Assessment of Image Quality and Lesion Detectability With Digital PET/CT System

Image Quality of Cardiac Silicon Photomultiplier PET/CT Using an Infant Phantom of Extremely Low Birth Weight

The lack of pediatrics-specific equipment for nuclear medicine imaging has resulted in insufficient diagnostic information for newborns, especially low-birth-weight infants. Although PET offers high spatial resolution and low radiation exposure, its use in newborns is limited. This study investigated the feasibility of cardiac PET imaging using the latest silicon photomultiplier (SiPM) PET technology in infants of extremely low birth weight (ELBW) using a phantom model. Methods: The study used a phantom model representing a 500-g ELBW infant with brain, cardiac, liver, and lung tissues. The cardiac tissue included a 3-mm-thick defect mimicking myocardial infarction. Organ tracer concentrations were calculated assuming 18F-FDG myocardial viability scans and 18F-flurpiridaz myocardial perfusion scans and were added to the phantom organs. Imaging was performed using an SiPM PET/CT scanner with a 5-min acquisition. The data acquired in list mode were reconstructed using 3-dimensional ordered-subsets expectation maximization with varying iterations. Image evaluation was based on the depiction of the myocardial defect compared with normal myocardial accumulation. Results: Increasing the number of iterations improved the contrast of the myocardial defect for both tracers, with 18F-flurpiridaz showing higher contrast than 18F-FDG. However, even at 50 iterations, both tracers overestimated the defect accumulation. A bull's-eye image can display the flow metabolism mismatch using images from both tracers. Conclusion: SiPM PET enabled cardiac PET imaging in a 500-g ELBW phantom with a 1-g heart. However, there were limitations in adequately depicting these defects. Considering the image quality and defect contrast,18F-flurpiridaz appears more desirable than 18F-FDG if only one of the two can be used.

Two-step optimization for accelerating deep image prior-based PET image reconstruction

Deep learning, particularly convolutional neural networks (CNNs), has advanced positron emission tomography (PET) image reconstruction. However, it requires extensive, high-quality training datasets. Unsupervised learning methods, such as deep image prior (DIP), have shown promise for PET image reconstruction. Although DIP-based PET image reconstruction methods demonstrate superior performance, they involve highly time-consuming calculations. This study proposed a two-step optimization method to accelerate end-to-end DIP-based PET image reconstruction and improve PET image quality. The proposed two-step method comprised a pre-training step using conditional DIP denoising, followed by an end-to-end reconstruction step with fine-tuning. Evaluations using Monte Carlo simulation data demonstrated that the proposed two-step method significantly reduced the computation time and improved the image quality, thereby rendering it a practical and efficient approach for end-to-end DIP-based PET image reconstruction.

Diagnostic Performance of 18F-FDG PET/CT According to Delay After Treatment to Detect Subclinical Recurrence of Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a malignancy with high rates of locoregional recurrence and poor prognosis for recurrent cases. Early detection of subclinical lesions is challenging but critical for effective patient management. Imaging surveillance after treatment, particularly 18F-FDG PET/CT, has shown promise in the diagnosis of HNSCC recurrence. The aim was to evaluate the diagnostic performance of 18F-FDG PET/CT according to delay after treatment in detecting subclinical recurrence (SCR) in HNSCC patients. Methods: In this retrospective study, all 18F-FDG PET/CT scans were performed at a single center. All adults with histologically proven HNSCC who were treated with curative intent between January 1, 2006, and December 31, 2021, were included. They had a normal clinical examination before each scan. Patients who underwent an intensive follow-up strategy after treatment had 18F-FDG PET/CT with an intravenous contrast agent at 3-6 mo and annually thereafter for 5 y. The primary endpoint was diagnostic performance (positive and negative predictive values, sensitivity, specificity, and accuracy). Results: In total, 2,566 18F-FDG PET/CT scans were performed among 852 patients, with an average of 3 scans per patient. The overall diagnostic performance measures were as follows: positive predictive value (88%), negative predictive value (98%), sensitivity (98%), specificity (89%), and accuracy (93%). There were no significant differences in diagnostic performance over time. The scans detected 126 cases of SCR (14.8%) and 118 cases of metachronous cancer (13.8%). The incidence of SCR decreased over time, with the highest detection rate in the first 2 y after treatment. Positive predictive value improved over time, reaching 90% for the digital Vision 600 system (third period) compared with 76% for the analog Gemini GXLi system (first period, P < 0.001). Multivariate analysis identified advanced stage, high body mass index, and initial PET/CT upstaging as predictive factors for detection of SCR. Conclusion: Our study demonstrates that 18F-FDG PET/CT has high diagnostic performance in detecting SCR during follow-up after treatment of HNSCC, especially in the first 2 y. Advanced tumor stage, initial PET/CT upstaging, and high body mass index were associated with a higher likelihood of SCR detection. The routine use of 18F-FDG PET/CT during follow-up seems justified for patients with HNSCC.

Comparative study of physiological FDG uptake in small structures between silicon photomultiplier-based PET and conventional PET

OBJECTIVE

Silicon photomultiplier-based positron emission tomography/computed tomography (SiPM-PET/CT) has the superior spatial resolution to conventional PET/CT (cPET/CT). This head-to-head comparison study compared the images of physiological 18F-fluorodeoxyglucose (FDG) accumulation in small-volume structures between SiPM-PET/CT and cPET/CT in patients scanned with both modalities, and we investigated whether the thresholds that are reported to be useful for differentiating physiological accumulations from malignant lesions can also be applied to SiPM-PET/CT.

METHODS

We enrolled 21 consecutive patients with head and neck malignancies who underwent whole-body FDG-PET/CT for initial staging or a follow-up evaluation (October 2020 to March 2022). After being injected with FDG, all patients underwent PET acquisition on both Vereos PET-CT and Gemini TF64 PET-CT systems (both Philips Healthcare) in random order. For each patient, the maximum standardized uptake value (SUVmax) was measured in the pituitary gland, esophagogastric junction (EGJ), adrenal glands, lumbar enlargement of the spinal cord, and epididymis. We measured the liver SUVmean and the blood pool SUVmean to calculate the target-to-liver ratio (TLR) and the target-to-blood ratio (TBR), respectively. Between-groups differences in each variable were examined by a paired t-test. We also investigated whether there were cases of target uptake greater than the reported threshold for distinguishing pathological from physiological accumulations.

RESULTS

Data were available for 19 patients. Ten patients were in Group 1, i.e., the patients who underwent SiPM-PET first, and the remaining nine patients who underwent cPET first were in Group 2. In the SiPM-PET results, the SUVmax of all targets was significantly higher than that obtained by cPET in all patients, and this tendency was also observed when the patients were divided into Groups 1/2. The TLRs of all targets were significantly higher in SiPM-PET than in cPET in all patients, and SiPM-PET also showed significantly higher TBRs for all targets except the EGJ (p = 0.052).

CONCLUSIONS

The physiological uptake in the small structures studied herein showed high accumulation on SiPM-PET. Our results also suggest that the thresholds reported for cPET to distinguish pathological accumulations likely lead to false-positive findings in SIPM-PET evaluations.

Insertion of synthetic lesions on patient data: a method for evaluating clinical performance differences between PET systems

BACKGROUND

Performance assessment of positron emission tomography (PET) scanners is crucial to guide clinical practice with efficiency. We have already introduced and experimentally evaluated a simulation method allowing the creation of a controlled ground truth for system performance assessment. In the current study, the goal was to validate the method using patient data and demonstrate its relevance to assess PET performances accuracy in clinical conditions.

METHODS

Twenty-four patients were recruited and sorted into two groups according to their body mass index (BMI). They were administered with a single dose of 2 MBq/kg 18F-FDG and scanned using clinical protocols consecutively on two PET systems: the Discovery-IQ (DIQ) and the Discovery-MI (DMI). For each BMI group, sixty synthetic lesions were dispatched in three subgroups and inserted at relevant anatomical locations. Insertion of synthetic lesions (ISL) was performed at the same location into the two consecutive exams. Two nuclear medicine physicians evaluated individually and blindly the images by qualitatively and semi-quantitatively reporting each detected lesion and agreed on a consensus. We assessed the inter-system detection rates of synthetic lesions and compared it to an initial estimate of at least 1.7 more targets detected on the DMI and the detection rates of natural lesions. We determined the inter-reader variability, evaluated according to the inter-observer agreement (IOA). Adequate inter-reader variability was found for IOA above 80%. Differences in standardized uptake value (SUV) metrics were also studied.

RESULTS

In the BMI ≤ 25 group, the relative true positive rate (RTPR) for synthetic and natural lesions was 1.79 and 1.83, respectively. In the BMI > 25 group, the RTPR for synthetic and natural lesions was 2.03 and 2.27, respectively. For each BMI group, the detection rate using ISL was consistent to our estimate and with the detection rate measured on natural lesions. IOA above 80% was verified for any scenario. SUV metrics showed a good agreement between synthetic and natural lesions.

CONCLUSIONS

ISL proved relevant to evaluate performance differences between PET scanners. Using these synthetically modified clinical images, we can produce a controlled ground truth in a realistic anatomical model and exploit the potential of PET scanner for clinical purposes.

Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner

PURPOSE

18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner.

MATERIALS AND METHODS

A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability.

RESULTS

In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features.

CONCLUSION

Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.

PET imaging and quantification of small animals using a clinical SiPM-based camera

BACKGROUND

Small-animal PET imaging is an important tool in preclinical oncology. This study evaluated the ability of a clinical SiPM-PET camera to image several rats simultaneously and to perform quantification data analysis.

METHODS

Intrinsic spatial resolution was measured using 18F line sources, and image quality was assessed using a NEMA NU 4-2018 phantom. Quantification was evaluated using a fillable micro-hollow sphere phantom containing 4 spheres of different sizes (ranging from 3.95 to 7.86 mm). Recovery coefficients were computed for the maximum (Amax) and the mean (A50) pixel values measured on a 50% isocontour drawn on each sphere. Measurements were performed first with the phantom placed in the centre of the field of view and then in the off-centre position with the presence of three scattering sources to simulate the acquisition of four animals simultaneously. Quantification accuracy was finally validated using four 3D-printed phantoms mimicking rats with four subcutaneous tumours each. All experiments were performed for both 18F and 68Ga radionuclides.

RESULTS

Radial spatial resolutions measured using the PSF reconstruction algorithm were 1.80 mm and 1.78 mm for centred and off-centred acquisitions, respectively. Spill-overs in air and water and uniformity computed with the NEMA phantom centred in the FOV were 0.05, 0.1 and 5.55% for 18F and 0.08, 0.12 and 2.81% for 68Ga, respectively. Recovery coefficients calculated with the 18F-filled micro-hollow sphere phantom for each sphere varied from 0.51 to 1.43 for Amax and from 0.40 to 1.01 for A50. These values decreased from 0.28 to 0.92 for Amax and from 0.22 to 0.66 for A50 for 68 Ga acquisition. The results were not significantly different when imaging phantoms in the off-centre position with 3 scattering sources. Measurements performed with the four 3D-printed phantoms showed a good correlation between theoretical and measured activity in simulated tumours, with r2 values of 0.99 and 0.97 obtained for 18F and 68Ga, respectively.

CONCLUSION

We found that the clinical SiPM-based PET system was close to that obtained with a dedicated small-animal PET device. This study showed the ability of such a system to image four rats simultaneously and to perform quantification analysis for radionuclides commonly used in oncology.

Automated F18-FDG PET/CT image quality assessment using deep neural networks on a latest 6-ring digital detector system

To evaluate whether a machine learning classifier can evaluate image quality of maximum intensity projection (MIP) images from F18-FDG-PET scans. A total of 400 MIP images from F18-FDG-PET with simulated decreasing acquisition time (120 s, 90 s, 60 s, 30 s and 15 s per bed-position) using block sequential regularized expectation maximization (BSREM) with a beta-value of 450 and 600 were created. A machine learning classifier was fed with 283 images rated "sufficient image quality" and 117 images rated "insufficient image quality". The classification performance of the machine learning classifier was assessed by calculating sensitivity, specificity, and area under the receiver operating characteristics curve (AUC) using reader-based classification as the target. Classification performance of the machine learning classifier was AUC 0.978 for BSREM beta 450 and 0.967 for BSREM beta 600. The algorithm showed a sensitivity of 89% and 94% and a specificity of 94% and 94% for the reconstruction BSREM 450 and 600, respectively. Automated assessment of image quality from F18-FDG-PET images using a machine learning classifier provides equivalent performance to manual assessment by experienced radiologists.

Multicentric development and evaluation of 18F-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer

PURPOSE

To develop machine learning models to predict para-aortic lymph node (PALN) involvement in patients with locally advanced cervical cancer (LACC) before chemoradiotherapy (CRT) using 18F-FDG PET/CT and MRI radiomics combined with clinical parameters.

METHODS

We retrospectively collected 178 patients (60% for training and 40% for testing) in 2 centers and 61 patients corresponding to 2 further external testing cohorts with LACC between 2010 to 2022 and who had undergone pretreatment analog or digital 18F-FDG PET/CT, pelvic MRI and surgical PALN staging. Only primary tumor volumes were delineated. Radiomics features were extracted using the Radiomics toolbox®. The ComBat harmonization method was applied to reduce the batch effect between centers. Different prediction models were trained using a neural network approach with either clinical, radiomics or combined models. They were then evaluated on the testing and external validation sets and compared.

RESULTS

In the training set (n = 102), the clinical model achieved a good prediction of the risk of PALN involvement with a C-statistic of 0.80 (95% CI 0.71, 0.87). However, it performed in the testing (n = 76) and external testing sets (n = 30 and n = 31) with C-statistics of only 0.57 to 0.67 (95% CI 0.36, 0.83). The ComBat-radiomic (GLDZM_HISDE_PET_FBN64 and Shape_maxDiameter2D3_PET_FBW0.25) and ComBat-combined (FIGO 2018 and same radiomics features) models achieved very high predictive ability in the training set and both models kept the same performance in the testing sets, with C-statistics from 0.88 to 0.96 (95% CI 0.76, 1.00) and 0.85 to 0.92 (95% CI 0.75, 0.99), respectively.

CONCLUSIONS

Radiomic features extracted from pre-CRT analog and digital 18F-FDG PET/CT outperform clinical parameters in the decision to perform a para-aortic node staging or an extended field irradiation to PALN. Prospective validation of our models should now be carried out.

Enhancement of 18F-Fluorodeoxyglucose PET Image Quality by Deep-Learning-Based Image Reconstruction Using Advanced Intelligent Clear-IQ Engine in Semiconductor-Based PET/CT Scanners

Deep learning (DL) image quality improvement has been studied for application to ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT). It is unclear, however, whether DL can increase the quality of images obtained with semiconductor-based PET/CT scanners. This study aimed to compare the quality of semiconductor-based PET/CT scanner images obtained by DL-based technology and conventional OSEM image with Gaussian postfilter. For DL-based data processing implementation, we used Advanced Intelligent Clear-IQ Engine (AiCE, Canon Medical Systems, Tochigi, Japan) and for OSEM images, Gaussian postfilter of 3 mm FWHM is used. Thirty patients who underwent semiconductor-based PET/CT scanner imaging between May 6, 2021, and May 19, 2021, were enrolled. We compared AiCE images and OSEM images and scored them for delineation, image noise, and overall image quality. We also measured standardized uptake values (SUVs) in tumors and healthy tissues and compared them between AiCE and OSEM. AiCE images scored significantly higher than OSEM images for delineation, image noise, and overall image quality. The Fleiss kappa value for the interobserver agreement was 0.57. Among the 21 SUV measurements in healthy organs, 11 (52.4%) measurements were significantly different between AiCE and OSEM images. More pathological lesions were detected in AiCE images as compared with OSEM images, with AiCE images showing higher SUVs for pathological lesions than OSEM images. AiCE can improve the quality of images acquired with semiconductor-based PET/CT scanners, including the noise level, contrast, and tumor detection capability.

Digital PET/CT with 18F-FACBC in early castration-resistant prostate cancer: our preliminary results

BACKGROUND

We assessed the role of digital PET/CT (dPET/CT) with ¹⁸F-FACBC in patients affected by castration-resistant prostate cancer with PSA levels ≤ 3 ng/mL (early CRPC), no lesions detectable at cross-sectional imaging (CIM) and bone scan (BS).

METHODS

Clinical data of patients submitted to ¹⁸F-FACBC dPET/CT were retrospectively reviewed. PET/CT results were analyzed: lesions' number, location, and, in case of positive lymph nodes, largest node's short axis (i.e. SA) were annotated. According to PET/CT's results, patients with ¹⁸F-FACBC-avid lesions were further stratified into 1) unifocal; 2) oligometastatic (≤ 5 lesions); 3) disseminated (> 6 lesions).

RESULTS

Twenty-four patients were enrolled. ¹⁸F-FACBC dPET/CT was positive in 21 out of 24 patients (87.5%). Thirteen patients (54.1%) showed recurrence in pelvic region, seven of whom with pelvic nodes' involvement, while eight cases (33.3%) presented ¹⁸F-FACBC-avid metastases to extra-pelvic nodes or bone. Average SA of PET-positive nodes resulted in 8.9 ± 3 mm. Patients were categorized as unifocal in four cases (26.6%), oligometastatic in 10 subjects (66.6%) and disseminated in 1 case (0.6%). PET/CT impacted on clinical management in 14 cases (58.3%).

CONCLUSIONS

¹⁸F-FACBC dPET/CT detected M1 status in 33.3% of early CRPC patients, significantly impacting on clinical management.

Dynamic PET Imaging Using Dual Texture Features

Purpose: This study aims to explore the impact of adding texture features in dynamic positron emission tomography (PET) reconstruction of imaging results. Methods: We have improved a reconstruction method that combines radiological dual texture features. In this method, multiple short time frames are added to obtain composite frames, and the image reconstructed by composite frames is used as the prior image. We extract texture features from prior images by using the gray level-gradient cooccurrence matrix (GGCM) and gray-level run length matrix (GLRLM). The prior information contains the intensity of the prior image, the inverse difference moment of the GGCM and the long-run low gray-level emphasis of the GLRLM. Results: The computer simulation results show that, compared with the traditional maximum likelihood, the proposed method obtains a higher signal-to-noise ratio (SNR) in the image obtained by dynamic PET reconstruction. Compared with similar methods, the proposed algorithm has a better normalized mean squared error (NMSE) and contrast recovery coefficient (CRC) at the tumor in the reconstructed image. Simulation studies on clinical patient images show that this method is also more accurate for reconstructing high-uptake lesions. Conclusion: By adding texture features to dynamic PET reconstruction, the reconstructed images are more accurate at the tumor.

Digital PET and detection of recurrent prostate cancer: what have we gained, and what is still missing?

Defined by the TIME magazine 'medical invention of the year 2000,' positron emission computed tomography (PET/CT) has experienced impressive improvements in technology and clinical applications over time. In recent years, silicon photomultipliers (SiPMs) detectors, characterized by excellent intrinsic time resolution and high photon-detection efficiency, have been introduced as an alternative to the classic photomultiplier tubes (PMTs), thus moving the field of PET technology forward and leading to the so-called digital PET/CT (dPET/CT). On the other side, the radiopharmaceutical ⁶⁸Ga-PSMA-11, approved by the Food and Drug Administration in December 2020, proved to strongly impact prostate cancer (PCa) diagnosis and management. In the study under evaluation, Alberts et al. retrospectively compared the performance of dPET/CT and PMTs-based PET/CT, namely analogue PET/CT (aPET/CT), in two cohorts, each one including 65 patients undergoing PET/CT with ⁶⁸Ga-PSMA-11 for suspected recurrent PCa. The authors found that dPET/CT presented a higher detection rate of pathological lesions with respect to aPET/CT. Of note, dPET/CT's higher sensitivity results are associated with an increased true-positive rate and high inter-reader agreement. This report underscores how innovative PET/CT instrumentation, by utilizing novel radiopharmaceuticals targeting specific metabolic/molecular signatures expressed by PCa, may represent a successful weapon in uro-oncology.