AIMC Journal:
Medical physics

Showing 591 to 600 of 759 articles

Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations.

Medical physics Jun 17, 2019
PURPOSE: The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods use only patient anatomy as input and assume consistent beam configuration for all ...
Lung Neoplasms Radiotherapy Planning, Computer-Assisted Radiotherapy Dosage Radiotherapy, Intensity-Modulated Radiation Dosage Humans Deep Learning
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MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.

Medical physics Jun 12, 2019
PURPOSE: Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the pati...
Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging Tomography, X-Ray Computed
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Deep learning-based carotid media-adventitia and lumen-intima boundary segmentation from three-dimensional ultrasound images.

Medical physics Jun 11, 2019
PURPOSE: Quantification of carotid plaques has been shown to be important for assessing as well as monitoring the progression and regression of carotid atherosclerosis. Various metrics have been proposed and methods of measurements ranging from manua...
Aged Deep Learning Time Factors Humans Carotid Arteries Tunica Intima Adventitia Ultrasonography Imaging, Three-Dimensional
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Dose distribution prediction in isodose feature-preserving voxelization domain using deep convolutional neural network.

Medical physics Jun 7, 2019
PURPOSE: To implement a framework for dose prediction using a deep convolutional neural network (CNN) based on the concept of isodose feature-preserving voxelization (IFPV) in simplifying the representation of the dose distribution.
Radiation Dosage Radiotherapy Dosage Deep Learning Radiotherapy, Intensity-Modulated Radiotherapy Planning, Computer-Assisted
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Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

Medical physics Jun 7, 2019
PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from...
Tomography, X-Ray Computed Female Humans Machine Learning Middle Aged Image Processing, Computer-Assisted Lung Neoplasms Adult Male Diagnosis, Differential Reference Standards
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Projection-domain scatter correction for cone beam computed tomography using a residual convolutional neural network.

Medical physics Jun 5, 2019
PURPOSE: Scatter is a major factor degrading the image quality of cone beam computed tomography (CBCT). Conventional scatter correction strategies require handcrafted analytical models with ad hoc assumptions, which often leads to less accurate scatt...
Scattering, Radiation Neural Networks, Computer Deep Learning Cone-Beam Computed Tomography Image Processing, Computer-Assisted Artifacts Monte Carlo Method Workflow
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Ultrasound prostate segmentation based on multidirectional deeply supervised V-Net.

Medical physics May 29, 2019
PURPOSE: Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle p...
Image Processing, Computer-Assisted Prostate Deep Learning Humans Observer Variation Ultrasonography Male Supervised Machine Learning
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Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery.

Medical physics May 21, 2019
PURPOSE: Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and su...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Radiosurgery Machine Learning Contrast Media Arteriovenous Malformations Brain Automation
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Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network.

Medical physics May 11, 2019
PURPOSE: In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distribution function (fODF) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultima...
Diffusion Tensor Imaging Time Factors Image Processing, Computer-Assisted Deep Learning
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Shape constrained fully convolutional DenseNet with adversarial training for multiorgan segmentation on head and neck CT and low-field MR images.

Medical physics May 6, 2019
PURPOSE: Image-guided radiotherapy provides images not only for patient positioning but also for online adaptive radiotherapy. Accurate delineation of organs-at-risk (OARs) on Head and Neck (H&N) CT and MR images is valuable to both initial treatment...
Neural Networks, Computer Tomography, X-Ray Computed Head and Neck Neoplasms Magnetic Resonance Imaging Image Processing, Computer-Assisted Humans Organs at Risk Radiotherapy, Image-Guided
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