AIMC Topic: Whole Body Imaging
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Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study.
Investigative radiology
Oct 3, 2022
OBJECTIVES: Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body...
Combining Deep Learning and Radiomics for Automated, Objective, Comprehensive Bone Marrow Characterization From Whole-Body MRI: A Multicentric Feasibility Study.
Investigative radiology
May 27, 2022
OBJECTIVES: Disseminated bone marrow (BM) involvement is frequent in multiple myeloma (MM). Whole-body magnetic resonance imaging (wb-MRI) enables to evaluate the whole BM. Reading of such whole-body scans is time-consuming, and yet radiologists can ...
Deep-learning image-reconstruction algorithm for dual-energy CT angiography with reduced iodine dose: preliminary results.
Clinical radiology
Nov 12, 2021
AIM: To evaluate the computed tomography (CT) attenuation values, background noise, arterial depiction, and image quality in whole-body dual-energy CT angiography (DECTA) at 40 keV with a reduced iodine dose using deep-learning image reconstruction (...
Multiclass classification of whole-body scintigraphic images using a self-defined convolutional neural network with attention modules.
Medical physics
Sep 14, 2021
PURPOSE: A self-defined convolutional neural network is developed to automatically classify whole-body scintigraphic images of concern (i.e., the normal, metastasis, arthritis, and thyroid carcinoma), automatically detecting diseases with whole-body ...
dSPIC: a deep SPECT image classification network for automated multi-disease, multi-lesion diagnosis.
BMC medical imaging
Aug 11, 2021
BACKGROUND: Functional imaging especially the SPECT bone scintigraphy has been accepted as the effective clinical tool for diagnosis, treatment, evaluation, and prevention of various diseases including metastasis. However, SPECT imaging is brightly c...
Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format.
Physics in medicine and biology
Aug 9, 2021
Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-bas...
Diagnostic performance of deep learning models for detecting bone metastasis on whole-body bone scan in prostate cancer.
European journal of nuclear medicine and molecular imaging
Aug 7, 2021
PURPOSE: We evaluated the performance of deep learning classifiers for bone scans of prostate cancer patients.
Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.
Clinical nutrition (Edinburgh, Scotland)
Jul 15, 2021
BACKGROUND & AIMS: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation...
Intra-Abdominal Fat
Male
Subcutaneous Fat
Middle Aged
Whole Body Imaging
Neural Networks, Computer
Nutrition Assessment
Positron Emission Tomography Computed Tomography
Republic of Korea
Abdomen
Retrospective Studies
Humans
Body Composition
Radiopharmaceuticals
Female
Sarcopenia
Fluorodeoxyglucose F18
Aged
Muscle, Skeletal
Reproducible Naevus Counts Using 3D Total Body Photography and Convolutional Neural Networks.
Dermatology (Basel, Switzerland)
Jul 8, 2021
BACKGROUND: The number of naevi on a person is the strongest risk factor for melanoma; however, naevus counting is highly variable due to lack of consistent methodology and lack of inter-rater agreement. Machine learning has been shown to be a valuab...
Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT.
Nature communications
Mar 25, 2021
Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existing supervise...
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