AIMC Topic:
Magnetic Resonance Imaging
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Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly.
European radiology
May 10, 2024
OBJECTIVES: To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lat...
SPICER: Self-supervised learning for MRI with automatic coil sensitivity estimation and reconstruction.
Magnetic resonance in medicine
May 10, 2024
PURPOSE: To introduce a novel deep model-based architecture (DMBA), SPICER, that uses pairs of noisy and undersampled k-space measurements of the same object to jointly train a model for MRI reconstruction and automatic coil sensitivity estimation.
Whole-body magnetic resonance imaging at 0.05 Tesla.
Science (New York, N.Y.)
May 10, 2024
Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress ha...
DFUSNN: zero-shot dual-domain fusion unsupervised neural network for parallel MRI reconstruction.
Physics in medicine and biology
May 10, 2024
. Recently, deep learning models have been used to reconstruct parallel magnetic resonance (MR) images from undersampled k-space data. However, most existing approaches depend on large databases of fully sampled MR data for training, which can be cha...
Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.
European radiology
May 9, 2024
OBJECTIVE: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the ...
Robust Automated Tumour Segmentation Network Using 3D Direction-Wise Convolution and Transformer.
Journal of imaging informatics in medicine
May 9, 2024
Semantic segmentation of tumours plays a crucial role in fundamental medical image analysis and has a significant impact on cancer diagnosis and treatment planning. UNet and its variants have achieved state-of-the-art results on various 2D and 3D med...
A Review of Artificial Intelligence in Breast Imaging.
Tomography (Ann Arbor, Mich.)
May 9, 2024
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging...
Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.
PloS one
May 9, 2024
OBJECTIVE: To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging.
Involving logical clinical knowledge into deep neural networks to improve bladder tumor segmentation.
Medical image analysis
May 8, 2024
Segmentation of bladder tumors from medical radiographic images is of great significance for early detection, diagnosis and prognosis evaluation of bladder cancer. Deep Convolution Neural Networks (DCNNs) have been successfully used for bladder tumor...
Comparative assessment of established and deep learning-based segmentation methods for hippocampal volume estimation in brain magnetic resonance imaging analysis.
NMR in biomedicine
May 7, 2024
In this study, our objective was to assess the performance of two deep learning-based hippocampal segmentation methods, SynthSeg and TigerBx, which are readily available to the public. We contrasted their performance with that of two established tech...
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