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

Characterization of hypermetabolic lymph nodes after SARS-CoV-2 vaccination using PET-CT derived node-RADS, in patients with melanoma

This study aimed to evaluate the diagnostic accuracy of Node Reporting and Data System (Node-RADS) in discriminating between normal, reactive, and metastatic axillary LNs in patients with melanoma who underwent SARS-CoV-2 vaccination. Patients with proven melanoma who underwent a 2-[$^{18}$F]-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[$^{18}$F]-FDG PET/CT) between February and April 2021 were included in this retrospective study. Primary melanoma site, vaccination status, injection site, and 2-[$^{18}$F]-FDG PET/CT were used to classify axillary LNs into normal, inflammatory, and metastatic (combined classification). An adapted Node-RADS classification (A-Node-RADS) was generated based on LN anatomical characteristics on low-dose CT images and compared to the combined classification. 108 patients were included in the study (54 vaccinated). HALNs were detected in 42 patients (32.8%), of whom 97.6% were vaccinated. 172 LNs were classified as normal, 30 as inflammatory, and 14 as metastatic using the combined classification. 152, 22, 29, 12, and 1 LNs were classified A-Node-RADS 1, 2, 3, 4, and 5, respectively. Hence, 174, 29, and 13 LNs were deemed benign, equivocal, and metastatic. The concordance between the classifications was very good (Cohen's k: 0.91, CI 0.86-0.95; p-value < 0.0001). A-Node-RADS can assist the classification of axillary LNs in melanoma patients who underwent 2-[$^{18}$F]-FDG PET/CT and SARS-CoV-2 vaccination

Opportunistic deep learning powered calcium scoring in oncologic patients with very high coronary artery calcium (≥ 1000) undergoing 18F-FDG PET/CT

Our aim was to identify and quantify high coronary artery calcium (CAC) with deep learning (DL)-powered CAC scoring (CACS) in oncological patients with known very high CAC (≥ 1000) undergoing 18F-FDG-PET/CT for re-/staging. 100 patients were enrolled: 50 patients with Agatston scores ≥ 1000 (high CACS group), 50 patients with Agatston scores < 1000 (negative control group). All patients underwent oncological 18F-FDG-PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on dedicated non-contrast ECG-gated CT scans obtained from SPECT-MPI (reference standard). Additionally, CACS was performed fully automatically with a user-independent DL-CACS tool on non-contrast, free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations. Image quality and noise of CT scans was assessed. Agatston scores obtained by manual CACS and DL tool were compared. The high CACS group had Agatston scores of 2200 ± 1620 (reference standard) and 1300 ± 1011 (DL tool, average underestimation of 38.6 ± 26%) with an intraclass correlation of 0.714 (95% CI 0.546, 0.827). Sufficient image quality significantly improved the DL tool's capability of correctly assigning Agatston scores ≥ 1000 (p = 0.01). In the control group, the DL tool correctly assigned Agatston scores < 1000 in all cases. In conclusion, DL-based CACS performed on non-contrast free-breathing, non-gated CT scans from 18F-FDG-PET/CT examinations of patients with known very high (≥ 1000) CAC underestimates CAC load, but correctly assigns an Agatston scores ≥ 1000 in over 70% of cases, provided sufficient CT image quality. Subgroup analyses of the control group showed that the DL tool does not generate false-positives

A third of the radiotracer dose: two decades of progress in pediatric [18^{18}F]fluorodeoxyglucose PET/CT and PET/MR imaging

OBJECTIVES To assess the evolution of administered radiotracer activity for F-18-fluorodeoxyglucose (18F-FDG) PET/CT or PET/MR in pediatric patients (0-16 years) between years 2000 and 2021. METHODS Pediatric patients (≤ 16 years) referred for 18F-FDG PET/CT or PET/MR imaging of the body during 2000 and 2021 were retrospectively included. The amount of administered radiotracer activity in megabecquerel (MBq) was recorded, and signal-to-noise ratio (SNR) was measured in the right liver lobe with a 4 cm$^{3}$ volume of interest as an indicator for objective image quality. Descriptive statistics were computed. RESULTS Two hundred forty-three children and adolescents underwent a total of 466 examinations. The median injected 18F-FDG activity in MBq decreased significantly from 296 MBq in 2000-2005 to 100 MBq in 2016-2021 (p < 0.001), equaling approximately one-third of the initial amount. The median SNR ratio was stable during all years with 11.7 (interquartile range [IQR] 10.7-12.9, p = 0.133). CONCLUSIONS Children have benefited from a massive reduction in the administered 18F-FDG dose over the past 20 years without compromising objective image quality. CLINICAL RELEVANCE STATEMENT Radiotracer dose was reduced considerably over the past two decades of pediatric F-18-fluorodeoxyglucose PET/CT and PET/MR imaging highlighting the success of technical innovations in pediatric PET imaging. KEY POINTS • The evolution of administered radiotracer activity for F-18-fluorodeoxyglucose (18F-FDG) PET/CT or PET/MR in pediatric patients (0-16 years) between 2000 and 2021 was assessed. • The injected tracer activity decreased by 66% during the study period from 296 megabecquerel (MBq) to 100 MBq (p < 0.001). • The continuous implementation of technical innovations in pediatric hybrid 18F-FDG PET has led to a steady decrease in the amount of applied radiotracer, which is particularly beneficial for children who are more sensitive to radiation

Diagnostic Value of Fully Automated Artificial Intelligence Powered Coronary Artery Calcium Scoring from 18F-FDG PET/CT

OBJECTIVES The objective of this study was to assess the feasibility and accuracy of a fully automated artificial intelligence (AI) powered coronary artery calcium scoring (CACS) method on ungated CT in oncologic patients undergoing 18F-FDG PET/CT. METHODS A total of 100 oncologic patients examined between 2007 and 2015 were retrospectively included. All patients underwent 18F-FDG PET/CT and cardiac SPECT myocardial perfusion imaging (MPI) by 99mTc-tetrofosmin within 6 months. CACS was manually performed on non-contrast ECG-gated CT scans obtained from SPECT-MPI (i.e., reference standard). Additionally, CACS was performed using a cloud-based, user-independent tool (AI-CACS) on ungated CT scans from 18F-FDG-PET/CT examinations. Agatston scores from the manual CACS and AI-CACS were compared. RESULTS On a per-patient basis, the AI-CACS tool achieved a sensitivity and specificity of 85% and 90% for the detection of CAC. Interscore agreement of CACS between manual CACS and AI-CACS was 0.88 (95% CI: 0.827, 0.918). Interclass agreement of risk categories was 0.8 in weighted Kappa analysis, with a reclassification rate of 44% and an underestimation of one risk category by AI-CACS in 39% of cases. On a per-vessel basis, interscore agreement of CAC scores ranged from 0.716 for the circumflex artery to 0.863 for the left anterior descending artery. CONCLUSIONS Fully automated AI-CACS as performed on non-contrast free-breathing, ungated CT scans from 18F-FDG-PET/CT examinations is feasible and provides an acceptable to good estimation of CAC burden. CAC load on ungated CT is, however, generally underestimated by AI-CACS, which should be taken into account when interpreting imaging findings

Splenic switch-off as a predictor for coronary adenosine response: validation against 13N-ammonia during co-injection myocardial perfusion imaging on a hybrid PET/CMR scanner

BACKGROUND Inadequate coronary adenosine response is a potential cause for false negative ischemia testing. Recently, the splenic switch-off (SSO) sign has been identified as a promising tool to ascertain the efficacy of adenosine during vasodilator stress cardiovascular magnetic resonance imaging (CMR). We assessed the value of SSO to predict adenosine response, defined as an increase in myocardial blood flow (MBF) during quantitative stress myocardial perfusion 13 N-ammonia positron emission tomography (PET). METHODS We prospectively enrolled 64 patients who underwent simultaneous CMR and PET myocardial perfusion imaging on a hybrid PET/CMR scanner with co-injection of gadolinium based contrast agent (GBCA) and 13N-ammonia during rest and adenosine-induced stress. A myocardial flow reserve (MFR) of  > 1.5 or ischemia as assessed by PET were defined as markers for adequate coronary adenosine response. The presence or absence of SSO was visually assessed. The stress-to-rest intensity ratio (SIR) was calculated as the ratio of stress over rest peak signal intensity for splenic tissue. Additionally, the spleen-to-myocardium ratio, defined as the relative change of spleen to myocardial signal, was calculated for stress (SMR$_{stress}$) and rest. RESULTS Sixty-one (95%) patients were coronary adenosine responders, but SSO was absent in 18 (28%) patients. SIR and SMR$_{stress}$ were significantly lower in patients with SSO (SIR: 0.56 ± 0.13 vs. 0.93 ± 0.23; p < 0.001 and SMR$_{stress}$: 1.09 ± 0.47 vs. 1.68 ± 0.62; p < 0.001). Mean hyperemic and rest MBF were 2.12 ± 0.68 ml/min/g and 0.78 ± 0.26 ml/min/g, respectively. MFR was significantly higher in patients with vs. patients without presence of SSO (3.07 ± 1.03 vs. 2.48 ± 0.96; p = 0.038), but there was only a weak inverse correlation between SMR$_{stress}$ and MFR (R = -0.378; p = 0.02) as well as between SIR and MFR (R = -0.356; p = 0.004). CONCLUSIONS The presence of SSO implies adequate coronary adenosine-induced MBF response. Its absence, however, is not a reliable indicator for failed adenosine-induced coronary vasodilatation

Impact of dose reduction and iterative reconstruction algorithm on the detectability of pulmonary nodules by artificial intelligence

PURPOSE The purpose of this study was to assess whether the performances of an automated software for lung nodule detection with computed tomography (CT) are affected by radiation dose and the use of iterative reconstruction algorithm. MATERIALS AND METHODS A chest phantom (Multipurpose Chest Phantom N1; Kyoto Kagaku Co. Ltd, Kyoto, Japan) with 15 pulmonary nodules was scanned with a total of five CT protocol settings with up to 20-fold dose reduction. All CT examinations were reconstructed with iterative reconstruction algorithms ADMIRE 3 and ADMIRE 5 and were then analyzed for the presence of pulmonary nodules with a fully automated computer aided detection software system (InferRead$^{TM}$ CT Lung, Infervision), which is based on deep neural networks. RESULTS The sensitivity of fully automated pulmonary nodule detection for ground-glass nodules at standard dose CT was greater (70.0%; 14/20; 95% CI: 51.6-88.4%) than at 10-fold and 20-fold dose reduction (30.0%; 6/20; 95% CI: 0.0%-62.5%). There were less false positive findings when ADMIRE 5 reconstruction was used (4.0 ± 2.8 [SD]; range: 2-6) instead of ADMIRE 3 reconstruction (25.0 ± 15.6 [SD]; range: 14-36). There was no difference in the sensitivity of detection of solid and subsolid nodules between standard dose (100%; 95% CI: 100-100%) and 10- and 20-fold reduced dose CT (92.5%; 95% CI: 83.8-100.0%). Image noise was significantly greater with ADMIRE 3 (81 ± 2 [SD] [range: 79-84]; 104 ± 3 [SD] [range: 101-107]; 114 ± 5 [SD] [range: 110-119]; 193 ± 10 [SD] [range: 183-203]; 220 ± 16 [SD] [range: 210-238]) compared to ADMIRE 5 (44 ± 2 [SD] [range: 42-46]; 60 ± 2 [SD] [range: 57-61]; 66 ± 1 [SD] [range: 65-67]; 103 ± 4 [SD] [range: 98-106]; 110 ± 1 [SD] [range: 109-111]), respectively in each of the five CT protocols. CONCLUSION This phantom study suggests that dose reduction and iterative reconstruction settings have an impact on detectability of pulmonary nodules by artificial intelligence software and we therefore encourage adaption of dose levels and reconstruction methods prior to widespread implementation of fully automatic nodule detection software for lung cancer screening purposes

Pneumonia Detection in Chest X-Ray Dose-Equivalent CT: Impact of Dose Reduction on Detectability by Artificial Intelligence

RATIONALE AND OBJECTIVES There has been a significant increase of immunocompromised patients in recent years due to new treatment modalities for previously fatal diseases. This comes at the cost of an elevated risk for infectious diseases, most notably pathogens affecting the respiratory tract. Because early diagnosis and treatment of pneumonia can help reducing morbidity and mortality, we assessed the performance of a deep neural network in the detection of pulmonary infection in chest X-ray dose-equivalent computed tomography (CT). MATERIALS AND METHODS The 100 patients included in this retrospective study were referred to our department for suspicion of pulmonary infection and/or follow-up of known pulmonary nodules. Every patient was scanned with a standard dose (1.43 ± 0.54 mSv) and a 20 times dose-reduced (0.07 ± 0.03 mSv) CT protocol. We trained a deep neural network to perform binary classification (pulmonary consolidation present or not) and assessed diagnostic performance on both standard dose and reduced dose CT images. RESULTS The areas under the curve of the deep learning algorithm for the standard dose CT was 0.923 (confidence interval [CI] 95%: 0.905-0.941) and significantly higher than the areas under the curve (0.881, CI 95%: 0.859-0.903) of the reduced dose CT (p = 0.001). Sensitivity and specificity of the standard dose CT was 82.9% and 93.8%, and of the reduced dose CT 71.0% and 93.3%. CONCLUSION Pneumonia detection with X-ray dose-equivalent CT using artificial intelligence is feasible and may contribute to a more robust and reproducible diagnostic performance. Dose reduction lowered the performance of the deep neural network, which calls for optimization and adaption of CT protocols when using AI algorithms at reduced doses

Evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[18^{18}F]FDG PET/CT between 2007 and 2021

OBJECTIVES To evaluate the evolution of CT radiation dose in pediatric patients undergoing hybrid 2-[$^{18}$F]fluoro-2-deoxy-D-glucose (2-[$^{18}$F]FDG) PET/CT between 2007 and 2021. METHODS AND MATERIALS Data from all pediatric patients aged 0-18 years who underwent hybrid 2-[$^{18}$F]FDG PET/CT of the body between January 2007 and May 2021 was reviewed. Demographic and imaging parameters were collected. A board-certified radiologist reviewed all CT scans and measured image noise in the brain, liver and adductor muscles. RESULTS 294 scans from 167 children (72 females (43%); median age: 14 (IQR 10-15) years; BMI: median 17.5 (IQR 15-20.4) kg/m$^{2}$) were included. CT dose index-volume (CTDIvol) and dose length product (DLP) both decreased significantly from 2007 to 2021 (both p < 0.001, Spearman's rho coefficients -0.46 and -0.35, respectively). Specifically, from 2007 to 2009 to 2019-2021 CTDIvol and DLP decreased from 2.94 (2.14-2.99) mGy and 309 (230-371) mGy*cm, respectively, to 0.855 (0.568-1.11) mGy and 108 (65.6-207) mGy*cm, respectively. From 2007 to 2021, image noise in the brain and liver remained constant (p = 0.26 and p = 0.06), while it decreased in the adductor muscles (p = 0.007). Peak tube voltage selection (in kilovolt, kV) of CT scans shifted from high kV imaging (140 or 120kVp) to low kV imaging (100 or 80kVp) (p < 0.001) from 2007 to 2021. CONCLUSION CT radiation dose in pediatric patients undergoing hybrid 2-[$^{18}$F]FDG PET/CT has decreased in recent years equaling approximately one-third of the initial amount. ADVANCES IN KNOWLEDGE Over the past 15 years CT radiation dose decreased considerably in pediatric patients undergoing hybrid imaging, while objective image quality may not have been compromised