AIMC Topic:
Magnetic Resonance Imaging
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Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.
Computer methods and programs in biomedicine
Jun 7, 2023
UNLABELLED: Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants w...
Using deep learning to accelerate temporomandibular joint MRI at 3 T: A case report.
Asian journal of surgery
Jun 7, 2023
Brain-optimized deep neural network models of human visual areas learn non-hierarchical representations.
Nature communications
Jun 7, 2023
Deep neural networks (DNNs) optimized for visual tasks learn representations that align layer depth with the hierarchy of visual areas in the primate brain. One interpretation of this finding is that hierarchical representations are necessary to accu...
Deep learning for predicting future lesion emergence in high-risk breast MRI screening: a feasibility study.
European radiology experimental
Jun 7, 2023
BACKGROUND: International societies have issued guidelines for high-risk breast cancer (BC) screening, recommending contrast-enhanced magnetic resonance imaging (CE-MRI) of the breast as a supplemental diagnostic tool. In our study, we tested the app...
Deep learning-based Lorentzian fitting of water saturation shift referencing spectra in MRI.
Magnetic resonance in medicine
Jun 6, 2023
PURPOSE: Water saturation shift referencing (WASSR) Z-spectra are used commonly for field referencing in chemical exchange saturation transfer (CEST) MRI. However, their analysis using least-squares (LS) Lorentzian fitting is time-consuming and prone...
Deep learning phase error correction for cerebrovascular 4D flow MRI.
Scientific reports
Jun 5, 2023
Background phase errors in 4D Flow MRI may negatively impact blood flow quantification. In this study, we assessed their impact on cerebrovascular flow volume measurements, evaluated the benefit of manual image-based correction, and assessed the pote...
Deep Learning Accelerated Image Reconstruction of Fluid-Attenuated Inversion Recovery Sequence in Brain Imaging: Reduction of Acquisition Time and Improvement of Image Quality.
Academic radiology
Jun 4, 2023
RATIONALE AND OBJECTIVES: Fluid-attenuated inversion recovery (FLAIR) imaging is playing an increasingly significant role in the detection of brain metastases with a concomitant increase in the number of magnetic resonance imaging (MRI) examinations....
A single stage knowledge distillation network for brain tumor segmentation on limited MR image modalities.
Computer methods and programs in biomedicine
Jun 4, 2023
BACKGROUND AND OBJECTIVE: Precisely segmenting brain tumors using multimodal Magnetic Resonance Imaging (MRI) is an essential task for early diagnosis, disease monitoring, and surgical planning. Unfortunately, the complete four image modalities utili...
Imaging of lung cancer.
Current problems in cancer
Jun 4, 2023
Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imag...
Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.
Journal of magnetic resonance imaging : JMRI
Jun 1, 2023
BACKGROUND: F-fluorodeoxyglucose (FDG) positron emission tomography (PET) is valuable for determining presence of viable tumor, but is limited by geographical restrictions, radiation exposure, and high cost.
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