scholarly journals Effects of Respiratory Motion on Y-90 PET Dosimetry for SIRT

Diagnostics ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 194
Author(s):  
Matthew D. Walker ◽  
Jonathan I. Gear ◽  
Allison J. Craig ◽  
Daniel R. McGowan
Keyword(s):  
Respiratory Motion ◽  
Motion Platform ◽  
Quiescent Period ◽  
Volume Of Interest ◽  
Pet Ct ◽  
Quantification Accuracy ◽  
The Impact ◽  
Pet Scanners ◽  
Phantom Experiments ◽  
Accurate Quantification

Respiratory motion degrades the quantification accuracy of PET imaging by blurring the radioactivity distribution. In the case of post-SIRT PET-CT verification imaging, respiratory motion can lead to inaccuracies in dosimetric measures. Using an anthropomorphic phantom filled with 90Y at a range of clinically relevant activities, together with a respiratory motion platform performing realistic motions (10–15 mm amplitude), we assessed the impact of respiratory motion on PET-derived post-SIRT dosimetry. Two PET scanners at two sites were included in the assessment. The phantom experiments showed that device-driven quiescent period respiratory motion correction improved the accuracy of the quantification with statistically significant increases in both the mean contrast recovery (+5%, p = 0.003) and the threshold activities corresponding to the dose to 80% of the volume of interest (+6%, p < 0.001). Although quiescent period gating also reduces the number of counts and hence increases the noise in the PET image, its use is encouraged where accurate quantification of the above metrics is desired.

scholarly journals Convolutional Neural Networks for automatic image quality control and EARL compliance of PET images

2021 ◽  
Author(s):  
Elisabeth Pfaehler ◽  
Daniela Euba ◽  
Andreas Rinscheid ◽  
Otto S. Hoekstra ◽  
Josee Zijlstra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Training Data ◽  
Independent Dataset ◽  
Pet Ct ◽  
Image Quality Control ◽  
The Cross ◽  
The Impact ◽  
Pet Scanners ◽  
Fold Cross Validation

Abstract Background Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and Methods 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using 5-fold cross validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations. Results In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by e.g. adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

scholarly journals Quantitative implications of the updated EARL 2019 PET–CT performance standards

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Andres Kaalep ◽  
Coreline N. Burggraaff ◽  
Simone Pieplenbosch ◽  
Eline E. Verwer ◽  
Terez Sera ◽  
...  
Keyword(s):  
Performance Standards ◽  
Blood Pool ◽  
Volume Of Interest ◽  
Quantitative Metrics ◽  
Pet Ct ◽  
Significant Difference ◽  
Cancer Types ◽  
Malignant Lesions ◽  
The Impact ◽  
Ct Studies

Abstract Purpose Recently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET–CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1). Methods Thirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET–CT studies were used to derive four image datasets—the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4). Results We found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23–30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed. Conclusions Application of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data.

scholarly journals Convolutional Neural Networks for Automatic Image Quality Control and EARL Compliance of PET Images

2021 ◽  
Author(s):  
Elisabeth Pfaehler ◽  
Daniela Euba ◽  
Andreas Rinscheid ◽  
Otto S. Hoekstra ◽  
Josee Zijlstra ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Training Data ◽  
Independent Dataset ◽  
Pet Ct ◽  
Image Quality Control ◽  
The Cross ◽  
The Impact ◽  
Pet Scanners ◽  
Fold Cross Validation

Abstract Background: Machine learning studies require a large number of images often obtained on different PET scanners. When merging these images, the use of harmonized images following EARL-standards is essential. However, when including retrospective images, EARL accreditation might not have been in place. The aim of this study was to develop a convolutional neural network (CNN) that can identify retrospectively if an image is EARL compliant and if it is meeting older or newer EARL-standards. Materials and Methods: 96 PET images acquired on three PET/CT systems were included in the study. All images were reconstructed with the locally clinically preferred, EARL1, and EARL2 compliant reconstruction protocols. After image pre-processing, one CNN was trained to separate clinical and EARL compliant reconstructions. A second CNN was optimized to identify EARL1 and EARL2 compliant images. The accuracy of both CNNs was assessed using 5-fold cross validation. The CNNs were validated on 24 images acquired on a PET scanner not included in the training data. To assess the impact of image noise on the CNN decision, the 24 images were reconstructed with different scan durations.Results: In the cross-validation, the first CNN classified all images correctly. When identifying EARL1 and EARL2 compliant images, the second CNN identified 100% EARL1 compliant and 85% EARL2 compliant images correctly. The accuracy in the independent dataset was comparable to the cross-validation accuracy. The scan duration had almost no impact on the results. Conclusion: The two CNNs trained in this study can be used to retrospectively include images in a multi-center setting by e.g. adding additional smoothing. This method is especially important for machine learning studies where the harmonization of images from different PET systems is essential.

scholarly journals The impact of respiratory motion on tumor quantification and delineation in static PET/CT imaging

2009 ◽  
Vol 54 (24) ◽  
pp. 7345-7362 ◽  
Author(s):  
Chi Liu ◽  
Larry A Pierce II ◽  
Adam M Alessio ◽  
Paul E Kinahan
Keyword(s):  
Respiratory Motion ◽  
Ct Imaging ◽  
Pet Ct ◽  
The Impact

Rapid evaluation of FDG imaging alternatives using head-to-head comparisons of full ring and gamma camera based PET scanners –

2002 ◽  
Vol 41 (05) ◽  
pp. 208-213 ◽  
Author(s):  
L. M. Haslinghuis-Bajan ◽  
L. Hooft ◽  
A. van Lingen ◽  
M. van Tulder ◽  
W. Devillé ◽  
...  
Keyword(s):  
Methodological Quality ◽  
Gamma Camera ◽  
First Generation ◽  
Prospective Evaluation ◽  
First Approximation ◽  
Variable Head ◽  
Malignant Lesions ◽  
The Impact ◽  
Pet Scanners

SummaryAim: While FDG full ring PET (FRPET) has been gradually accepted in oncology, the role of the cheaper gamma camera based alternatives (GCPET) is less clear. Since technology is evolving rapidly, “tracker trials” would be most helpful to provide a first approximation of the relative merits of these alternatives. As difference in scanner sensitivity is the key variable, head-to-head comparison with FRPET is an attractive study design. This systematic review summarises such studies. Methods: Nine studies were identified until July 1, 2000. Two observers assessed the methodological quality (Cochrane criteria), and extracted data. Results: The studies comprised a variety of tumours and indications. The reported GC- and FRPET agreement for detection of malignant lesions ranged from 55 to 100%, but with methodological limitations (blinding, standardisation, limited patient spectrum). Mean lesion diameter was 2.9 cm (SD 1.8), with only about 20% <1.5 cm. The 3 studies with the highest quality reported concordances of 74-79%, for the studied lesion spectrum. Contrast at GCPET was lower than that of FRPET, contrast and detection agreement were positively related. Logistic regression analysis suggested that pre-test indicators might be used to predict FRPET-GCPET concordance. Conclusion: In spite of methodological limitations, “first generation” GCPET devices detected sufficient FRPET positive lesions to allow prospective evaluation in clinical situations where the impact of FRPET is not confined to detection of small lesions (<1.5 cm). The efficiency of head-to-head comparative studies would benefit from application in a clinically relevant patient spectrum, with proper blinding and standardisation of acquisition procedures.

scholarly journals The impact of particulate electron paramagnetic resonance oxygen sensors on fluorodeoxyglucose imaging characteristics detected via positron emission tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Philip E. Schaner ◽  
Ly-Binh-An Tran ◽  
Bassem I. Zaki ◽  
Harold M. Swartz ◽  
Eugene Demidenko ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Oxygen Sensor ◽  
Oxygen Sensors ◽  
Paramagnetic Resonance ◽  
Imaging Characteristics ◽  
Positron Emission ◽  
Pet Ct ◽  
Fdg Pet Ct ◽  
The Impact ◽  
Electron Paramagnetic

AbstractDuring a first-in-humans clinical trial investigating electron paramagnetic resonance tumor oximetry, a patient injected with the particulate oxygen sensor Printex ink was found to have unexpected fluorodeoxyglucose (FDG) uptake in a dermal nodule via positron emission tomography (PET). This nodule co-localized with the Printex ink injection; biopsy of the area, due to concern for malignancy, revealed findings consistent with ink and an associated inflammatory reaction. Investigations were subsequently performed to assess the impact of oxygen sensors on FDG-PET/CT imaging. A retrospective analysis of three clinical tumor oximetry trials involving two oxygen sensors (charcoal particulates and LiNc-BuO microcrystals) in 22 patients was performed to evaluate FDG imaging characteristics. The impact of clinically used oxygen sensors (carbon black, charcoal particulates, LiNc-BuO microcrystals) on FDG-PET/CT imaging after implantation in rat muscle (n = 12) was investigated. The retrospective review revealed no other patients with FDG avidity associated with particulate sensors. The preclinical investigation found no injected oxygen sensor whose mean standard uptake values differed significantly from sham injections. The risk of a false-positive FDG-PET/CT scan due to oxygen sensors appears low. However, in the right clinical context the potential exists that an associated inflammatory reaction may confound interpretation.

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

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhifang Wu ◽  
Binwei Guo ◽  
Bin Huang ◽  
Xinzhong Hao ◽  
Ping Wu ◽  
...  
Keyword(s):  
Time Of Flight ◽  
Pulmonary Nodules ◽  
Medium Size ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Silicon Photomultiplier ◽  
Standardized Uptake Values ◽  
Pet Ct ◽  
Quantification Accuracy ◽  
Small Pulmonary Nodules

AbstractTo 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.

Quantifying The Impact That Coxswain Behaviour Has on Whole Body Vibration - Simulator Trial

2021 ◽  
Vol 163 (A3) ◽  
Author(s):  
T J Newman
Keyword(s):  
Phase 1 ◽  
Whole Body Vibration ◽  
Whole Body ◽  
Motion Platform ◽  
Body Vibration ◽  
Sea Trial ◽  
Data Driven Approach ◽  
Exposure Levels ◽  
Transit Speed ◽  
The Impact

A common risk to personnel is from Whole Body Vibration (WBV) and shock when transiting at speed in heavy seas, and much research has been done by maritime organisations to reduce this risk and the associated health impacts. It is well known that coxswain ‘driving style’ can radically affect exposure levels for a given sea state and sustained transit speed. A data-driven approach to define what makes a good coxswain from a WBV perspective is currently being developed by the Naval Design Partnering team (NDP). In phase 1, a systematic coxswain behaviour tracking methodology has been developed and demonstrated using a motion platform-based fast craft simulator at MARIN. The performance of several experienced volunteer coxswains from MOD, RNLI and KNRM has been evaluated based on a set pattern of tests. The advantages of using the simulator, over a sea trial, have been demonstrated: it is more repeatable, more controllable, accurate and more accessible. The potential disadvantages of the approach are also discussed with reference to feedback gathered from coxswains. Analysis has shown effective throttle control is much more important than steering to reduce WBV. Several interesting trends in WBV reduction potential have been shown which it is thought, with further validation, could aid mission planning, mission execution and provide data for training autonomous feedback/control algorithms. Further work is required before the findings of this study can be fully exploited. These subsequent phases, which include sea trials, aim to provide validation and further evidence to support the initial findings.

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