AIMC Topic: Tomography

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A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.

Journal of digital imaging Aug 14, 2023
This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images ...
Male Deep Learning Humans Algorithms Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Tomography Image Processing, Computer-Assisted Abdomen
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A deep-learning assisted bioluminescence tomography method to enable radiation targeting in rat glioblastoma.

Physics in medicine and biology Jul 24, 2023
. A novel solution is required for accurate 3D bioluminescence tomography (BLT) based glioblastoma (GBM) targeting. The provided solution should be computationally efficient to support real-time treatment planning, thus reducing the x-ray imaging dos...
Rats Deep Learning Models, Animal Glioblastoma Cone-Beam Computed Tomography Animals Tomography
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Label-free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning.

Journal of biophotonics Jun 5, 2023
For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In th...
Humans Deep Learning Thrombosis Brain Ischemia Ischemic Stroke Stroke Tomography
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Optimal machine learning methods for prediction of high-flow nasal cannula outcomes using image features from electrical impedance tomography.

Computer methods and programs in biomedicine May 16, 2023
BACKGROUND: High-flow nasal cannula (HNFC) is able to provide ventilation support for patients with hypoxic respiratory failure. Early prediction of HFNC outcome is warranted, since failure of HFNC might delay intubation and increase mortality rate. ...
Tomography Cannula Humans Machine Learning Bayes Theorem Electric Impedance
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Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry.

Scientific reports Apr 13, 2023
Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry syst...
Liquid Biopsy Tomography Neoplastic Cells, Circulating Flow Cytometry Artificial Intelligence Humans Machine Learning
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Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery Mar 15, 2023
PURPOSE: Classical prognostic indicators of head and neck squamous cell carcinoma (HNSCC) can no longer meet the clinical needs of precision medicine. This study aimed to establish a radiomics model to predict Granzyme A (GZMA) expression in patients...
Humans Granzymes Squamous Cell Carcinoma of Head and Neck Prognosis RNA, Messenger Head and Neck Neoplasms Tomography Machine Learning
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Pulmonary nodule volumetric accuracy of a deep learning-based reconstruction algorithm in low-dose computed tomography: A phantom study.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) Nov 5, 2022
PURPOSE: To compare the image properties and pulmonary nodule volumetric accuracies among deep learning-based reconstruction (DLR), filtered back projection (FBP), and hybrid iterative reconstruction (hybrid IR) in low-dose computed tomography (LDCT)...
Deep Learning Tomography Tomography, X-Ray Computed
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Differentiation of eosinophilic and non-eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning.

Clinical otolaryngology : official journal of ENT-UK ; official journal of Netherlands Society for Oto-Rhino-Laryngology & Cervico-Facial Surgery Oct 14, 2022
OBJECTIVES: This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS (NECRS) on preoperative CT.
Tomography Rhinitis Eosinophilia Deep Learning Sinusitis Humans Tomography, X-Ray Computed
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A deep learning and Monte Carlo based framework for bioluminescence imaging center of mass-guided glioblastoma targeting.

Physics in medicine and biology Jul 8, 2022
Bioluminescence imaging (BLI) is a valuable tool for non-invasive monitoring of glioblastoma multiforme (GBM) tumor-bearing small animals without incurring x-ray radiation burden. However, the use of this imaging modality is limited due to photon sca...
Monte Carlo Method Tomography Rats Glioblastoma Neural Networks, Computer Animals Deep Learning
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Size-adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset.

Medical physics Jun 20, 2022
PURPOSE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these methods generally target one particular lesion type, that is, pulmonary nodules. In this work, we intend t...
Tomography Tomography, X-Ray Computed Deep Learning Humans Research Design
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