Optimization of PET/CT image quality using the GE 'Sharp IR' point-spread function reconstruction algorithm

Impact of reconstruction algorithms at different sphere-to-background ratios on PET quantification: A phantom study

Using National Electrical Manufacturers Association (NEMA) phantom, the behavior of four distinct Positron Emission Tomography/Computed Tomography (PET/CT) reconstruction algorithms was investigated. These reconstruction algorithms were (Ordered Subset Expectation Maximization (OSEM), OSEM+ (Point Spread Function) PSF, OSEM + Time of Flight (TOF), and OSEM + TOF + PSF), and the focus was on sphere sizes and SBRs using recovery coefficients as a quantitation method. The obtained results demonstrated the significant effect of TOF on Gibbs artifact and Partial Volume Effect (PVE) at various Sphere-to-Background Ratios (SBRs). TOF-based algorithms improved quantification accuracy and mitigated the influence of Gibbs artifact, particularly at higher SBRs. Compared to PSF algorithm, TOF- based algorithms effectively mitigated the impact of PVE on small-sized spheres and less dependent on SBRs. In terms of Standardized Uptake Value (SUV) quantification, SUVmean was better when utilizing TOF-based algorithms at lower SBRs, whereas SUVmax at higher SBRs. The combination of TOF and PSF produced a promising outcomes in quantifying and detecting a small-sized spheres across various SBRs, ultimately resulting in a more reliable and precise diagnostic information.

Optimization of Reconstruction Parameters for Discovery 710 Positron Emission Tomography/Computed Tomography

Aim

This study aimed to optimize the quantitative aspects of (18F) fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) imaging by investigating the impact of various reconstruction parameters on the recovery coefficients (RCs) using the NEMA image quality phantom. Specifically, the study aims to assess how different matrix sizes, iterations, subsets, and Gaussian postfilters affect the accuracy of standardized uptake value (SUV) quantification in (18F) FDG PET/CT imaging.

Materials and Methods

The study utilized the "Vue Point FX + Sharp IR" algorithm for PET image reconstruction, incorporating 3D-ordered subset expectation maximization (3D-OSEM), time-of-flight, and point spread function technologies. Various reconstruction parameters were explored, including two distinct matrix sizes, multiple iterations, subsets, and a wide range of Gaussian postfilters. The investigation focused on the impact of these parameters on RCs using the NEMA image quality phantom.

Results

The results of the study indicated that for accurate SUV quantification in spheres ≥17 mm, the 256 × 256 matrix size and mean SUV should be employed. Conversely, for spheres ≤13 mm, maximum SUV was found to be more suitable. The choice of postfiltering value was shown to have a significant impact on SUV quantification accuracy, particularly for small-sized spheres. In addition, a larger matrix size was found to partially mitigate the effects of Gibbs artifact and slightly enhance SUV quantification for the spheres of various sizes.

Conclusion

This study highlights the critical importance of optimizing PET reconstruction parameters in accordance with the guidelines set by European Association of Nuclear Medicine/EARL. By optimizing these parameters, the accuracy and reliability of SUV quantification in (18F) FDG PET imaging can be significantly enhanced, especially for small-sized spheres. This underscores the necessity of carefully considering reconstruction parameters to ensure precise and reliable quantitative measurements in PET/CT imaging.

Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm

Positron Emission Tomography (PET) imaging after90 Y liver radioembolization is used for both lesion identification and dosimetry. Bayesian penalized likelihood (BPL) reconstruction algorithms are an alternative to ordered subset expectation maximization (OSEM) with improved image quality and lesion detectability. The investigation of optimal parameters for90 Y image reconstruction of Q.Clear, a commercial BPL algorithm developed by General Electric (GE), in PET/MR is a field of interest and the subject of this study. The NEMA phantom was filled at an 8:1 sphere-to-background ratio. Acquisitions were performed on a PET/MR scanner for clinically relevant activities between 0.7 and 3.3 MBq/ml. Reconstructions with Q.Clear were performed varying the β penalty parameter between 20 and 6000, the acquisition time between 5 and 20 min and pixel size between 1.56 and 4.69 mm. OSEM reconstructions of 28 subsets with 2 and 4 iterations with and without Time-of-Flight (TOF) were compared to Q.Clear with β = 4000. Recovery coefficients (RC), their coefficient of variation (COV), background variability (BV), contrast-to-noise ratio (CNR) and residual activity in the cold insert were evaluated. Increasing β parameter lowered RC, COV and BV, while CNR was maximized at β = 4000; further increase resulted in oversmoothing. For quantification purposes, β = 1000-2000 could be more appropriate. Longer acquisition times resulted in larger CNR due to reduced image noise. Q.Clear reconstructions led to higher CNR than OSEM. A β of 4000 was obtained for optimal image quality, although lower values could be considered for quantification purposes. An optimal acquisition time of 15 min was proposed considering its clinical use.

Investigating the Impact of Voxel Size and Postfiltering on Quantitative Analysis of Positron Emission Tomography/Computed Tomography: A Phantom Study

Aim

This study aims to investigate the influence of voxel size and postfiltering on the quantification of standardized uptake value (SUV) in positron emission tomography/computed tomography (PET/CT) images.

Materials and Methods

National Electrical Manufacturers Association phantom with the spheres of different sizes were utilized to simulate the lesions. The phantom was scanned using a PET/CT scanner, and the acquired images were reconstructed using two different matrix sizes, (192 × 192) and (256 × 256), and a wide range of postfiltering values.

Results

The findings demonstrated that postfiltering significantly affected SUV measurements. The changes in postfiltering values can result in overestimation or underestimation of SUV values, highlighting the importance of carefully selecting appropriate filters. Increasing the matrix size improved SUVmax and SUVmean values, particularly for small-sized spheres. Smaller voxel reconstructions slightly reduced partial volume effects and partially enhanced SUV quantification.

Conclusions

Careful consideration of postfiltering values and matrix size selection can lead to better SUV quantification. These findings emphasize the need to optimize the reconstruction parameters to enhance the clinical utility of PET/CT in detecting and evaluating malignant lesions.

The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine

Cardiovascular diseases (CVD) are the leading cause of death worldwide and have an increasing impact on society. Precision medicine, in which optimal care is identified for an individual or a group of individuals rather than for the average population, might provide significant health benefits for this patient group and decrease CVD morbidity and mortality. Molecular imaging provides the opportunity to assess biological processes in individuals in addition to anatomical context provided by other imaging modalities and could prove to be essential in the implementation of precision medicine in CVD. New developments in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) systems, combined with rapid innovations in promising and specific radiopharmaceuticals, provide an impressive improvement of diagnostic accuracy and therapy evaluation. This may result in improved health outcomes in CVD patients, thereby reducing societal impact. Furthermore, recent technical advances have led to new possibilities for accurate image quantification, dynamic imaging, and quantification of radiotracer kinetics. This potentially allows for better evaluation of disease activity over time and treatment response monitoring. However, the clinical implementation of these new methods has been slow. This review describes the recent advances in molecular imaging and the clinical value of quantitative PET and SPECT in various fields in cardiovascular molecular imaging, such as atherosclerosis, myocardial perfusion and ischemia, infiltrative cardiomyopathies, systemic vascular diseases, and infectious cardiovascular diseases. Moreover, the challenges that need to be overcome to achieve clinical translation are addressed, and future directions are provided.

Development of a bespoke phantom to optimize molecular PET imaging of pituitary tumors

BACKGROUND

Image optimization is a key step in clinical nuclear medicine, and phantoms play an essential role in this process. However, most phantoms do not accurately reflect the complexity of human anatomy, and this presents a particular challenge when imaging endocrine glands to detect small (often subcentimeter) tumors. To address this, we developed a novel phantom for optimization of positron emission tomography (PET) imaging of the human pituitary gland. Using radioactive 3D printing, phantoms were created which mimicked the distribution of ¹¹C-methionine in normal pituitary tissue and in a small tumor embedded in the gland (i.e., with no inactive boundary, thereby reproducing the in vivo situation). In addition, an anatomical phantom, replicating key surrounding structures [based on computed tomography (CT) images from an actual patient], was created using material extrusion 3D printing with specialized filaments that approximated the attenuation properties of bone and soft tissue.

RESULTS

The phantom enabled us to replicate pituitary glands harboring tumors of varying sizes (2, 4 and 6 mm diameters) and differing radioactive concentrations (2 ×, 5 × and 8 × the normal gland). The anatomical phantom successfully approximated the attenuation properties of surrounding bone and soft tissue. Two iterative reconstruction algorithms [ordered subset expectation maximization (OSEM); Bayesian penalized likelihood (BPL)] with a range of reconstruction parameters (e.g., 3, 5, 7 and 9 OSEM iterations with 24 subsets; BPL regularization parameter (β) from 50 to 1000) were tested. Images were analyzed quantitatively and qualitatively by eight expert readers. Quantitatively, signal was the highest using BPL with β = 50; noise was the lowest using BPL with β = 1000; contrast was the highest using BPL with β = 100. The qualitative review found that accuracy and confidence were the highest when using BPL with β = 400.

CONCLUSIONS

The development of a bespoke phantom has allowed the identification of optimal parameters for molecular pituitary imaging: BPL reconstruction with TOF, PSF correction and a β value of 400; in addition, for small (< 4 mm) tumors with low contrast (2:1 or 5:1), sensitivity may be improved using a β value of 100. Together, these findings should increase tumor detection and confidence in reporting scans.

Absolute Resting 13N-Ammonia PET Myocardial Blood Flow for Predicting Myocardial Viability and Recovery of Ventricular Function after Coronary Artery Bypass Grafting

OBJECTIVE

We aimed to evaluate the feasibility of resting myocardial blood flow (rMBF), quantified with dynamic 13 N-Ammonia (NH3) PET, for identifying myocardial viability and predicting improvement of left ventricular ejection fraction (LVEF) after coronary artery bypass grafting (CABG).

METHODS

Ninety-three patients with coronary artery disease (CAD) and chronic LVEF < 45%, scheduled for CABG, had dynamic 13NH3 PET and 18F-FDG PET imaging. The perfusion/metabolism polar maps were categorized in four patterns: normal (N), mismatch (M1), match (M2) and reverse mismatch (RM). The value of rMBF for identifying viable myocardium (M1, RM) and post CABG improvement of LVEF≥8% was analyzed by receiver operating characteristic (ROC) curves. Correlations of rMBF in segments to ΔLVEF post CABG were verified.

RESULTS

Mean rMBFs were significantly different (N=0.60±0.14; M1=0.44±0.07, M2=0.34±0.08, RM=0.53±0.09 ml/min/g, P<0.001). The optimal rMBF cutoff to identify viable myocardium was 0.42 ml/min/g (sensitivity=88.3%, specificity=82.0%) and 0.43 ml/min/g for predicting improvement of LVEF ≥8% (74.6%, 80.0%). The extent and rMBF of combined M1/RM demonstrated a moderate to high correlation to improved LVEF (r=0.78, 0.71, P<0.001).

CONCLUSION

Resting MBF, derived by dynamic 13NH3 PET, may be positioned as a supplement to 18F-FDG PET imaging for assessing the presence of viable myocardium and predicting potential improvement of LVEF after CABG.

BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients

The aim of the study was to analyze the use of block sequential regularized expectation maximization (BSREM) with different β-values for the detection of brain metastases in digital fluorine-18 labeled 2-deoxy-2-fluoro-D-glucose (18F-FDG) PET/CT in lung cancer patients. We retrospectively analyzed staging/restaging 18F-FDG PET/CT scans of 40 consecutive lung cancer patients with new brain metastases, confirmed by MRI. PET images were reconstructed using BSREM (β-values of 100, 200, 300, 400, 500, 600, 700) and OSEM. Two independent blinded readers (R1 and R2) evaluated each reconstruction using a 4-point scale for general image quality, noise, and lesion detectability. SUVmax of metastases, brain background, target-to-background ratio (TBR), and contrast recovery (CR) ratio were recorded for each reconstruction. Among all reconstruction techniques, differences in qualitative parameters were analyzed using non-parametric Friedman test, while differences in quantitative parameters were compared using analysis of variances for repeated measures. Cohen's kappa (k) was used to measure inter-reader agreement. The overall detectability of brain metastases was highest for BSREM200 (R1: 2.83 ± 1.17; R2: 2.68 ± 1.32) and BSREM300 (R1: 2.78 ± 1.23; R2: 2.68 ± 1.36), followed by BSREM100, which had lower accuracy owing to noise. The highest median TBR was found for BSREM100 (R1: 2.19 ± 1.05; R2: 2.42 ± 1.08), followed by BSREM200 and BSREM300. Image quality ratings were significantly different among reconstructions (p < 0.001). The median quality score was higher for BSREM100-300, and both noise and metastases' SUVmax decreased with increasing β-value. Inter-reader agreement was particularly high for the detectability of photopenic metastases and blurring (all k > 0.65). BSREM200 and BSREM300 yielded the best results for the detection of brain metastases, surpassing both BSREM400 and OSEM, typically used in clinical practice.

Computerized Tomography Image Features under the Reconstruction Algorithm in the Evaluation of the Effect of Ropivacaine Combined with Dexamethasone and Dexmedetomidine on Assisted Thoracoscopic Lobectomy

This research was aimed to study CT image features based on the backprojection filtering reconstruction algorithm and evaluate the effect of ropivacaine combined with dexamethasone and dexmedetomidine on assisted thoracoscopic lobectomy to provide reference for clinical diagnosis. A total of 110 patients undergoing laparoscopic resection were selected as the study subjects. Anesthesia induction and nerve block were performed with ropivacaine combined with dexamethasone and dexmedetomidine before surgery, and chest CT scan was performed. The backprojection image reconstruction algorithm was constructed and applied to patient CT images for reconstruction processing. The results showed that when the overlapping step size was 16 and the block size was 32 × 32, the running time of the algorithm was the shortest. The resolution and sharpness of reconstructed images were better than the Fourier transform analytical method and iterative reconstruction algorithm. The detection rates of lung nodules smaller than 6 mm and 6–30 mm (92.35% and 95.44%) were significantly higher than those of the Fourier transform analytical method and iterative reconstruction algorithm (90.98% and 87.53%; 88.32% and 90.87%) (P < 0.05). After anesthesia induction and lobectomy with ropivacaine combined with dexamethasone and dexmedetomidine, the visual analogue scale (VAS) decreased with postoperative time. The VAS score decreased to a lower level (1.76 ± 0.54) after five days. In summary, ropivacaine combined with dexamethasone and dexmedetomidine had better sedation and analgesia effects in patients with thoracoscopic lobectomy. CT images based on backprojection reconstruction algorithm had a high recognition accuracy for lung lesions.

Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT

Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms.

Methods: This retrospective study included 140 patients who underwent [¹⁸F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning.

Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort.

Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.

The impact of iterative reconstruction protocol, signal-to-background ratio and background activity on measurement of PET spatial resolution

OBJECTIVES

The present study aims to assess the impact of acquisition time, different iterative reconstruction protocols as well as image context (including contrast levels and background activities) on the measured spatial resolution in PET images.

METHODS

Discovery 690 PET/CT scanner was used to quantify spatial resolutions in terms of full width half maximum (FWHM) as derived (i) directly from capillary tubes embedded in air and (ii) indirectly from 10 mm-diameter sphere of the NEMA phantom. Different signal-to-background ratios (SBRs), background activity levels and acquisition times were applied. The emission data were reconstructed using iterative reconstruction protocols. Various combinations of iterations and subsets (it × sub) were evaluated.

RESULTS

For capillary tubes, improved FWHM values were obtained for higher it × sub, with improved performance for PSF algorithms relative to non-PSF algorithms. For the NEMA phantom, by increasing acquisition times from 1 to 5 min, intrinsic FWHM for reconstructions with it × sub 32 (54) was improved by 15.3% (13.2%), 15.1% (13.8%), 14.5% (12.8%) and 13.7% (12.7%) for OSEM, OSEM + PSF, OSEM + TOF and OSEM + PSF + TOF, respectively. Furthermore, for all reconstruction protocols, the FWHM improved with more impact for higher it × sub.

CONCLUSION

Our results indicate that PET spatial resolution is greatly affected by SBR, background activity and the choice of the reconstruction protocols.

Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging

OBJECTIVES

We aim to develop and rigorously evaluate an image-based deconvolution method to jointly compensate respiratory motion and partial volume effects (PVEs) for quantitative oncologic PET imaging, including studying the impact of various reconstruction algorithms on quantification performance.

PROCEDURES

An image-based deconvolution method that incorporated wavelet-based denoising within the Lucy-Richardson algorithm was implemented and assessed. The method was evaluated using phantom studies with signal-to-background ratios (SBR) of 4 and 8, and clinical data of 10 patients with 42 lung lesions ≤30 mm in diameter. In each study, PET images were reconstructed using four different algorithms: OSEM-basic, PSF, TOF, and TOFPSF. The performance was quantified using contrast recovery (CR), coefficient of variation (COV) and contrast-to-noise-ratio (CNR) metrics. Further, in each study, variabilities arising due to the four different reconstruction algorithms were assessed.

RESULTS

In phantom studies, incorporation of wavelet-based denoising improved COV in all cases. Processing images using proposed method yielded significantly higher CR and CNR particularly in small spheres, for all reconstruction algorithms and all SBRs (P < 0.05). In patient studies, processing images using the proposed method yielded significantly higher CR and CNR (P < 0.05). The choice of the reconstruction algorithm impacted quantification performance for changes in motion amplitude, tumor size and SBRs.

CONCLUSIONS

Our results provide strong evidence that the proposed joint-compensation method can yield improved PET quantification. The choice of the reconstruction algorithm led to changes in quantitative accuracy, emphasizing the need to carefully select the right combination of reconstruction-image-based compensation methods.