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Positron Emission Tomography Computed Tomography

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Deep learning-based multimodal image analysis for cervical cancer detection.

Methods (San Diego, Calif.)
Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PE...

Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network.

European radiology
OBJECTIVES: To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children.

Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

European journal of nuclear medicine and molecular imaging
PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).

Deep learning signatures reveal multiscale intratumor heterogeneity associated with biological functions and survival in recurrent nasopharyngeal carcinoma.

European journal of nuclear medicine and molecular imaging
PURPOSE: How to discriminate different risks of recurrent nasopharyngeal carcinoma (rNPC) patients and guide individual treatment has become of great importance. This study aimed to explore the associations between deep learning signatures and biolog...

Tc-PSMA targeted robot-assisted radioguided surgery during radical prostatectomy and extended lymph node dissection of prostate cancer patients.

Annals of nuclear medicine
OBJECTIVE: The feasibility of tracer production of technetium (Tc)-prostate-specific membrane antigen (PSMA)-I&S sterile cold kit, imaging with single photon emission tomography/computed tomography (SPECT/CT), and Tc-PSMA-radioguided robot-assisted l...

Heart and bladder detection and segmentation on FDG PET/CT by deep learning.

BMC medical imaging
PURPOSE: Positron emission tomography (PET)/ computed tomography (CT) has been extensively used to quantify metabolically active tumors in various oncology indications. However, FDG-PET/CT often encounters false positives in tumor detection due to fl...

The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

The British journal of radiology
OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumor...

Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning.

European journal of nuclear medicine and molecular imaging
PURPOSE: Total-body dynamic positron emission tomography/computed tomography (PET/CT) provides much sensitivity for clinical imaging and research, bringing new opportunities and challenges regarding the generation of total-body parametric images. Thi...

Deep learning-based attenuation correction for whole-body PET - a multi-tracer study with F-FDG,  Ga-DOTATATE, and F-Fluciclovine.

European journal of nuclear medicine and molecular imaging
UNLABELLED: A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using F-FDG,  Ga-DOTATATE, and F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps e...