AIMC Topic: Positron-Emission Tomography

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The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging.

Magnetic resonance imaging clinics of North America
Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could...

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

PloS one
Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT...

Improvement of phoswich detector-based β+/γ-ray discrimination algorithm with deep learning.

Medical physics
BACKGROUND: Positron probes can accurately localize malignant tumors by directly detecting positrons emitted from positron-emitting radiopharmaceuticals that accumulate in malignant tumors. In the conventional method for direct positron detection, mu...

Feasibility of a deep learning algorithm to achieve the low-dose Ga-FAPI/the fast-scan PET images: a multicenter study.

The British journal of radiology
OBJECTIVES: Our work aims to study the feasibility of a deep learning algorithm to reduce the Ga-FAPI radiotracer injected activity and/or shorten the scanning time and to investigate its effects on image quality and lesion detection ability.

The use of weather nowcasting convolutional neural network extrapolators in cardiac PET imaging.

Journal of medical radiation sciences
INTRODUCTION: Algorithms to predict short-term changes in local weather modalities have been used in meteorology for many years. These algorithms predict the temporospatial change in the movement of weather patterns such as cloud cover or precipitati...

Deep learning model for automatic image quality assessment in PET.

BMC medical imaging
BACKGROUND: A variety of external factors might seriously degrade PET image quality and lead to inconsistent results. The aim of this study is to explore a potential PET image quality assessment (QA) method with deep learning (DL).

A Two-Branch Neural Network for Short-Axis PET Image Quality Enhancement.

IEEE journal of biomedical and health informatics
The axial field of view (FOV) is a key factor that affects the quality of PET images. Due to hardware FOV restrictions, conventional short-axis PET scanners with FOVs of 20 to 35 cm can acquire only low-quality PET (LQ-PET) images in fast scanning ti...

Imaging of lung cancer.

Current problems in cancer
Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imag...

Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost.

Inter-crystal scattering event identification using a novel silicon photomultiplier signal multiplexing method.

Physics in medicine and biology
Identifying the inter-crystal scatter (ICS) events and recovering the first interaction position enables the accurate determination of the line-of-response in positron emission tomography (PET). However, conventional silicon photomultiplier (SiPM) si...