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Positron Emission Tomography Computed Tomography

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Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.

European journal of nuclear medicine and molecular imaging
PURPOSE: Respiratory motion (RM) significantly impacts image quality in thoracoabdominal PET/CT imaging. This study introduces a unified data-driven respiratory motion correction (uRMC) method, utilizing deep learning neural networks, to solve all th...

Efficient model-informed co-segmentation of tumors on PET/CT driven by clustering and classification information.

Computers in biology and medicine
Automatic tumor segmentation via positron emission tomography (PET) and computed tomography (CT) images plays a critical role in the prevention, diagnosis, and treatment of this disease via radiation oncology. However, segmenting these tumors is chal...

Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient ...

Development and validation of a machine learning-based F-fluorodeoxyglucose PET/CT radiomics signature for predicting gastric cancer survival.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Survival prognosis of patients with gastric cancer (GC) often influences physicians' choice of their follow-up treatment. This study aimed to develop a positron emission tomography (PET)-based radiomics model combined with clinical tumor-...

F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

Annals of surgical oncology
PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nu...

Single-Subject Deep-Learning Image Reconstruction With a Neural Optimization Transfer Algorithm for PET-Enabled Dual-Energy CT Imaging.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Combining dual-energy computed tomography (DECT) with positron emission tomography (PET) offers many potential clinical applications but typically requires expensive hardware upgrades or increases radiation doses on PET/CT scanners due to an extra X-...

Nuclear medicine technologists practice impacted by AI denoising applications in PET/CT images.

Radiography (London, England : 1995)
PURPOSE: Artificial intelligence (AI) in positron emission tomography/computed tomography (PET/CT) can be used to improve image quality when it is useful to reduce the injected activity or the acquisition time. Particular attention must be paid to en...

Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis - a proof of concept study.

European journal of nuclear medicine and molecular imaging
INTRODUCTION: Prosthetic valve endocarditis (PVE) is a serious complication of prosthetic valve implantation, with an estimated yearly incidence of at least 0.4-1.0%. The Duke criteria and subsequent modifications have been developed as a diagnostic ...

Multi-modal segmentation with missing image data for automatic delineation of gross tumor volumes in head and neck cancers.

Medical physics
BACKGROUND: Head and neck (HN) gross tumor volume (GTV) auto-segmentation is challenging due to the morphological complexity and low image contrast of targets. Multi-modality images, including computed tomography (CT) and positron emission tomography...

Explainable deep-learning-based ischemia detection using hybrid O-15 HO perfusion positron emission tomography and computed tomography imaging with clinical data.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
BACKGROUND: We developed an explainable deep-learning (DL)-based classifier to identify flow-limiting coronary artery disease (CAD) by O-15 HO perfusion positron emission tomography computed tomography (PET/CT) and coronary CT angiography (CTA) imagi...