AIMC Topic: Magnetic Resonance Imaging
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Deep-learning-based methods of attenuation correction for SPECT and PET.
Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology
Jun 9, 2022
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to gene...
Ensemble deep learning model for predicting anterior cruciate ligament tear from lateral knee radiograph.
Skeletal radiology
Jun 9, 2022
OBJECTIVE: To develop an ensemble deep learning model (DLM) predicting anterior cruciate ligament (ACL) tears from lateral knee radiographs and to evaluate its diagnostic performance.
Editorial for "Automated Segmentation of Brain Metastases on T1-Weighted MRI Using Convolutional Neural Network: Impact of Using Volume Aware Loss and Sampling Strategy".
Journal of magnetic resonance imaging : JMRI
Jun 9, 2022
Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 9, 2022
Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetr...
A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.
European radiology
Jun 8, 2022
OBJECTIVES: To evaluate the value of deep learning (DL) combining multimodal radiomics and clinical and imaging features for differentiating ocular adnexal lymphoma (OAL) from idiopathic orbital inflammation (IOI).
Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma.
World journal of surgical oncology
Jun 8, 2022
BACKGROUND: Preoperative prediction of microvascular invasion (MVI) is critical for treatment strategy making in patients with hepatocellular carcinoma (HCC). We aimed to develop a deep learning (DL) model based on preoperative dynamic contrast-enhan...
Prediction for Distant Metastasis of Breast Cancer Using Dynamic Contrast-Enhanced Magnetic Resonance Imaging Images under Deep Learning.
Computational intelligence and neuroscience
Jun 8, 2022
This research aimed to explore the effect of using magnetic resonance imaging (MRI) radiomic features to establish a model for predicting distant metastasis under dynamic contrast-enhanced MRI imaging with deep learning algorithms. The deep learning ...
Deep learning models of cognitive processes constrained by human brain connectomes.
Medical image analysis
Jun 7, 2022
Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity ...
Deep learning DCE-MRI parameter estimation: Application in pancreatic cancer.
Medical image analysis
Jun 7, 2022
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an MRI technique for quantifying perfusion that can be used in clinical applications for classification of tumours and other types of diseases. Conventionally, the non-linear least squ...
Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures.
Molecules (Basel, Switzerland)
Jun 7, 2022
Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challeng...
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