MO-G-17A-08: Applications of Quantitative PET/CT Imaging of Yttrium-90: A Tool for Improving Radioembolization

2014 ◽  
Vol 41 (6Part25) ◽  
pp. 438-439
Author(s):  
A Pasciak ◽  
Y Bradley
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
G. Paone ◽  
F. Martucci ◽  
V. Espeli ◽  
L. Ceriani ◽  
G. Treglia ◽  
...  

This study assessed the role of 18F-FDG PET-CT (PET/CT) to detect the cartilage and paraglottic infiltration in advanced glottic cancer comparing the results with those of conventional imaging (CI) (contrast-enhanced computed tomography and/or magnetic resonance). In addition, we assessed the prognostic value of quantitative parameters, measured on baseline PET/CT, in terms of event-free survival (EFS) and overall survival (OS). We retrospectively analyzed 27 patients with glottic squamous cell carcinoma stage III and IVA, treated in our institute between 2010 and 2016, comparing PET/CT, performed for staging and radiotherapy planning, and CI findings. Cohen’s K was used to compare concordance between PET/CT and CI. Imaging findings were correlated with endoscopic evaluation and histological reports (gold standard (GS)). All lesions shown by CI were also detected by PET/CT imaging, and in 5 cases, a better definition of local infiltration was achieved with PET/CT than CI (5 CT). Sensitivity, specificity, and accuracy of PET/CT and CT were 95%, 86%, and 93% and 70%, 86%, and 74% for, respectively. MRI showed sensitivity and specificity of 100%. One false-negative (FN) cases and 1 false-positive (FP) case were observed with PET/CT with no difference compared to MRI (10 cases). Six FN cases and 1 FP case were observed with CT. Cohen’s K was 0.60 (PET vs. CI) and 0.80 (PET vs. GS). Patients were followed-up for at least 24 months to calculate EFS and OS. 13 local recurrence and 7 deaths were recorded. Among quantitative PET parameters, baseline MTV was the most powerful predictor of outcome. Our data suggest a reliable sensitivity and accuracy of PET/CT in the evaluation of local extension, proving a useful method for initial local staging in addition to the well-established role in lymph-node and distant sites assessment. Furthermore, pretreatment MTV provides better prognostic information than other PET/CT parameters.


2013 ◽  
Vol 40 (6Part1) ◽  
pp. 062503 ◽  
Author(s):  
George A. Prenosil ◽  
Thilo Weitzel ◽  
Michael Hentschel ◽  
Bernd Klaeser ◽  
Thomas Krause

2017 ◽  
Vol 58 (7) ◽  
pp. 1065-1071 ◽  
Author(s):  
Joshua S. Scheuermann ◽  
Janet S. Reddin ◽  
Adam Opanowski ◽  
Paul E. Kinahan ◽  
Barry A. Siegel ◽  
...  

Author(s):  
Hossein Arabi ◽  
Habib Zaidi

Abstract Objectives The susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning–based metal artefact reduction (MAR) in quantitative PET/CT imaging. Methods Deep learning–based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques. Results The evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR. Conclusion The DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images. Key Points • The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images. • The aim of this work is to develop and evaluate a deep learning–based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging. • Deep learning–based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.


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