AIMC Topic:
Magnetic Resonance Imaging

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Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study.

Scientific reports Mar 7, 2024
In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development o...
Diffusion Magnetic Resonance Imaging Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Diffusion Tensor Imaging Algorithms Deep Learning
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Synthesizing Contrast-Enhanced MR Images from Noncontrast MR Images Using Deep Learning.

AJNR. American journal of neuroradiology Mar 7, 2024
BACKGROUND AND PURPOSE: Recent developments in deep learning methods offer a potential solution to the need for alternative imaging methods due to concerns about the toxicity of gadolinium-based contrast agents. The purpose of the study was to synthe...
Magnetic Resonance Imaging Gadolinium Humans Brain Brain Neoplasms Deep Learning Contrast Media
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A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping.

IEEE journal of biomedical and health informatics Mar 6, 2024
Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation....
Brain Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans
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Brain MR Image Classification Using Superpixel-Based Deep Transfer Learning.

IEEE journal of biomedical and health informatics Mar 6, 2024
Nowadays, brain MR (Magnetic Resonance) images are widely used by clinicians to examine the brain's anatomy to look into various pathological conditions like cerebrovascular incidents and neuro-degenerative diseases. Generally, these diseases can be ...
Algorithms Humans Brain Neural Networks, Computer Magnetic Resonance Imaging Machine Learning
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Using a deep learning prior for accelerating hyperpolarized C MRSI on synthetic cancer datasets.

Magnetic resonance in medicine Mar 5, 2024
PURPOSE: We aimed to incorporate a deep learning prior with k-space data fidelity for accelerating hyperpolarized carbon-13 MRSI, demonstrated on synthetic cancer datasets.
Algorithms Brain Brain Neoplasms Animals Male Deep Learning Phantoms, Imaging Humans Carbon Isotopes Image Processing, Computer-Assisted Magnetic Resonance Imaging Mice Prostatic Neoplasms Signal-To-Noise Ratio
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Deep learning-based automatic segmentation of meningioma from T1-weighted contrast-enhanced MRI for preoperative meningioma differentiation using radiomic features.

BMC medical imaging Mar 5, 2024
BACKGROUND: This study aimed to establish a dedicated deep-learning model (DLM) on routine magnetic resonance imaging (MRI) data to investigate DLM performance in automated detection and segmentation of meningiomas in comparison to manual segmentatio...
Humans Radiomics Deep Learning Magnetic Resonance Imaging Meningioma Meningeal Neoplasms
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A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.

Scientific reports Mar 5, 2024
Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public ava...
Radiography Breast Density Deep Learning Humans Magnetic Resonance Imaging Female Breast Neoplasms
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Wavelet-Improved Score-Based Generative Model for Medical Imaging.

IEEE transactions on medical imaging Mar 5, 2024
The score-based generative model (SGM) has demonstrated remarkable performance in addressing challenging under-determined inverse problems in medical imaging. However, acquiring high-quality training datasets for these models remains a formidable tas...
Magnetic Resonance Imaging Artifacts Signal-To-Noise Ratio Tomography, X-Ray Computed Image Processing, Computer-Assisted Neural Networks, Computer Algorithms
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Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

Liver international : official journal of the International Association for the Study of the Liver Mar 4, 2024
BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI ...
Magnetic Resonance Imaging Neoplasm Invasiveness Neoplasm Recurrence, Local Disease-Free Survival Microvessels Humans Deep Learning Retrospective Studies Liver Neoplasms Female Carcinoma, Hepatocellular Aged Male Middle Aged
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Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.

Abdominal radiology (New York) Mar 3, 2024
PURPOSE: To investigate the value of a multimodal deep learning (MDL) model based on computed tomography (CT) and magnetic resonance imaging (MRI) for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).
Female Aged Adult Retrospective Studies Tomography, X-Ray Computed Liver Neoplasms Carcinoma, Hepatocellular Magnetic Resonance Imaging Male Middle Aged Deep Learning Predictive Value of Tests Humans Microvessels Neoplasm Invasiveness Multimodal Imaging
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