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Radiation Dosage

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External validation of the performance of commercially available deep-learning-based lung nodule detection on low-dose CT images for lung cancer screening in Japan.

Japanese journal of radiology 39613978
PURPOSE: Artificial intelligence (AI) algorithms for lung nodule detection have been developed to assist radiologists. However, external validation of its performance on low-dose CT (LDCT) images is insufficient. We examined the performance of the co...
Aged Aged, 80 and over Deep Learning Early Detection of Cancer Female Humans Japan Lung Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Sensitivity and Specificity Solitary Pulmonary Nodule Tomography, X-Ray Computed
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3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.

European radiology 39609283
OBJECTIVES: Low-dose (LD) PET imaging would lead to reduced image quality and diagnostic efficacy. We propose a deep learning (DL) method to reduce radiotracer dosage for PET studies while maintaining diagnostic quality.
Adult Aged Aged, 80 and over Brain Deep Learning Female Humans Imaging, Three-Dimensional Male Middle Aged Positron-Emission Tomography Radiation Dosage Radiopharmaceuticals Retrospective Studies
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A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.

European journal of nuclear medicine and molecular imaging 39585354
PURPOSE: Recent development in positron emission tomography (PET) dramatically increased the effective sensitivity by increasing the geometric coverage leading to total-body PET imaging. This encouraging breakthrough brings the hope of ultra-low dose...
Deep Learning Humans Image Processing, Computer-Assisted Positron-Emission Tomography Radiation Dosage Wavelet Analysis
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An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting: a reader study.

European radiology experimental 39565453
BACKGROUND: To retrospectively assess the added value of an artificial intelligence (AI) algorithm for detecting pulmonary nodules on ultra-low-dose computed tomography (ULDCT) performed at the emergency department (ED).
Aged Algorithms Artificial Intelligence Deep Learning Emergency Service, Hospital Female Humans Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Prospective Studies Radiation Dosage Retrospective Studies Solitary Pulmonary Nodule Tomography, X-Ray Computed
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Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.

Abdominal radiology (New York) 39560744
OBJECTIVES: To evaluate the effectiveness of super-resolution deep learning reconstruction (SR-DLR) in low-dose abdominal computed tomography (CT) imaging compared with hybrid iterative reconstruction (HIR) and conventional deep learning reconstructi...
Adult Aged Aged, 80 and over Algorithms Artifacts Deep Learning Female Humans Male Middle Aged Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Retrospective Studies Signal-To-Noise Ratio Tomography, X-Ray Computed
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Feasibility of Ultra-low Radiation and Contrast Medium Dosage in Aortic CTA Using Deep Learning Reconstruction at 60 kVp: An Image Quality Assessment.

Academic radiology 39542806
OBJECTIVE: To assess the viability of using ultra-low radiation and contrast medium (CM) dosage in aortic computed tomography angiography (CTA) through the application of low tube voltage (60kVp) and a novel deep learning image reconstruction algorit...
Aged Algorithms Aorta Aortography Computed Tomography Angiography Contrast Media Deep Learning Feasibility Studies Female Humans Male Middle Aged Phantoms, Imaging Prospective Studies Radiation Dosage Radiographic Image Interpretation, Computer-Assisted
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Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.

Journal of applied clinical medical physics 39540681
OBJECTIVE: We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Abdominal Neoplasms Algorithms Deep Learning Feasibility Studies Female Humans Image Processing, Computer-Assisted Male Organs at Risk Pelvic Neoplasms Phantoms, Imaging Prospective Studies Radiation Dosage Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Image-Guided Radiotherapy, Intensity-Modulated Signal-To-Noise Ratio Tomography, X-Ray Computed
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Deep learning prediction of scenario doses for direct plan robustness evaluations in IMPT for head-and-neck.

Physics in medicine and biology 39530440
. Intensity modulated proton therapy (IMPT) is susceptible to uncertainties in patient setup and proton range. Robust optimization is employed in IMPT treatment planning to ensure sufficient coverage of the clinical target volume (CTV) in predefined ...
Deep Learning Head and Neck Neoplasms Humans Proton Therapy Radiation Dosage Radiotherapy Dosage Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Tomography, X-Ray Computed
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State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.

Radiographics : a review publication of the Radiological Society of North America, Inc 39612283
The implementation of deep neural networks has spurred the creation of deep learning reconstruction (DLR) CT algorithms. DLR CT techniques encompass a spectrum of deep learning-based methodologies that operate during the different steps of the image ...
Algorithms Deep Learning Humans Image Processing, Computer-Assisted Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Tomography, X-Ray Computed
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Converting dose-area product to effective dose in dental cone-beam computed tomography using organ-specific deep learning.

Dento maxillo facial radiology 39607772
OBJECTIVE: To develop an accurate method for converting dose-area product (DAP) to patient dose for dental cone-beam computed tomography (CBCT) using deep learning.
Adult Cone-Beam Computed Tomography Deep Learning Humans Neural Networks, Computer Phantoms, Imaging Radiation Dosage Radiography, Dental
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