scholarly journals CERMEP-IDB-MRXFDG: a database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Inés Mérida ◽  
Julien Jung ◽  
Sandrine Bouvard ◽  
Didier Le Bars ◽  
Sophie Lancelot ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Short Form ◽  
Human Subjects ◽  
Statistical Parametric Mapping ◽  
Volumetric Analysis ◽  
Ct Images ◽  
Normal Adult ◽  
Adult Human ◽  
Leave One Out ◽  
Fdg Pet Ct

AbstractWe present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent static [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants’ individual demographics (mean age 38 ± 11.5 years, range 23–65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: (1) DICOM (data not processed), (2) NIFTI (multimodal images coregistered to PET subject space), (3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.

scholarly journals CERMEP-IDB-MRXFDG: A database of 37 normal adult human brain [18F]FDG PET, T1 and FLAIR MRI, and CT images available for research

2020 ◽  
Author(s):  
Ines Merida ◽  
Julien Jung ◽  
Sandrine Bouvard ◽  
Didier Le Bars ◽  
Sophie Lancelot ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Short Form ◽  
Human Subjects ◽  
Statistical Parametric Mapping ◽  
Volumetric Analysis ◽  
Ct Images ◽  
Normal Adult ◽  
Adult Human ◽  
18F Fdg ◽  
Leave One Out

We present a database of cerebral PET FDG and anatomical MRI for 37 normal adult human subjects (CERMEP-IDB-MRXFDG). Thirty-nine participants underwent [18F]FDG PET/CT and MRI, resulting in [18F]FDG PET, T1 MPRAGE MRI, FLAIR MRI, and CT images. Two participants were excluded after visual quality control. We describe the acquisition parameters, the image processing pipeline and provide participants' individual demographics (mean age 38 ± 11.5 years, range 23-65, 20 women). Volumetric analysis of the 37 T1 MRIs showed results in line with the literature. A leave-one-out assessment of the 37 FDG images using Statistical Parametric Mapping (SPM) yielded a low number of false positives after exclusion of artefacts. The database is stored in three different formats, following the BIDS common specification: 1) DICOM (data not processed), 2) NIFTI (multimodal images coregistered to PET subject space), 3) NIFTI normalized (images normalized to MNI space). Bona fide researchers can request access to the database via a short form.

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 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.

scholarly journals Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Margarita Kirienko ◽  
Martina Sollini ◽  
Giorgia Silvestri ◽  
Serena Mognetti ◽  
Emanuele Voulaz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Fdg Pet ◽  
Area Under The Curve ◽  
Ct Images ◽  
Tnm System ◽  
Positron Emission ◽  
Pet Ct ◽  
Test Sets ◽  
Fdg Pet Ct

Aim. To develop an algorithm, based on convolutional neural network (CNN), for the classification of lung cancer lesions as T1-T2 or T3-T4 on staging fluorodeoxyglucose positron emission tomography (FDG-PET)/CT images. Methods. We retrospectively selected a cohort of 472 patients (divided in the training, validation, and test sets) submitted to staging FDG-PET/CT within 60 days before biopsy or surgery. TNM system seventh edition was used as reference. Postprocessing was performed to generate an adequate dataset. The input of CNNs was a bounding box on both PET and CT images, cropped around the lesion centre. The results were classified as Correct (concordance between reference and prediction) and Incorrect (discordance between reference and prediction). Accuracy (Correct/[Correct + Incorrect]), recall (Correctly predicted T3-T4/[all T3-T4]), and specificity (Correctly predicted T1-T2/[all T1-T2]), as commonly defined in deep learning models, were used to evaluate CNN performance. The area under the curve (AUC) was calculated for the final model. Results. The algorithm, composed of two networks (a “feature extractor” and a “classifier”), developed and tested achieved an accuracy, recall, specificity, and AUC of 87%, 69%, 69%, and 0.83; 86%, 77%, 70%, and 0.73; and 90%, 47%, 67%, and 0.68 in the training, validation, and test sets, respectively. Conclusion. We obtained proof of concept that CNNs can be used as a tool to assist in the staging of patients affected by lung cancer.

scholarly journals Prognostic Value of Sarcopenia in Patients Treated by Radiochemotherapy for Locally Advanced Oesophageal Cancer

2020 ◽  
Author(s):  
Romain Mallet ◽  
Romain Modzelewski ◽  
Justine Lequesne ◽  
Pierre Decazes ◽  
Hugues Auvray ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Cancer Patients ◽  
Oesophageal Cancer ◽  
Fdg Pet ◽  
Prognostic Value ◽  
Locally Advanced ◽  
Ct Images ◽  
Pet Ct ◽  
Nutritional Risk Index ◽  
Fdg Pet Ct

Abstract Background Sarcopenia is defined by a loss of skeletal muscle mass with or without loss of fat mass. Sarcopenia has been associated to reduced tolerance to treatment and worse prognosis in cancer patients, including patients undergoing surgery for limited oesophageal cancer. Concomitant chemo-radiotherapy is the standard treatment for locally-advanced tumour, not accessible to surgical resection. Using automated delineation of the skeletal muscle, we have investigated the prognostic value of sarcopenia in locally advanced oesophageal cancer patients treated by curative-intent chemo-radiotherapy. Methods The clinical, nutritional, anthropometric, and functional-imaging ( 18 FDG-PET/CT) data were collected in 97 patients treated between 2006 and 2012 in our institution (RTEP3). The skeletal muscle area was automatically delineated on cross-sectional CT images acquired at the 3 rd . lumbar vertebra level and divided by the patient’s squared height (SML3/h 2 ) to obtain the Skeletal Muscle Index (SMI). The primary endpoint was overall survival probability. Results Seventy-six deaths were reported. The median survival time was 27 [95% Confidence Interval 23 – 40] months for the whole population. Univariate analyses (Cox Proportional Hazard Model) showed decreased survival probabilities in patients with reduced SMI, WHO >0, Body Mass Index ≤21, and Nutritional Risk Index ≤97.5. Multivariate analyses showed that reduced SMI (Hazard Ratio 0.948 [0.919 - 0.978] and male sex (2.977 [1.427 - 6.213] were significantly associated to decreased survival. Using Receiver Operating Characteristics curves, the Area Under the Curve (AUC) was 0.73 in males (p=0.0002], the optimal threshold being 51.5 cm 2 /m 2 . In women, the AUC was 0.65 (p=0.19). Conclusion Sarcopenia is a powerful independent prognostic factor, associated with a rise of the overall mortality in patients treated exclusively by radiochemotherapy for a locally advanced oesophageal cancer. L3 CT images are easily gathered from 18 FDG-PET/CT acquisitions

scholarly journals Radiomics in Vulvar Cancer: First Clinical Experience Using 18F-FDG PET/CT Images

2018 ◽  
Vol 60 (2) ◽  
pp. 199-206 ◽  
Author(s):  
Angela Collarino ◽  
Giorgia Garganese ◽  
Simona M. Fragomeni ◽  
Lenka M. Pereira Arias-Bouda ◽  
Francesco P. Ieria ◽  
...  
Keyword(s):  
Clinical Experience ◽  
Fdg Pet ◽  
Vulvar Cancer ◽  
Ct Images ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

SUVmax of FDG PET-CT Can Be Used As An Identifier of Lesion for AI To Interpret Radiology Reports

2020 ◽  
Author(s):  
Kenji Hirata ◽  
Osamu Manabe ◽  
Keiichi Magota ◽  
Sho Furuya ◽  
Tohru Shiga ◽  
...  
Keyword(s):  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Local Maximum ◽  
Ct Images ◽  
Whole Body ◽  
Training Dataset ◽  
Imaging Biomarkers ◽  
Radiology Reports ◽  
Pet Ct ◽  
Fdg Pet Ct

Abstract Background Radiology reports contribute not only to the particular patient, but also to constructing massive training dataset in the era of artificial intelligence (AI). The maximum standardized uptake value (SUVmax) is often described in daily radiology reports of FDG PET-CT. If SUVmax can be used as an identifier of lesion, that would greatly help AI interpret radiology reports. We aimed to clarify whether the lesion can be localized using SUVmax written in radiology reports.Methods The institutional review board approved this retrospective study. We investigated a total of 112 lesions from 30 FDG PET-CT images acquired with 3 different scanners. SUVmax was calculated from DICOM files based on the latest Quantitative Imaging Biomarkers Alliance (QIBA) publication. The voxels showing the given SUVmax were exhaustively searched in the whole-body images and counted. SUVmax was provided with 5 different degrees of precision: integer (e.g., 3), 1st decimal places (DP) (3.1), 2nd DP (3.14), 3rd DP (3.142), and 4th DP (3.1416). For instance, when SUVmax=3.14 was given, the voxels with 3.135≤SUVmax<3.145 were extracted. We also evaluated whether local maximum restriction could improve the identifying performance, where only the voxels showing the highest intensity within some neighborhood were considered. We defined that “identical detection” was achieved when only single voxel satisfied the criterion.Results A total of 112 lesions from 30 FDG PET-CT images were investigated. SUVmax ranged from 1.3 to 49.1 (median = 5.6, IQR = 5.2). Generally, when larger and more precise SUVmax values were given, fewer voxels satisfied the criterion. The local maximum restriction was very effective. When SUVmax was determined to 4 decimal places (e.g., 3.1416) and the local maximum restriction was applied, identical detection was achieved in 33.3% (lesions with SUVmax<2), 79.5% (2≤SUVmax<5), and 97.8% (5≤SUVmax) of lesions.Conclusions SUVmax of FDG PET-CT can be used as an identifier to localize the lesion if precise SUVmax is provided and local maximum restriction was applied, although the lesions showing SUVmax<2 were difficult to identify. The proposed method may have potential to make use of radiology reports retrospectively for constructing training datasets for AI.

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