AIMC Topic:
Magnetic Resonance Imaging
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Application of a deep learning algorithm for three-dimensional T1-weighted gradient-echo imaging of gadoxetic acid-enhanced MRI in patients at a high risk of hepatocellular carcinoma.
Abdominal radiology (New York)
Dec 14, 2023
PURPOSE: To evaluate the efficacy of a vendor-specific deep learning reconstruction algorithm (DLRA) in enhancing image quality and focal lesion detection using three-dimensional T1-weighted gradient-echo images in gadoxetic acid-enhanced liver magne...
Noninvasive diagnostic imaging for endometriosis part 1: a systematic review of recent developments in ultrasound, combination imaging, and artificial intelligence.
Fertility and sterility
Dec 14, 2023
Endometriosis affects 1 in 9 women and those assigned female at birth. However, it takes 6.4 years to diagnose using the conventional standard of laparoscopy. Noninvasive imaging enables a timelier diagnosis, reducing diagnostic delay as well as the ...
Identification of IDH and TERTp mutations using dynamic susceptibility contrast MRI with deep learning in 162 gliomas.
European journal of radiology
Dec 13, 2023
PURPOSE: Isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase gene promoter (TERTp) mutations play crucial roles in glioma biology. Such genetic information is typically obtained invasively from excised tumor tissue; however, these mut...
AI approach to biventricular function assessment in cine-MRI: an ultra-small training dataset and multivendor study.
Physics in medicine and biology
Dec 13, 2023
. It was a great challenge to train an excellent and generalized model on an ultra-small data set composed of multi-orientation cardiac cine magnetic resonance imaging (MRI) images. We try to develop a 3D deep learning method based on an ultra-small ...
Deep learning-based automated kidney and cyst segmentation of autosomal dominant polycystic kidney disease using single vs. multi-institutional data.
Clinical imaging
Dec 12, 2023
PURPOSE: This study aimed to investigate if a deep learning model trained with a single institution's data has comparable accuracy to that trained with multi-institutional data for segmenting kidney and cyst regions in magnetic resonance (MR) images ...
Deep learning MRI-only synthetic-CT generation for pelvis, brain and head and neck cancers.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 12, 2023
BACKGROUND AND PURPOSE: MRI-only planning relies on dosimetrically accurate synthetic-CT (sCT) generation to allow dose calculation. Here we validated the dosimetric accuracy of sCTs generated using a deep learning algorithm for pelvic, brain and hea...
Correcting synthetic MRI contrast-weighted images using deep learning.
Magnetic resonance imaging
Dec 12, 2023
Synthetic magnetic resonance imaging (MRI) offers a scanning paradigm where a fast multi-contrast sequence can be used to estimate underlying quantitative tissue parameter maps, which are then used to synthesize any desirable clinical contrast by ret...
Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study.
European journal of radiology
Dec 12, 2023
PURPOSE: To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality.
Prediction of brain sex from EEG: using large-scale heterogeneous dataset for developing a highly accurate and interpretable ML model.
NeuroImage
Dec 12, 2023
This study presents a comprehensive examination of sex-related differences in resting-state electroencephalogram (EEG) data, leveraging two different types of machine learning models to predict an individual's sex. We utilized data from the Two Decad...
Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.
BMC medical imaging
Dec 11, 2023
BACKGROUND: The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty ...
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