AIMC Journal:
Medical physics

Showing 431 to 440 of 732 articles

Deep learning algorithms for brain disease detection with magnetic induction tomography.

Medical physics Dec 18, 2020
PURPOSE: In order to improve the reconstruction accuracy of magnetic induction tomography (MIT) and achieve fast imaging especially in the detection of cerebral hemorrhage, artificial intelligence algorithms are proposed to improve the accuracy of MI...
Brain Deep Learning Artificial Intelligence Algorithms Brain Diseases Humans Phantoms, Imaging Tomography Image Processing, Computer-Assisted Magnetic Phenomena
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Low-dose CT image and projection dataset.

Medical physics Dec 16, 2020
PURPOSE: To describe a large, publicly available dataset comprising computed tomography (CT) projection data from patient exams, both at routine clinical doses and simulated lower doses.
Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted Artificial Intelligence Algorithms Tomography Scanners, X-Ray Computed Radiation Dosage Thorax
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Cross-modality deep learning: Contouring of MRI data from annotated CT data only.

Medical physics Dec 13, 2020
PURPOSE: Online adaptive radiotherapy would greatly benefit from the development of reliable auto-segmentation algorithms for organs-at-risk and radiation targets. Current practice of manual segmentation is subjective and time-consuming. While deep l...
Magnetic Resonance Imaging Neural Networks, Computer Humans Deep Learning Tomography, X-Ray Computed Image Processing, Computer-Assisted
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A k-space-to-image reconstruction network for MRI using recurrent neural network.

Medical physics Dec 12, 2020
PURPOSE: Reconstructing the images from undersampled k-space data are an ill-posed inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic resonance (MR) images directly from k-space data using a recurrent neural n...
Research Design Neural Networks, Computer Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Real-time biomechanics using the finite element method and machine learning: Review and perspective.

Medical physics Dec 7, 2020
PURPOSE: The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real...
Biomechanical Phenomena Finite Element Analysis Algorithms Computer Simulation Machine Learning
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MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images.

Medical physics Dec 7, 2020
PURPOSE: Pancreas segmentation is a difficult task because of the high intrapatient variability in the shape, size, and location of the organ, as well as the low contrast and small footprint of the CT scan. At present, the U-Net model is likely to le...
Pancreas Image Processing, Computer-Assisted Abdomen Neural Networks, Computer Tomography, X-Ray Computed
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Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.

Medical physics Nov 30, 2020
PURPOSE: The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM).
Humans Retrospective Studies Tomography, X-Ray Computed Colorectal Neoplasms Liver Neoplasms Deep Learning
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Deformable MR-CBCT prostate registration using biomechanically constrained deep learning networks.

Medical physics Nov 27, 2020
BACKGROUND AND PURPOSE: Radiotherapeutic dose escalation to dominant intraprostatic lesions (DIL) in prostate cancer could potentially improve tumor control. The purpose of this study was to develop a method to accurately register multiparametric mag...
Image Processing, Computer-Assisted Male Prostatic Neoplasms Humans Magnetic Resonance Imaging Deep Learning Spiral Cone-Beam Computed Tomography Algorithms
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Spatial feature fusion convolutional network for liver and liver tumor segmentation from CT images.

Medical physics Nov 27, 2020
PURPOSE: The accurate segmentation of liver and liver tumors from CT images can assist radiologists in decision-making and treatment planning. The contours of liver and liver tumors are currently obtained by manual labeling, which is time-consuming a...
Tomography, X-Ray Computed Image Processing, Computer-Assisted Humans Liver Neoplasms Neural Networks, Computer
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Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.

Medical physics Nov 23, 2020
PURPOSE: Post-reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoisi...
Myocardial Perfusion Imaging Phantoms, Imaging Tomography, Emission-Computed, Single-Photon Image Processing, Computer-Assisted Algorithms Signal-To-Noise Ratio Humans Deep Learning
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