AIMC Topic: Image Processing, Computer-Assisted

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Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.

Journal of applied clinical medical physics Feb 19, 2024
PURPOSE: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive fu...
Infant Deep Learning Adolescent Adult Humans Brain Neoplasms Brain Workflow Young Adult Organs at Risk Radiotherapy Planning, Computer-Assisted Child, Preschool Child Image Processing, Computer-Assisted
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Deep learning for head and neck semi-supervised semantic segmentation.

Physics in medicine and biology Feb 19, 2024
. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, th...
Organs at Risk Semantics Deep Learning Image Processing, Computer-Assisted Humans Head and Neck Neoplasms Tomography, X-Ray Computed
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A model-based direct inversion network (MDIN) for dual spectral computed tomography.

Physics in medicine and biology Feb 19, 2024
. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Add...
Neural Networks, Computer Models, Theoretical Artifacts Phantoms, Imaging Algorithms Tomography, X-Ray Computed Image Processing, Computer-Assisted
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Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Journal of applied clinical medical physics Feb 18, 2024
BACKGROUND: Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT).
Radiotherapy, Image-Guided Defibrillators, Implantable Image Processing, Computer-Assisted Magnetic Resonance Imaging Artifacts Humans Deep Learning
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Comments on "A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI".

Journal of magnetic resonance imaging : JMRI Feb 17, 2024
Humans Image Processing, Computer-Assisted Breast Image Interpretation, Computer-Assisted Female Breast Neoplasms Deep Learning Diffusion Magnetic Resonance Imaging
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Accuracy of automated segmentation and volumetry of acute intracerebral hemorrhage following minimally invasive surgery using a patch-based convolutional neural network in a small dataset.

Neuroradiology Feb 17, 2024
PURPOSE: In cases of acute intracerebral hemorrhage (ICH) volume estimation is of prognostic and therapeutic value following minimally invasive surgery (MIS). The ABC/2 method is widely used, but suffers from inaccuracies and is time consuming. Super...
Cerebral Hemorrhage Minimally Invasive Surgical Procedures Middle Aged Image Processing, Computer-Assisted Neural Networks, Computer Machine Learning Tomography, X-Ray Computed Aged Aged, 80 and over Male Humans
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Responds to the Comments on "A Channel-Dimensional Feature-Reconstructed Deep Learning Model for Predicting Breast Cancer Molecular Subtypes on Overall b-Value Diffusion-Weighted MRI".

Journal of magnetic resonance imaging : JMRI Feb 16, 2024
Female Diffusion Magnetic Resonance Imaging Image Processing, Computer-Assisted Breast Neoplasms Breast Deep Learning Humans Image Interpretation, Computer-Assisted
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Ultra-high-resolution CT of the temporal bone: Comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction.

Diagnostic and interventional imaging Feb 16, 2024
PURPOSE: The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction a...
Humans Algorithms Tomography, X-Ray Computed Deep Learning Temporal Bone Stapes Chorda Tympani Nerve Image Processing, Computer-Assisted Phantoms, Imaging
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Performance evaluation of three versions of a convolutional neural network for object detection and segmentation using a multiclass and reduced panoramic radiograph dataset.

Journal of dentistry Feb 16, 2024
OBJECTIVES: To evaluate the diagnostic performance of three versions of a deep-learning convolutional neural network in terms of object detection and segmentation using a multiclass panoramic radiograph dataset.
Tooth Maxilla Mandibular Nerve Dental Implants Humans Neural Networks, Computer Mandible Deep Learning Image Processing, Computer-Assisted Radiography, Panoramic
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Using deep learning-based denoising and iterative reconstruction to reduce radiation exposure - How low can we go?

European journal of radiology Feb 16, 2024
Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Humans Deep Learning Radiation Dosage Image Processing, Computer-Assisted Radiation Exposure Algorithms
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