The initial VTM model based on pre-chemo CT and PET-CT images is showed in its digital design, preliminary to PLA anatomic replica creation and subsequent prosthesis graphic design

ASVIDE ◽  
2020 ◽  
Vol 7 ◽  
pp. 244-244
Author(s):  
Jacopo Vannucci ◽  
Elisa Scarnecchia ◽  
Rossella Potenza ◽  
Silvia Ceccarelli ◽  
Donato Monopoli Monopoli ◽  
...  
Keyword(s):  
Graphic Design ◽  
Ct Images ◽  
Digital Design ◽  
Model Based ◽  
Pet Ct

Automated Lung Nodule Segmentation Using an Active Contour Model Based on PET/CT Images

2015 ◽  
Vol 12 (8) ◽  
pp. 1972-1976 ◽  
Author(s):  
Yan Qiang ◽  
Xiaohui Zhang ◽  
Guohua Ji ◽  
Juanjuan Zhao
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Lung Nodule ◽  
Ct Images ◽  
Model Based ◽  
Contour Model ◽  
Pet Ct

scholarly journals Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on 18F-FDG PET/CT radiomics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Haiqun Xing ◽  
Zhixin Hao ◽  
Wenjia Zhu ◽  
Dehui Sun ◽  
Jie Ding ◽  
...  
Keyword(s):  
Prediction Model ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Fdg Pet ◽  
Ct Images ◽  
Ductal Adenocarcinoma ◽  
Pathological Grade ◽  
Operating Characteristics ◽  
Model Based ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Purpose To develop and validate a machine learning model based on radiomic features derived from 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) images to preoperatively predict the pathological grade in patients with pancreatic ductal adenocarcinoma (PDAC). Methods A total of 149 patients (83 men, 66 women, mean age 61 years old) with pathologically proven PDAC and a preoperative 18F-FDG PET/CT scan between May 2009 and January 2016 were included in this retrospective study. The cohort of patients was divided into two separate groups for the training (99 patients) and validation (50 patients) in chronological order. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, and the XGBoost algorithm was used to build a prediction model. Conventional PET parameters, including standardized uptake value, metabolic tumor volume, and total lesion glycolysis, were also measured. The quality of the proposed model was appraised by means of receiver operating characteristics (ROC) and areas under the ROC curve (AUC). Results The prediction model based on a twelve-feature-combined radiomics signature could stratify PDAC patients into grade 1 and grade 2/3 groups with AUC of 0.994 in the training set and 0.921 in the validation set. Conclusion The model developed is capable of predicting pathological differentiation grade of PDAC based on preoperative 18F-FDG PET/CT radiomics features.

scholarly journals Textural Features of Pretreatment 18F-FDG PET/CT Images: Prognostic Significance in Patients with Advanced T-Stage Oropharyngeal Squamous Cell Carcinoma

2013 ◽  
Vol 54 (10) ◽  
pp. 1703-1709 ◽  
Author(s):  
N.-M. Cheng ◽  
Y.-H. Dean Fang ◽  
J. Tung-Chieh Chang ◽  
C.-G. Huang ◽  
D.-L. Tsan ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Fdg Pet ◽  
Prognostic Significance ◽  
Ct Images ◽  
Textural Features ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

[68Ga]Ga-FAPI and [18F]FDG PET/CT images in a patient with juvenile polymyositis

2021 ◽  
Author(s):  
Jieling Zheng ◽  
Huaning Chen ◽  
Kaixian Lin ◽  
Shaobo Yao ◽  
Weibing Miao
Keyword(s):  
Fdg Pet ◽  
Ct Images ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

scholarly journals Increased [18F]FMISO accumulation under hypoxia by multidrug-resistant protein 1 inhibitors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yoichi Shimizu ◽  
Yukihiro Nakai ◽  
Hiroyuki Watanabe ◽  
Shimpei Iikuni ◽  
Masahiro Ono ◽  
...  
Keyword(s):  
Cyclosporine A ◽  
Pet Imaging ◽  
Multidrug Resistant ◽  
Ct Images ◽  
Pet Scan ◽  
Reduced Form ◽  
Pet Ct ◽  
Fadu Cells ◽  
Hypoxic State

Abstract Background [18F]Fluoromisonidazole ([18F]FMISO) is a PET imaging probe widely used for the detection of hypoxia. We previously reported that [18F]FMISO is metabolized to the glutathione conjugate of the reduced form in hypoxic cells. In addition, we found that the [18F]FMISO uptake level varied depending on the cellular glutathione conjugation and excretion ability such as enzyme activity of glutathione-S-transferase and expression levels of multidrug resistance-associated protein 1 (MRP1, an efflux transporter), in addition to the cellular hypoxic state. In this study, we evaluated whether MRP1 activity affected [18F]FMISO PET imaging. Methods FaDu human pharyngeal squamous cell carcinoma cells were pretreated with MRP1 inhibitors (cyclosporine A, lapatinib, or MK-571) for 1 h, incubated with [18F]FMISO for 4 h under hypoxia, and their radioactivity was then measured. FaDu tumor-bearing mice were intravenously injected with [18F]FMISO, and PET/CT images were acquired at 4 h post-injection (1st PET scan). Two days later, the same mice were pretreated with MRP1 inhibitors (cyclosporine A, lapatinib, or MK-571) for 1 h, and PET/CT images were acquired (2nd PET scan). Results FaDu cells pretreated with MRP1 inhibitors exhibited significantly higher radioactivity than those without inhibitor treatment (cyclosporine A: 6.91 ± 0.27, lapatinib: 10.03 ± 0.47, MK-571: 10.15 ± 0.44%dose/mg protein, p < 0.01). In the in vivo PET study, the SUVmean ratio in tumors [calculated as after treatment (2nd PET scan)/before treatment of MRP1 inhibitors (1st PET scan)] of the mice treated with MRP1 inhibitors was significantly higher than those of control mice (cyclosporine A: 2.6 ± 0.7, lapatinib: 2.2 ± 0.7, MK-571: 2.2 ± 0.7, control: 1.2 ± 0.2, p < 0.05). Conclusion In this study, we revealed that MRP1 inhibitors increase [18F]FMISO accumulation in hypoxic cells. This suggests that [18F]FMISO-PET imaging is affected by MRP1 inhibitors independent of the hypoxic state.

scholarly journals Impact of the Q.Clear reconstruction algorithm on the interpretation of PET/CT images in patients with lymphoma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michał Wyrzykowski ◽  
Natalia Siminiak ◽  
Maciej Kaźmierczak ◽  
Marek Ruchała ◽  
Rafał Czepczyński
Keyword(s):  
Reconstruction Algorithm ◽  
Ct Images ◽  
Pet Ct

scholarly journals Automated quantification of baseline imaging PET metrics on FDG PET/CT images of pediatric Hodgkin lymphoma patients

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Amy J. Weisman ◽  
Jihyun Kim ◽  
Inki Lee ◽  
Kathleen M. McCarten ◽  
Sandy Kessel ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Correlation Coefficient ◽  
Pet Imaging ◽  
Fdg Pet ◽  
Relative Difference ◽  
Ct Images ◽  
Imaging Features ◽  
Pediatric Hodgkin Lymphoma ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Purpose For pediatric lymphoma, quantitative FDG PET/CT imaging features such as metabolic tumor volume (MTV) are important for prognosis and risk stratification strategies. However, feature extraction is difficult and time-consuming in cases of high disease burden. The purpose of this study was to fully automate the measurement of PET imaging features in PET/CT images of pediatric lymphoma. Methods 18F-FDG PET/CT baseline images of 100 pediatric Hodgkin lymphoma patients were retrospectively analyzed. Two nuclear medicine physicians identified and segmented FDG avid disease using PET thresholding methods. Both PET and CT images were used as inputs to a three-dimensional patch-based, multi-resolution pathway convolutional neural network architecture, DeepMedic. The model was trained to replicate physician segmentations using an ensemble of three networks trained with 5-fold cross-validation. The maximum SUV (SUVmax), MTV, total lesion glycolysis (TLG), surface-area-to-volume ratio (SA/MTV), and a measure of disease spread (Dmaxpatient) were extracted from the model output. Pearson’s correlation coefficient and relative percent differences were calculated between automated and physician-extracted features. Results Median Dice similarity coefficient of patient contours between automated and physician contours was 0.86 (IQR 0.78–0.91). Automated SUVmax values matched exactly the physician determined values in 81/100 cases, with Pearson’s correlation coefficient (R) of 0.95. Automated MTV was strongly correlated with physician MTV (R = 0.88), though it was slightly underestimated with a median (IQR) relative difference of − 4.3% (− 10.0–5.7%). Agreement of TLG was excellent (R = 0.94), with median (IQR) relative difference of − 0.4% (− 5.2–7.0%). Median relative percent differences were 6.8% (R = 0.91; IQR 1.6–4.3%) for SA/MTV, and 4.5% (R = 0.51; IQR − 7.5–40.9%) for Dmaxpatient, which was the most difficult feature to quantify automatically. Conclusions An automated method using an ensemble of multi-resolution pathway 3D CNNs was able to quantify PET imaging features of lymphoma on baseline FDG PET/CT images with excellent agreement to reference physician PET segmentation. Automated methods with faster throughput for PET quantitation, such as MTV and TLG, show promise in more accessible clinical and research applications.

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