AIMC Topic: Magnetic Resonance Imaging
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Decentralized collaborative multi-institutional PET attenuation and scatter correction using federated deep learning.
European journal of nuclear medicine and molecular imaging
Dec 12, 2022
PURPOSE: Attenuation correction and scatter compensation (AC/SC) are two main steps toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI systems. These can be effectively tackled via deep learning (DL) methods. However, t...
Deep learning-based image reconstruction improves radiologic evaluation of pituitary axis and cavernous sinus invasion in pituitary adenoma.
European journal of radiology
Dec 12, 2022
PURPOSE: To compare performance of 1-mm deep learning reconstruction (DLR) with 3-mm routine MRI imaging for the delineation of pituitary axis and identification of cavernous sinus invasion for pituitary macroadenoma.
A multi-perspective information aggregation network for automated-staging detection of nasopharyngeal carcinoma.
Physics in medicine and biology
Dec 9, 2022
Accurate-staging is important when planning personalized radiotherapy. However,-staging via manual slice-by-slice inspection is time-consuming while tumor sizes and shapes are heterogeneous, and junior physicians find such inspection challenging. Wit...
Improving accelerated MRI by deep learning with sparsified complex data.
Magnetic resonance in medicine
Dec 8, 2022
PURPOSE: To obtain high-quality accelerated MR images with complex-valued reconstruction from undersampled k-space data.
Deriving a robust deep-learning model for subcortical brain segmentation by using a large-scale database: Preprocessing, reproducibility, and accuracy of volume estimation.
NMR in biomedicine
Dec 8, 2022
Increasing the accuracy and reproducibility of subcortical brain segmentation is advantageous in various related clinical applications. In this study, we derived a segmentation method based on a convolutional neural network (i.e., U-Net) and a large-...
A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction.
Journal of magnetic resonance (San Diego, Calif. : 1997)
Dec 8, 2022
Multi-contrast magnetic resonance imaging (MRI) can provide richer diagnosis information. The data acquisition time, however, is increased than single-contrast imaging. To reduce this time, k-space undersampling is an effective way but a smart recons...
Fully automated cardiac MRI segmentation using dilated residual network.
Medical physics
Dec 7, 2022
PURPOSE: Cardiac ventricle segmentation from cine magnetic resonance imaging (CMRI) is a recognized modality for the noninvasive assessment of cardiovascular pathologies. Deep learning based algorithms achieved state-of-the-art result performance fro...
Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools.
Computers in biology and medicine
Dec 7, 2022
BACKGROUND: Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients s...
A Deep Learning Model for Automatic Detection and Classification of Disc Herniation in Magnetic Resonance Images.
IEEE journal of biomedical and health informatics
Dec 7, 2022
Localization of lumbar discs in magnetic resonance imaging (MRI) is a challenging task, due to a vast range of shape, size, number, and appearance of discs and vertebrae. Based on a review of the cutting-edge methods, the majority of applied techniqu...
Diffusion-weighted MRI with deep learning for visualizing treatment results of MR-guided HIFU ablation of uterine fibroids.
European radiology
Dec 6, 2022
OBJECTIVES: No method is available to determine the non-perfused volume (NPV) repeatedly during magnetic resonance-guided high-intensity focused ultrasound (MR-HIFU) ablations of uterine fibroids, as repeated acquisition of contrast-enhanced T1-weigh...
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