed threshold (FT) on the 3D reconstructed data.

Results

SPECTRE parameters that demonstrated improved image quality and quantitative accuracy were determined through investigation of the HKEM algorithm parameter space. OSEM_RM and SPECTRE reconstructions performed in 2D and 3D were qualitatively and quantitatively similar, with SPECTRE showing an average reduction in background COV % by a factor of 2.7 and 3.3 for the 2D case and 3D case respectively. The 42% FT analysis produced an average % volume difference from ground truth of 158% and 26%, for the OSEM_RM and SPECTRE reconstructions, respectively.

Conclusions

The SPECTRE reconstruction approach demonstrates significant potential for improved SPECT image quality, leading to more accurate RNT dosimetry estimates when conventional segmentation methods are used. Exploration and optimization of SPECTRE benefited from both fast reconstruction times afforded by first considering the 2D case. This is the first in-depth exploration of the SPECTRE reconstruction approach, and as such, it reveals several insights for reconstructing SPECT data using PET side information.

Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction

We present an adaptive method for fine-tuning hyperparameters in edge-preserving regularization for PET image reconstruction. For edge-preserving regularization, in addition to the smoothing parameter that balances data fidelity and regularization, one or more control parameters are typically incorporated to adjust the sensitivity of edge preservation by modifying the shape of the penalty function. Although there have been efforts to develop automated methods for tuning the hyperparameters in regularized PET reconstruction, the majority of these methods primarily focus on the smoothing parameter. However, it is challenging to obtain high-quality images without appropriately selecting the control parameters that adjust the edge preservation sensitivity. In this work, we propose a method to precisely tune the hyperparameters, which are initially set with a fixed value for the entire image, either manually or using an automated approach. Our core strategy involves adaptively adjusting the control parameter at each pixel, taking into account the degree of patch similarities calculated from the previous iteration within the pixel's neighborhood that is being updated. This approach allows our new method to integrate with a wide range of existing parameter-tuning techniques for edge-preserving regularization. Experimental results demonstrate that our proposed method effectively enhances the overall reconstruction accuracy across multiple image quality metrics, including peak signal-to-noise ratio, structural similarity, visual information fidelity, mean absolute error, root-mean-square error, and mean percentage error.

PET image enhancement using artificial intelligence for better characterization of epilepsy lesions

INTRODUCTION

[18F]fluorodeoxyglucose ([18F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e.g., Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learning-based PET image enhancement method using simulated PET to improve lesion visualization.

METHODS

We created 210 numerical brain phantoms (MRI segmented into 9 regions) and assigned 10 different plausible activity values (e.g., GM/WM ratios) resulting in 2100 ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created 2100 simulated standard quality (S-SQ) [18F]FDG scans. We trained a ResNet on 80% of this dataset (10% used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET. For the remaining 10%, we assessed Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Root Mean Squared Error (RMSE) against GT-HQ PET. For GM and WM, we computed recovery coefficients (RC) and coefficient of variation (COV). We also created lesioned GT-HQ phantoms, S-SQ PET and P-HQ PET with simulated small hypometabolic lesions characteristic of FCDs. We evaluated lesion detectability on S-SQ and P-HQ PET both visually and measuring the Relative Lesion Activity (RLA, measured activity in the reduced-activity ROI over the standard-activity ROI). Lastly, we applied our previously trained ResNet on 10 clinical epilepsy PETs to predict the corresponding HQ-PET and assessed image quality and confidence metrics.

RESULTS

Compared to S-SQ PET, P-HQ PET improved PNSR, SSIM and RMSE; significatively improved GM RCs (from 0.29 ± 0.03 to 0.79 ± 0.04) and WM RCs (from 0.49 ± 0.03 to 1 ± 0.05); mean COVs were not statistically different. Visual lesion detection improved from 38 to 75%, with average RLA decreasing from 0.83 ± 0.08 to 0.67 ± 0.14. Visual quality of P-HQ clinical PET improved as well as reader confidence.

CONCLUSION

P-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics and increased detectability of simulated lesions. In addition, the model generalized to clinical data. Further evaluation is required to study generalization of our method and to assess clinical performance in larger cohorts.

Direct mapping from PET coincidence data to proton-dose and positron activity using a deep learning approach

Objective. Obtaining the intrinsic dose distributions in particle therapy is a challenging problem that needs to be addressed by imaging algorithms to take advantage of secondary particle detectors. In this work, we investigate the utility of deep learning methods for achieving direct mapping from detector data to the intrinsic dose distribution. Approach. We performed Monte Carlo simulations using GATE/Geant4 10.4 simulation toolkits to generate a dataset using human CT phantom irradiated with high-energy protons and imaged with compact in-beam PET for realistic beam delivery in a single-fraction (∼2 Gy). We developed a neural network model based on conditional generative adversarial networks to generate dose maps conditioned on coincidence distributions in the detector. The model performance is evaluated by the mean relative error, absolute dose fraction difference, and shift in Bragg peak position. Main results. The relative deviation in the dose and range of the distributions predicted by the model from the true values for mono-energetic irradiation between 50 and 122 MeV lie within 1% and 2%, respectively. This was achieved using 105 coincidences acquired five minutes after irradiation. The relative deviation in the dose and range for spread-out Bragg peak distributions were within 1% and 2.6% uncertainties, respectively. Significance. An important aspect of this study is the demonstration of a method for direct mapping from detector counts to dose domain using the low count data of compact detectors suited for practical implementation in particle therapy. Including additional prior information in the future can further expand the scope of our model and also extend its application to other areas of medical imaging.

From bench to bedside: The mGluR5 system in people with and without Autism Spectrum Disorder and animal model systems

The metabotropic glutamate receptor 5 (mGluR5) is a key regulator of excitatory (E) glutamate and inhibitory (I) γ-amino butyric acid (GABA) signalling in the brain. Despite the close functional ties between mGluR5 and E/I signalling, no-one has directly examined the relationship between mGluR5 and glutamate or GABA in vivo in the human brain of autistic individuals. We measured [¹⁸F] FPEB (¹⁸F-3-fluoro-5-[(pyridin-3-yl)ethynyl]benzonitrile) binding in 15 adults (6 with Autism Spectrum Disorder) using two regions of interest, the left dorsomedial prefrontal cortex and a region primarily composed of left striatum and thalamus. These two regions were mapped out using MEGA-PRESS voxels and then superimposed on reconstructed PET images. This allowed for direct comparison between mGluR5, GABA + and Glx. To better understand the molecular underpinnings of our results we used an autoradiography study of mGluR5 in three mouse models associated with ASD: Cntnap2 knockout, Shank3 knockout, and 16p11.2 deletion. Autistic individuals had significantly higher [¹⁸F] FPEB binding (t (13) = -2.86, p = 0.047) in the left striatum/thalamus region of interest as compared to controls. Within this region, there was a strong negative correlation between GABA + and mGluR5 density across the entire cohort (Pearson's correlation: r (14) = -0.763, p = 0.002). Cntnap2 KO mice had significantly higher mGlu5 receptor binding in the striatum (caudate-putamen) as compared to wild-type (WT) mice (n = 15, p = 0.03). There were no differences in mGluR5 binding for mice with the Shank3 knockout or 16p11.2 deletion. Given that Cntnap2 is associated with a specific striatal deficit of parvalbumin positive GABA interneurons and 'autistic' features, our findings suggest that an increase in mGluR5 in ASD may relate to GABAergic interneuron abnormalities.

Impact of image quality on radiomics applications

Radiomics features extracted from medical images have been widely reported to be useful in the patient specific outcome modeling for variety of assessment and prediction purposes. Successful application of radiomics features as imaging biomarkers, however, is dependent on the robustness of the approach to the variation in each step of the modeling workflow. Variation in the input image quality is one of the main sources that impacts the reproducibility of radiomics analysis when a model is applied to broader range of medical imaging data. The quality of medical image is generally affected by both the scanner related factors such as image acquisition/reconstruction settings and the patient related factors such as patient motion. This article aimed to review the published literatures in this field that reported the impact of various imaging factors on the radiomics features through the change in image quality. The literatures were categorized by different imaging modalities and also tabulated based on the imaging parameters and the class of radiomics features included in the study. Strategies for image quality standardization were discussed based on the relevant literatures and recommendations for reducing the impact of image quality variation on the radiomics in multi-institutional clinical trial were summarized at the end of this article.

Advances in Preclinical PET

The classical intent of PET imaging is to obtain the most accurate estimate of the amount of positron-emitting radiotracer in the smallest possible volume element located anywhere in the imaging subject at any time using the least amount of radioactivity. Reaching this goal, however, is confounded by an enormous array of interlinked technical issues that limit imaging system performance. As a result, advances in PET, human or animal, are the result of cumulative innovations across each of the component elements of PET, from data acquisition to image analysis. In the report that follows, we trace several of these advances across the imaging process with a focus on small animal PET.

Evaluation of the spline reconstruction technique for preclinical PET imaging

BACKGROUND AND OBJECTIVE

The Spline Reconstruction Technique (SRT) is a fast algorithm based on a novel numerical implementation of an analytic representation of the inverse Radon transform. The purpose of this study is to provide a comparison between SRT, Filtered Back-Projection (FBP), Ordered Subset Expectation Maximization 2D (2D-OSEM), and the Tera-Tomo 3D algorithm, using phantom data at various acquisition durations as well as small-animal data obtained from the Mediso nanoScan® PET/CT scanner.

METHODS

For this purpose, the "NEMA NU 4-2008 standards" protocol was employed at five different realizations and acquisition durations. In addition to the image quality metrics described by the NEMA protocol, Cold Region Contrast was also considered as a figure-of-merit. Furthermore, Cold Region Contrast was measured in the myocardial infarction region of six male Wistar rats. The volumetric defect quantification was assessed with dedicated computer software. Lastly, plots of Recovery Coefficient and Spill-Over Ratio as a function of the Percentage Standard Deviation were generated, after smoothing the phantom reconstructions with four different Gaussian filters. Statistical significance was determined by employing the Kruskal-Wallis test or One-way Analysis of Variance depending on the normality of the variable's distribution.

RESULTS

The present study revealed that, at the expense of slightly increased noise in the reconstructed images, SRT resulted in higher Recovery Coefficient values for small hot regions of interest, when compared with FBP and 2D-OSEM at all acquisition durations. Furthermore, SRT reconstructed images exhibit higher Recovery Coefficient values, for all hot regions of interest, when compared to the other 2D algorithms at short acquisition durations. In both phantom and animal studies, SRT achieved a significant improvement over 2D-OSEM for the Spill-Over Ratio and the Cold Region Contrast. These advantages were maintained even after comparing the algorithms at equal noise levels. The Tera-Tomo 3D algorithm (4 subsets, iterations≥ 13) performed significantly better compared to the other algorithms for all figures-of-merit. No statistically significant differences regarding the myocardial defect size were observed between the algorithms investigated.

CONCLUSIONS

Overall, SRT appears that could be useful for the quantification of small hot regions of interest, cold regions of interest, as well as in low-count imaging applications.

Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram

Over the last decades, there has been an increasing interest in dedicated preclinical imaging modalities for research in biomedicine. Especially in the case of positron emission tomography (PET), reconstructed images provide useful information of the morphology and function of an internal organ. PET data, stored as sinograms, involve the Radon transform of the image under investigation. The analytical approach to PET image reconstruction incorporates the derivative of the Hilbert transform of the sinogram. In this direction, in the present work we present a novel numerical algorithm for the inversion of the Radon transform based on Chebyshev polynomials of the first kind. By employing these polynomials, the computation of the derivative of the Hilbert transform of the sinogram is significantly simplified. Extending the mathematical setting of previous research based on Chebyshev polynomials, we are able to efficiently apply our new Chebyshev inversion scheme for the case of analytic preclinical PET image reconstruction. We evaluated our reconstruction algorithm on projection data from a small-animal image quality (IQ) simulated phantom study, in accordance with the NEMA NU 4-2008 standards protocol. In particular, we quantified our reconstructions via the image quality metrics of percentage standard deviation, recovery coefficient, and spill-over ratio. The projection data employed were acquired for three different Poisson noise levels: 100% (NL1), 50% (NL2), and 20% (NL3) of the total counts, respectively. In the uniform region of the IQ phantom, Chebyshev reconstructions were consistently improved over filtered backprojection (FBP), in terms of percentage standard deviation (up to 29% lower, depending on the noise level). For all rods, we measured the contrast-to-noise-ratio, indicating an improvement of up to 68% depending on the noise level. In order to compare our reconstruction method with FBP, at equal noise levels, plots of recovery coefficient and spill-over ratio as functions of the percentage standard deviation were generated, after smoothing the NL3 reconstructions with three different Gaussian filters. When post-smoothing was applied, Chebyshev demonstrated recovery coefficient values up to 14% and 42% higher, for rods 1–3 mm and 4–5 mm, respectively, compared to FBP, depending on the smoothing sigma values. Our results indicate that our Chebyshev-based analytic reconstruction method may provide PET reconstructions that are comparable to FBP, thus yielding a good alternative to standard analytic preclinical PET reconstruction methods.

A backprojection kernel (KRNL3D) for very-wide-aperture 3D tomography applied to PET with Multigrid for precise use of time-of-flight data

In 'KRNL3D' we derive a kernel functionK(y₁,y₂,φ) whose backprojections from all directions (θ,φ) in the spherical band∣φ∣<φ¯maxon the celestial sphere, when integrated with respect to solid angle, yieldρ, the 3D Gaussian point response function (PRF) of radius 1. ThisK, when convolved against line integral data from an unknown density functionf, yields an integral formula for the 'mollification'ff=ρ∗f, which is a slightly blurred version off, and which stabilizes the mild ill-posedness. Applied to positron emission tomography that backprojection reconstruction occurs stochastically and one emission event at a time, after needed data corrections. We describe Octave (≈Matlab) codes to tabulateKand to test its use with a large apertureφ¯max=π/3orπ/6. 'KRNL3D-TOF' truncates backprojection to a cylindrical patch about the TOF approximate location of each event. These 'backplacements' decrease the computational cost and limit noise and streaking in one region from contaminating the reconstruction in more distant regions. They also retain the ability to count emission events in an isolated blob despiteverylow event counts, a valuable feature fordynamicstudies of metabolic processes. 'Multigrid' allows further reduction in the radius and lengths of the cylinders, thereby enabling even moreprecise use of the TOF information. This precision should be especially important as researchers decrease the TOF uncertainty in newer generation scanners. Finally, we discuss 'further work' that needs to be done. Our codes are being made freely available athttps://github.com/keithmillerberkeley/PET-codes.

Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement

High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of artificial intelligence models for PET image enhancement, and discuss future directions.

The Evolution of Image Reconstruction in PET: From Filtered Back-Projection to Artificial Intelligence

PET can provide functional images revealing physiologic processes in vivo. Although PET has many applications, there are still some limitations that compromise its precision: the absorption of photons in the body causes signal attenuation; the dead-time limit of system components leads to the loss of the count rate; the scattered and random events received by the detector introduce additional noise; the characteristics of the detector limit the spatial resolution; and the low signal-to-noise ratio caused by the scan-time limit (eg, dynamic scans) and dose concern. The early PET reconstruction methods are analytical approaches based on an idealized mathematical model.

Phantom and clinical assessment of small pulmonary nodules using Q.Clear reconstruction on a silicon-photomultiplier-based time-of-flight PET/CT system

To evaluate the quantification accuracy of different positron emission tomography-computed tomography (PET/CT) reconstruction algorithms, we measured the recovery coefficient (RC) and contrast recovery (CR) in phantom studies. The results played a guiding role in the partial-volume-effect correction (PVC) for following clinical evaluations. The PET images were reconstructed with four different methods: ordered subsets expectation maximization (OSEM), OSEM with time-of-flight (TOF), OSEM with TOF and point spread function (PSF), and Bayesian penalized likelihood (BPL, known as Q.Clear in the PET/CT of GE Healthcare). In clinical studies, SUVmax and SUVmean (the maximum and mean of the standardized uptake values, SUVs) of 75 small pulmonary nodules (sub-centimeter group: < 10 mm and medium-size group: 10-25 mm) were measured from 26 patients. Results show that Q.Clear produced higher RC and CR values, which can improve quantification accuracy compared with other methods (P < 0.05), except for the RC of 37 mm sphere (P > 0.05). The SUVs of sub-centimeter fludeoxyglucose (FDG)-avid pulmonary nodules with Q.Clear illustrated highly significant differences from those reconstructed with other algorithms (P < 0.001). After performing the PVC, highly significant differences (P < 0.001) still existed in the SUVmean measured by Q.Clear comparing with those measured by the other algorithms. Our results suggest that the Q.Clear reconstruction algorithm improved the quantification accuracy towards the true uptake, which potentially promotes the diagnostic confidence and treatment response evaluations with PET/CT imaging, especially for the sub-centimeter pulmonary nodules. For small lesions, PVC is essential.

Improvement of image quality using amplitude-based respiratory gating in PET-computed tomography scanning

OBJECTIVES

This study sought to provide data supporting the expanded clinical use of respiratory gating by assessing the diagnostic accuracy of breathing motion correction using amplitude-based respiratory gating.

METHODS

A respiratory movement tracking device was attached to a PET-computed tomography scanner, and images were obtained in respiratory gating mode using a motion phantom that was capable of sensing vertical motion. Specifically, after setting amplitude changes and intervals according to the movement cycle using a total of nine combinations of three waveforms and three amplitude ranges, respiratory motion-corrected images were reconstructed using the filtered back projection method. After defining areas of interest in the acquired images in the same image planes, statistical analyses were performed to compare differences in standardized uptake value (SUV), lesion volume, full width at half maximum (FWHM), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR).

RESULTS

SUVmax increased by 89.9%, and lesion volume decreased by 27.9%. Full width at half maximum decreased by 53.9%, signal-to-noise ratio increased by 11% and contrast-to-noise ratio increased by 16.3%. Optimal results were obtained when using a rest waveform and 35% duty cycle, in which the change in amplitude in the respiratory phase signal was low, and a constant level of long breaths was maintained.

CONCLUSIONS

These results demonstrate that respiratory-gated PET-CT imaging can be used to accurately correct for SUV changes and image distortion caused by respiratory motion, thereby providing excellent imaging information and quality.

Artificial intelligence in molecular imaging

AI has, to varying degrees, affected all aspects of molecular imaging, from image acquisition to diagnosis. During the last decade, the advent of deep learning in particular has transformed medical image analysis. Although the majority of recent advances have resulted from neural-network models applied to image segmentation, a broad range of techniques has shown promise for image reconstruction, image synthesis, differential-diagnosis generation, and treatment guidance. Applications of AI for drug design indicate the way forward for using AI to facilitate molecular-probe design, which is still in its early stages. Deep-learning models have demonstrated increased efficiency and image quality for PET reconstruction from sinogram data. Generative adversarial networks (GANs), which are paired neural networks that are jointly trained to generate and classify images, have found applications in modality transformation, artifact reduction, and synthetic-PET-image generation. Some AI applications, based either partly or completely on neural-network approaches, have demonstrated superior differential-diagnosis generation relative to radiologists. However, AI models have a history of brittleness, and physicians and patients may not trust AI applications that cannot explain their reasoning. To date, the majority of molecular-imaging applications of AI have been confined to research projects, and are only beginning to find their ways into routine clinical workflows via commercialization and, in some cases, integration into scanner hardware. Evaluation of actual clinical products will yield more realistic assessments of AI’s utility in molecular imaging.

Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-Flight 68Ga-PSMA-11 PET/CT Imaging

In prostate cancer, the early detection of distant spread has been shown to be of importance. Prostate-specific membrane antigen (PSMA)-binding radionuclides in positron emission tomography (PET) is a promising method for precise disease staging. PET diagnostics depend on image reconstruction techniques, and ordered subset expectation maximization (OSEM) is the established standard. Block sequential regularized expectation maximization (BSREM) is a more recent reconstruction algorithm and may produce fewer equivocal findings and better lesion detection. Methods: ⁶⁸Ga PSMA-11 PET/CT scans of patients with de novo or suspected recurrent prostate cancer were retrospectively reformatted using both the OSEM and BSREM algorithms. The lesions were counted and categorized by three radiologists. The intra-class correlation (ICC) and Cohen’s kappa for the inter-rater reliability were calculated. Results: Sixty-one patients were reviewed. BSREM identified slightly fewer lesions overall and fewer equivocal findings. ICC was excellent with regards to definitive lymph nodes and bone metastasis identification and poor with regards to equivocal metastasis irrespective of the reconstruction algorithm. The median Cohen’s kappa were 0.66, 0.74, 0.61 and 0.43 for OSEM and 0.61, 0.63, 0.66 and 0.53 for BSREM, with respect to the tumor, local lymph nodes, metastatic lymph nodes and bone metastasis detection, respectively. Conclusions: BSREM in the setting of ⁶⁸Ga PMSA PET staging or restaging is comparable to OSEM.

Does the beta regularization parameter of bayesian penalized likelihood reconstruction always affect the quantification accuracy and image quality of positron emission tomography computed tomography?

Purpose

This study aims to provide a detailed investigation on the noise penalization factor in Bayesian penalized likelihood (BPL)‐based algorithm, with the utilization of partial volume effect correction (PVC), so as to offer the suitable beta value and optimum standardized uptake value (SUV) parameters in clinical practice for small pulmonary nodules.

Methods

A National Electrical Manufacturers Association (NEMA) image‐quality phantom was scanned and images were reconstructed using BPL with beta values ranged from 100 to 1000. The recovery coefficient (RC), contrast recovery (CR), and background variability (BV) were measured to assess the quantification accuracy and image quality. In the clinical assessment, lesions were categorized into sub‐centimeter (<10 mm, n = 7) group and medium size (10–30 mm, n = 16) group. Signal‐to‐noise ratio (SNR) and contrast‐to‐noise ratio (CNR) were measured to evaluate the image quality and lesion detectability. With PVC was performed, the impact of beta values on SUVs (SUVmax, SUVmean, SUVpeak) of small pulmonary nodules was evaluated. Subjective image analysis was performed by two experienced readers.

Results

With the increasing of beta values, RC, CR, and BV decreased gradually in the phantom work. In the clinical study, SNR and CNR of both groups increased with the beta values (P < 0.001), although the sub‐centimeter group showed increases after the beta value reached over 700. In addition, highly significant negative correlations were observed between SUVs and beta values for both lesion‐size groups before the PVC (P < 0.001 for all). After the PVC, SUVpeak measured from the sub‐centimeter group was no significantly different among different beta values (P = 0.830).

Conclusion

Our study suggests using SUVpeak as the quantification parameter with PVC performed to mitigate the effects of beta regularization. Beta values between 300 and 400 were preferred for pulmonary nodules smaller than 30 mm.

Artificial Intelligence for Response Evaluation With PET/CT

Positron emission tomography (PET)/computed tomography (CT) are nuclear diagnostic imaging modalities that are routinely deployed for cancer staging and monitoring. They hold the advantage of detecting disease related biochemical and physiologic abnormalities in advance of anatomical changes, thus widely used for staging of disease progression, identification of the treatment gross tumor volume, monitoring of disease, as well as prediction of outcomes and personalization of treatment regimens. Among the arsenal of different functional imaging modalities, nuclear imaging has benefited from early adoption of quantitative image analysis starting from simple standard uptake value normalization to more advanced extraction of complex imaging uptake patterns; thanks to application of sophisticated image processing and machine learning algorithms. In this review, we discuss the application of image processing and machine/deep learning techniques to PET/CT imaging with special focus on the oncological radiotherapy domain as a case study and draw examples from our work and others to highlight current status and future potentials.

Quantitative Rodent Brain Receptor Imaging

Positron emission tomography (PET) is a non-invasive imaging technology employed to describe metabolic, physiological, and biochemical processes in vivo. These include receptor availability, metabolic changes, neurotransmitter release, and alterations of gene expression in the brain. Since the introduction of dedicated small-animal PET systems along with the development of many novel PET imaging probes, the number of PET studies using rats and mice in basic biomedical research tremendously increased over the last decade. This article reviews challenges and advances of quantitative rodent brain imaging to make the readers aware of its physical limitations, as well as to inspire them for its potential applications in preclinical research. In the first section, we briefly discuss the limitations of small-animal PET systems in terms of spatial resolution and sensitivity and point to possible improvements in detector development. In addition, different acquisition and post-processing methods used in rodent PET studies are summarized. We further discuss factors influencing the test-retest variability in small-animal PET studies, e.g., different receptor quantification methodologies which have been mainly translated from human to rodent receptor studies to determine the binding potential and changes of receptor availability and radioligand affinity. We further review different kinetic modeling approaches to obtain quantitative binding data in rodents and PET studies focusing on the quantification of endogenous neurotransmitter release using pharmacological interventions. While several studies have focused on the dopamine system due to the availability of several PET tracers which are sensitive to dopamine release, other neurotransmitter systems have become more and more into focus and are described in this review, as well. We further provide an overview of latest genome engineering technologies, including the CRISPR/Cas9 and DREADD systems that may advance our understanding of brain disorders and function and how imaging has been successfully applied to animal models of human brain disorders. Finally, we review the strengths and opportunities of simultaneous PET/magnetic resonance imaging systems to study drug-receptor interactions and challenges for the translation of PET results from bench to bedside.

Uncertainty analysis of MR-PET image registration for precision neuro-PET imaging

Accurate regional brain quantitative PET measurements, particularly when using partial volume correction, rely on robust image registration between PET and MR images. We argue here that the precision, and hence the uncertainty, of MR-PET image registration is mainly driven by the registration implementation and the quality of PET images due to their lower resolution and higher noise compared to the structural MR images. We propose a dedicated uncertainty analysis for quantifying the precision of MR-PET registration, centred around the bootstrap resampling of PET list-mode events to generate multiple PET image realisations with different noise (count) levels. The effects of PET image reconstruction parameters, such as the use of attenuation and scatter corrections and different number of iterations, on the precision and accuracy of MR-PET registration were investigated. In addition, the performance of four software packages with their default settings for rigid inter-modality image registration were considered: NiftyReg, Vinci, FSL and SPM. Four distinct PET image distributions made of two early time frames (similar to cortical FDG) and two late frames using two amyloid PET dynamic acquisitions of one amyloid positive and one amyloid negative participants were investigated. For the investigated four PET frames, the biggest impact on the uncertainty was observed between registration software packages (up to 10-fold difference in precision) followed by the reconstruction parameters. On average, the lowest uncertainty for different PET frames and brain regions was observed with SPM and two iterations of fully quantitative image reconstruction. The observed uncertainty for the varying PET count-level (from 5% to 60%) was slightly lower than for the reconstruction parameters. We also observed that the registration uncertainty in quantitative PET analysis depends on amyloid status of the considered PET frames, with increased uncertainty (up to three times) when using post-reconstruction partial volume correction. This analysis is applicable for PET data obtained from either PET/MR or PET/CT scanners.

Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time in 68Ga-DOTATATE PET/CT studies?

Background

Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for ⁶⁸Ga-DOTATATE (⁶⁸Ga-DOTA) PET studies, both visually and quantitatively.

Thirty consecutive whole-body ⁶⁸Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually.

Results

Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUV_max of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM.

Conclusion

⁶⁸Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUV_max and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

Impact of PET data driven respiratory motion correction and BSREM reconstruction of 68Ga-DOTATATE PET/CT for differentiating neuroendocrine tumors (NET) and intrapancreatic accessory spleens (IPAS)

To evaluate whether quantitative PET parameters of motion-corrected ⁶⁸Ga-DOTATATE PET/CT can differentiate between intrapancreatic accessory spleens (IPAS) and pancreatic neuroendocrine tumor (pNET). A total of 498 consecutive patients with neuroendocrine tumors (NET) who underwent ⁶⁸Ga-DOTATATE PET/CT between March 2017 and July 2019 were retrospectively analyzed. Subjects with accessory spleens (n = 43, thereof 7 IPAS) and pNET (n = 9) were included, resulting in a total of 45 scans. PET images were reconstructed using ordered-subsets expectation maximization (OSEM) and a fully convergent iterative image reconstruction algorithm with β-values of 1000 (BSREM₁₀₀₀). A data-driven gating (DDG) technique (MOTIONFREE, GE Healthcare) was applied to extract respiratory triggers and use them for PET motion correction within both reconstructions. PET parameters among different samples were compared using non-parametric tests. Receiver operating characteristics (ROC) analyzed the ability of PET parameters to differentiate IPAS and pNETs. SUVmax was able to distinguish pNET from accessory spleens and IPAs in BSREM₁₀₀₀ reconstructions (p < 0.05). This result was more reliable using DDG-based motion correction (p < 0.003) and was achieved in both OSEM and BSREM₁₀₀₀ reconstructions. For differentiating accessory spleens and pNETs with specificity 100%, the ROC analysis yielded an AUC of 0.742 (sensitivity 56%)/0.765 (sensitivity 56%)/0.846 (sensitivity 62%)/0.840 (sensitivity 63%) for SUVmax 36.7/41.9/36.9/41.7 in OSEM/BSREM₁₀₀₀/OSEM + DDG/BSREM₁₀₀₀ + DDG, respectively. BSREM₁₀₀₀ + DDG can accurately differentiate pNET from accessory spleen. Both BSREM₁₀₀₀ and DDG lead to a significant SUV increase compared to OSEM and non-motion-corrected data.

The value of Bayesian penalized likelihood reconstruction for improving lesion conspicuity of malignant lung tumors on 18F-FDG PET/CT: comparison with ordered subset expectation maximization reconstruction incorporating time-of-flight model and point spread function correction

OBJECTIVE

To evaluate the value of Bayesian penalized likelihood (BPL) reconstruction for improving lesion conspicuity of malignant lung tumors on ¹⁸F-fluoro-2-deoxy-D-glucose (FDG) positron emission tomography computed tomography (PET/CT) as compared with the ordered subset expectation maximization (OSEM) reconstruction incorporating time-of-flight (TOF) model and point-spread-function (PSF) correction.

METHODS

Twenty-nine patients with primary or metastatic lung cancers who underwent ¹⁸F-FDG PET/CT were retrospectively studied. PET images were reconstructed with OSEM + TOF, OSEM + TOF + PSF, and BPL with noise penalty strength β-value of 200, 400, 600, and 800. The signal-to-noise ratio (SNR) was determined in normal liver parenchyma. Lung lesion conspicuity was evaluated in 50 lung lesions by using a 4-point scale (0, no visible; 1, poor; 2, good; 3, excellent conspicuity). Two observers were independently asked to choose the most preferred reconstruction for detecting the lung lesions on a per-patient level. The maximum standardized uptake value (SUV_max) was measured in each of the 50 lung lesions.

RESULTS

Liver SNR on the images reconstructed by BPL with β-value of 600 and 800 (17.8 ± 3.7 and 22.5 ± 4.6, respectively) was significantly higher than that by OSEM + TOF + PSF (15.0 ± 3.4, p < 0.0001). BPL with β-value of 600 was chosen most frequently as the preferred reconstruction algorithm for lung lesion assessment by both observers. The conspicuity score of the lung lesions < 10 mm in diameter on images reconstructed by BPL with β-value of 600 was significantly greater than that with OSEM + TOF + PSF (2.2 ± 0.8 vs 1.6 ± 0.9, p < 0.0001), while the conspicuity score of the lesions ≥ 10 mm in diameter was not significantly different between BPL with β-value of 600 and OSEM + TOF + PSF. The mean SUV_max was increased by BPL with β-value of 600 for the lung lesions with < 10 mm in diameter, compared to OSEM + TOF + PSF (3.4 ± 3.1 to 4.2 ± 3.5, p = 0.001). In contrast, BPL with β-value of 600 did not provide increased SUV_max for the lesions  ≥ 10 mm in diameter.

CONCLUSION

BPL reconstruction significantly improves the detection of small inconspicuous malignant tumors in the lung, improving the diagnostic performance of PET/CT.

(Hybrid) SPECT and PET Technologies

SPECT and PET are nuclear tomographic imaging modalities that visualize functional information based on the accumulation of radioactive tracer molecules. However, SPECT and PET lack anatomical information, which has motivated their combination with an anatomical imaging modality such as CT or MRI. This chapter begins with an overview over the fundamental physics of SPECT and PET followed by a presentation of the respective detector technologies, including detection requirements, principles and different detector concepts. The reader is subsequently provided with an introduction into hybrid imaging concepts, before a dedicated section presents the challenges that arise when hybridizing SPECT or PET with MRI, namely, mutual distortions of the different electromagnetic fields in MRI on the nuclear imaging system and vice versa. The chapter closes with an overview about current hybrid imaging systems of both clinical and preclinical kind. Finally, future developments in hybrid SPECT and PET technology are discussed.

The effect of a novel Bayesian penalised likelihood PET reconstruction algorithm on the assessment of malignancy risk in solitary pulmonary nodules according to the British Thoracic Society guidelines

PURPOSE

British Thoracic Society (BTS) guidelines advocate using FDG PET-CT with the Herder model to estimate malignancy risk in solitary pulmonary nodules (SPNs). Qualitative and semi-quantitative assessment of SPN uptake is based upon analysis of Ordered Subset Expected Maximisation (OSEM) PET images. Our aim was to assess the effect of a Bayesian Penalised Likelihood (BPL) PET reconstruction on the assessment of SPN FDG uptake and estimation of malignancy risk (Herder score).

METHODS

Subjects with SPNs who underwent FDG PET-CT between 2014-2017, with histological confirmation of malignancy or histological/imaging follow-up confirmation of benignity were included. Two blinded readers independently classified SPN uptake on both OSEM and BPL (BTS score; 1 = none; 2 = ≤ mediastinal blood pool (MBP); 3 = >MBP but ≤ 2x liver; 4 = >2x liver), with resultant calculation of the Herder score (%) for both reconstructions.

RESULTS

97 subjects with 75 (77%) malignant SPNs were included. BPL increased the BTS score in 25 (26%) SPNs; 9 SPNs (7 malignant) increased from BTS score 2 to 3, 16 (13 malignant) from BTS score 3 to 4, with a mean Herder score increase of 18 ± 22%. The mean Herder score for all SPNs with BPL was higher than OSEM (73 ± 29 vs 68 ± 32%, p = 0.001). There was no difference in Herder model diagnostic performance between BPL and OSEM, with similar areas under the curve (0.84 vs 0.83, p = 0.39).

CONCLUSION

BPL increases the Herder score in 26% of SPNs compared to OSEM but does not alter the diagnostic performance of the Herder model.

A kernel-based image denoising method for improving parametric image generation

One of the main challenges in the pixel-wise modeling analysis is the presence of high noise levels. Wang and Qi proposed a kernel-based method for dynamic positron emission tomgraphy reconstruction. Inspired by this method, we propose a kernel-based image denoising method based on the minimization of a kernel-based lp-norm regularized problem. To solve the kernel-based image denoising problem, we used the general-threshold filtering algorithm in combination with total difference. In the present study, we investigated whether diffusion-weighted magnetic resonance imaging (DW-MRI) data denoised using the proposed method can provide improved intravoxel incoherent motion (IVIM) parametric images. We also compared the proposed method with the method using the local principal component analysis (LPCA). The simulated DW-MR magnitude images are assumed to have Rician distributed noise. Computer simulations show that the proposed image denoising method can achieve a better bias-variance trade-off than the LPCA method. Moreover, the proposed method can reduce variance while simultaneously preserving edges in the parametric images. We tested our image denoising method on in vivo DW-MRI data, and the result showed that the denoised DWI-MRI data obtained using the proposed method can substantially improve the quality of IVIM parametric images.

Ordered subset expectation maximization algorithm for positron emission tomographic image reconstruction using belief kernels

The aim of this study is to investigate the benefits of incorporating prior information in list mode, time-of-flight (TOF) positron emission tomography (PET) image reconstruction using the ordered subset expectation maximization (OSEM) algorithm. This investigation consists of an IEC phantom study and a patient study. For the image under reconstruction, the activity profile along a line of response is treated as a priori and is combined with the TOF measurement to define a belief kernel used for forward and backward projections during the OSEM image reconstruction. Activity profiles are smoothed and combined with the TOF kernels to control the adverse impact of noise, and different levels of smoothness are attempted. The standard TOF OSEM reconstruction is used as a baseline for comparison. Image quality is assessed using a combination of visual assessment and quantitative measurement including contrast recovery coefficients (CRC) and background variability. On the IEC phantom study, the reconstruction using belief kernels converges faster and the reconstructed images are more appealing. The CRCs for all sizes of regions of interest on images reconstructed with belief kernels are higher than those of the baseline. The background variability, measured as a coefficient of variation, is generally lower for the images reconstructed using belief kernels. Similar observations occur on the patient study. Particularly, the images reconstructed using belief kernels have better defined lesions, improved contrast, and reduced background noise. OSEM PET image reconstruction using belief kernels that combine the information from prior images and TOF measurements seems promising and worth further investigation.

Impact of different image reconstructions on PET quantification in non-small cell lung cancer: a comparison of adenocarcinoma and squamous cell carcinoma

OBJECTIVE:

Positron emission tomography (PET) using ¹⁸F-fludeoxyglucose (¹⁸F-FDG) is an established imaging modality for tumor staging in patients with non-small cell lung cancer (NSCLC). There is a growing interest in using ¹⁸F-FDG PET for therapy response assessment in NSCLC which relies on quantitative PET parameters such as standardized uptake values (SUV). Different reconstruction algorithms in PET may affect SUV. We sought to determine the variation of SUV in patients with NSCLC when using ordered subset expectation maximization (OSEM) and block sequential regularized expectation maximization (BSREM) in latest-generation digital PET/CT, including a subanalysis for adenocarcinoma and squamous cell carcinoma.

METHODS:

A total of 58 patients (34 = adenocarcinoma, 24 = squamous cell carcinoma) who underwent a clinically indicated ¹⁸F-FDG PET/CT for staging were reviewed. PET images were reconstructed with OSEM and BSREM reconstruction with noise penalty strength β-levels of 350, 450, 600, 800 and 1200. Lung tumors maximum standardized uptake value (SUV_max) were compared.

RESULTS:

Lung tumors SUV_max were significantly lower in adenocarcinomas compared to squamous cell carcinomas in all reconstructions evaluated (all p < 0.01). Comparing BSREM to OSEM, absolute SUV_max differences were highest in lower β-levels of BSREM with + 2.9 ± 1.6 in adenocarcinoma and + 4.0 ± 2.9 in squamous cell carcinoma (difference between histology; p-values > 0.05). There was a statistically significant difference of the relative increase of SUV_max in adenocarcinoma (mean + 34.8%) and squamous cell carcinoma (mean 23.4%), when using BSREM₃₅₀ instead of OSEM_TOF (p < 0.05).

CONCLUSION:

In NSCLC the relative change of SUV when using BSREM instead of OSEM is significantly higher in adenocarcinoma as compared to squamous cell carcinoma.

ADVANCES IN KNOWLEDGE:

The impact of BSREM on SUV may vary in different histological subtypes of NSCLC. This highlights the importance for careful standardization of β-value used for serial ¹⁸F-FDG PET scans when following-up NSCLC patients.

Physiological uptake of 18F-FDG in the vertebral bone marrow in healthy adults on PET/CT imaging

BACKGROUND

18F-fluorodeoxyglucose *Equal contributors. positron emission tomography/computed tomography (18F-FDG PET/CT) has proven to be a valuable imaging modality for the assessment of bone marrow condition.

PURPOSE

To investigate the physiological uptake of 18F-FDG in the vertebral bone marrow in healthy adults on PET/CT imaging, and correlate the appearance with clinical factors including gender, body mass index, and age.

MATERIAL AND METHODS

A total of 64 healthy individuals underwent PET/CT scan, and for each vertebral body, the mean and maximum standardized uptake value (SUVmean and SUVmax) were determined in the central slice of vertebral body on the transversal fused PET/CT image. For each individual, the FDG uptake of the four regions was obtained by averaging the SUVmean and SUVmax of the vertebrae in individual regions.

RESULTS

The FDG uptake from thoracic to sacral vertebrae showed an upward trend first, then a downward trend, while that of cervical vertebrae was relatively stable. The SUVmax and SUVmean values of bone marrow in the old group (age ≥ 50 years) were significantly lower than those in the young group (age < 50 years) in all regions of the spine ( P < 0.05). FDG uptake of the whole spine showed significant negative correlation with age, and the strongest correlation was observed in lumbar spine (SUVmean: r = -0.364, P < 0.05; SUVmax: r = -0.344, P < 0.05).

CONCLUSION

FDG uptake showed a tendency to increase first then decrease from thoracic to sacral vertebrae while the tendency was not obvious in cervical vertebrae. In addition, the glycolytic metabolism of all the four regions decreased with advancing age.

An automatic algorithm to exploit the symmetries of the system response matrix in positron emission tomography iterative reconstruction

Positron emission tomography (PET) iterative 3D reconstruction is a very computational demanding task. One of the main issues of the iterative reconstruction concerns the management of the system response matrix (SRM). The SRM models the relationship between the projection and the voxel space and its memory footprint can easily exceed hundreds of GB. Moreover, in order to make the reconstruction fast enough not to hinder its practical application, the SRM must be stored in the random access memory of the workstation used for the reconstruction. This issue is normally solved by implementing efficient storage schemes and by reducing the number of redundant patterns in the SRM through symmetries. However, finding a sufficient number of symmetries is often non-trivial and is typically performed using dedicated solutions that cannot be exported to different detectors and geometries. In this paper, an automatic approach to reduce the memory footprint of a pre-computed SRM is described. The proposed approach was named symmetry search algorithm (SSA) and consists in an algorithm that searches for some of the redundant patterns present in the SRM, leading to its lossy compression. This approach was built to detect translations, reflections and coordinates swap in voxel space. Therefore, it is particularly well suited for those scanners where some of the rotational symmetries are broken, e.g. small animal scanner where the modules are arranged in a polygonal ring made of few elements, and dual head planar PET systems. In order to validate this approach, the SSA is applied to the SRM of a preclinical scanner (the IRIS PET/CT). The data acquired by the scanner were reconstructed with a dedicated maximum likelihood estimation maximization algorithm with both the uncompressed and the compressed SRMs. The results achieved show that the information lost due to the SSA compression is negligible. Compression factors up to 52 when using the SSA together with manually inserted symmetries and up to 204 when using the SSA alone, can be obtained for the IRIS SRM. These results come without significant differences in the values and in the main quality metrics of the reconstructed images, i.e. spatial resolution and noise. Although the compression factors depend on the system considered, the SSA is applicable to any SRM and therefore it can be considered a general tool to reduce the footprint of a pre-computed SRM.

Impact of a Bayesian penalized likelihood reconstruction algorithm on image quality in novel digital PET/CT: clinical implications for the assessment of lung tumors

Background

The aim of this study was to evaluate and compare PET image reconstruction algorithms on novel digital silicon photomultiplier PET/CT in patients with newly diagnosed and histopathologically confirmed lung cancer. A total of 45 patients undergoing 18F-FDG PET/CT for initial lung cancer staging were included. PET images were reconstructed using ordered subset expectation maximization (OSEM) with time-of-flight and point spread function modelling as well as Bayesian penalized likelihood reconstruction algorithm (BSREM) with different β-values yielding a total of 7 datasets per patient. Subjective and objective image assessment with all image datasets was carried out, including subgroup analyses for patients with high dose (> 2.0 MBq/kg) and low dose (≤ 2.0 MBq/kg) of 18F-FDG injection regimen.

Results

Subjective image quality ratings were significantly different among all different reconstruction algorithms as well as among BSREM using different β-values only (both p < 0.001). BSREM with a β-value of 600 was assigned the highest score for general image quality, image sharpness, and lesion conspicuity. BSREM reconstructions resulted in higher SUV_max of lung tumors compared to OSEM of up to + 28.0% (p < 0.001). BSREM reconstruction resulted in higher signal-/ and contrast-to-background ratios of lung tumor and higher signal-/ and contrast-to-noise ratio compared to OSEM up to a β-value of 800. Lower β-values (BSREM₄₅₀) resulted in the best image quality for high dose 18F-FDG injections, whereas higher β-values (BSREM₆₀₀) lead to the best image quality in low dose 18F-FDG PET/CT (p < 0.05).

Conclusions

BSREM reconstruction algorithm used in digital detector PET leads to significant increases of lung tumor SUV_max, signal-to-background ratio, and signal-to-noise ratio, which translates into a higher image quality, tumor conspicuity, and image sharpness.

Parameters Influencing PET Imaging Features: A Phantom Study with Irregular and Heterogeneous Synthetic Lesions

Aim

To evaluate reproducibility and stability of radiomic features as effects of the use of different volume segmentation methods and reconstruction settings. The potential of radiomics in really capturing the presence of heterogeneous tumor uptake and irregular shape was also investigated.

Materials and Methods

An anthropomorphic phantom miming real clinical situations including synthetic lesions with irregular shape and nonuniform radiotracer uptake was used. ¹⁸F-FDG PET/CT measurements of the phantom were performed including 38 lesions of different shape, size, lesion-to-background ratio, and radiotracer uptake distribution. Different reconstruction parameters and segmentation methods were considered. COVs were calculated to quantify feature variations over the different reconstruction settings. Friedman test was applied to the values of the radiomic features obtained for the considered segmentation approaches. Two sets of test-retest measurement were acquired and the pairwise intraclass correlation coefficient was calculated. Fifty-eight morphological and statistical features were extracted from the segmented lesion volumes. A Mann–Whitney test was used to evaluate significant differences among each feature when calculated from heterogeneous versus homogeneous uptake. The significance of each radiomic feature in terms of capturing heterogeneity was evaluated also by testing correlation with gold standard indexes of heterogeneity and sphericity.

Results

The choice of the segmentation method has a strong impact on the stability of radiomic features (less than 20% can be considered stable features). Reconstruction affects the estimate of radiomic features (only 26% are stable). Thirty-one radiomic features (53%) resulted to be reproducible, 11 of them are able to discriminate heterogeneity. Among these, we found a subset of 3 radiomic features strongly correlated with GS heterogeneity index that can be suggested as good features for retrospective evaluations.

Innovations in Instrumentation for Positron Emission Tomography

PET scanners are sophisticated and highly sensitive biomedical imaging devices that can produce highly quantitative images showing the 3-dimensional distribution of radiotracers inside the body. PET scanners are commonly integrated with x-ray CT or MRI scanners in hybrid devices that can provide both molecular imaging (PET) and anatomical imaging (CT or MRI). Despite decades of development, significant opportunities still exist to make major improvements in the performance of PET systems for a variety of clinical and research tasks. These opportunities stem from new ideas and concepts, as well as a range of enabling technologies and methodologies. In this paper, we review current state of the art in PET instrumentation, detectors and systems, describe the major limitations in PET as currently practiced, and offer our own personal insights into some of the recent and emerging technological innovations that we believe will impact the field. Our focus is on the technical aspects of PET imaging, specifically detectors and system design, and the opportunity and necessity to move closer to PET systems for diagnostic patient use and in vivo biomedical research that truly approach the physical performance limits while remaining mindful of imaging time, radiation dose, and cost. However, other key endeavors, which are not covered here, including innovations in reconstruction and modeling methodology, radiotracer development, and expanding the range of clinical and research applications, also will play an equally important, if not more important, role in defining the future of the field.