AIMC Topic: Whole Body Imaging
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Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.
Physics in medicine and biology
Mar 2, 2020
Deriving accurate structural maps for attenuation correction (AC) of whole-body positron emission tomography (PET) remains challenging. Common problems include truncation, inter-scan motion, and erroneous transformation of structural voxel-intensitie...
Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer.
European radiology
Feb 13, 2020
INTRODUCTION: The aim of the study was to extract anthropometric measures from CT by deep learning and to evaluate their prognostic value in patients with non-small-cell lung cancer (NSCLC).
Humans
Prognosis
Female
Intra-Abdominal Fat
Subcutaneous Fat
Whole Body Imaging
Progression-Free Survival
Proportional Hazards Models
Body Surface Area
Deep Learning
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Survival Rate
Muscle, Skeletal
Male
Positron Emission Tomography Computed Tomography
Middle Aged
Neoplasm Staging
Disease Progression
Body Composition
Reproducibility of Results
Aged
Adult
'Squeeze & excite' guided few-shot segmentation of volumetric images.
Medical image analysis
Oct 13, 2019
Deep neural networks enable highly accurate image segmentation, but require large amounts of manually annotated data for supervised training. Few-shot learning aims to address this shortcoming by learning a new class from a few annotated support exam...
Fully automated analysis for bone scintigraphy with artificial neural network: usefulness of bone scan index (BSI) in breast cancer.
Annals of nuclear medicine
Jul 17, 2019
OBJECTIVE: Artificial neural network (ANN) technology has been developed for clinical use to analyze bone scintigraphy with metastatic bone tumors. It has been reported to improve diagnostic accuracy and reproducibility especially in cases of prostat...
Separation of water and fat signal in whole-body gradient echo scans using convolutional neural networks.
Magnetic resonance in medicine
Apr 29, 2019
PURPOSE: To perform and evaluate water-fat signal separation of whole-body gradient echo scans using convolutional neural networks.
Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data.
Clinical radiology
Feb 23, 2019
Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project,...
Simultaneous cosegmentation of tumors in PET-CT images using deep fully convolutional networks.
Medical physics
Jan 4, 2019
PURPOSE: To investigate the use and efficiency of 3-D deep learning, fully convolutional networks (DFCN) for simultaneous tumor cosegmentation on dual-modality nonsmall cell lung cancer (NSCLC) and positron emission tomography (PET)-computed tomograp...
A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation.
IEEE transactions on bio-medical engineering
Aug 30, 2018
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate se...
Automated Whole-Body Bone Lesion Detection for Multiple Myeloma on Ga-Pentixafor PET/CT Imaging Using Deep Learning Methods.
Contrast media & molecular imaging
Jan 8, 2018
The identification of bone lesions is crucial in the diagnostic assessment of multiple myeloma (MM). Ga-Pentixafor PET/CT can capture the abnormal molecular expression of CXCR-4 in addition to anatomical changes. However, whole-body detection of doze...
Deep reconstruction model for dynamic PET images.
PloS one
Sep 21, 2017
Accurate and robust tomographic reconstruction from dynamic positron emission tomography (PET) acquired data is a difficult problem. Conventional methods, such as the maximum likelihood expectation maximization (MLEM) algorithm for reconstructing the...
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