AIMC Topic: Magnetic Resonance Imaging

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Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.

Magma (New York, N.Y.) Nov 21, 2023
OBJECTIVE: This study aims to assess the statistical significance of training parameters in 240 dense UNets (DUNets) used for enhancing low Signal-to-Noise Ratio (SNR) and undersampled MRI in various acquisition protocols. The objective is to determi...
Neural Networks, Computer Analysis of Variance Algorithms Humans Brain Reproducibility of Results Magnetic Resonance Imaging Deep Learning Image Enhancement Image Processing, Computer-Assisted Signal-To-Noise Ratio
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Prediction of recurrence risk factors in patients with early-stage cervical cancers by nomogram based on MRI handcrafted radiomics features and deep learning features: a dual-center study.

Abdominal radiology (New York) Nov 21, 2023
PURPOSE: To establish and validate a deep learning radiomics nomogram (DLRN) based on intratumoral and peritumoral regions of MR images and clinical characteristics to predict recurrence risk factors in early-stage cervical cancer and to clarify whet...
Magnetic Resonance Imaging Humans Retrospective Studies Uterine Cervical Neoplasms Deep Learning Nomograms Female Risk Factors Radiomics
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Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection.

International journal of computer assisted radiology and surgery Nov 20, 2023
PURPOSE: Accurate and automatic segmentation of basal ganglia from magnetic resonance (MR) images is important for diagnosis and treatment of various brain disorders. However, the basal ganglia segmentation is a challenging task because of the class ...
Image Processing, Computer-Assisted Humans Magnetic Resonance Imaging Neural Networks, Computer Basal Ganglia Brain
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An attention 3DUNET and visual geometry group-19 based deep neural network for brain tumor segmentation and classification from MRI.

Journal of biomolecular structure & dynamics Nov 18, 2023
There has been an abrupt increase in brain tumor (BT) related medical cases during the past ten years. The tenth most typical type of tumor affecting millions of people is the BT. The cure rate can, however, rise if it is found early. When evaluating...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Brain Neoplasms Deep Learning Humans Neural Networks, Computer Algorithms
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Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma.

Academic radiology Nov 18, 2023
RATIONALE AND OBJECTIVES: To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients wi...
Male Middle Aged Magnetic Resonance Imaging Female Squamous Cell Carcinoma of Head and Neck Treatment Outcome Induction Chemotherapy Head and Neck Neoplasms Aged Imaging Genomics Contrast Media Humans Adult Machine Learning
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Ultra-High-Resolution T2-Weighted PROPELLER MRI of the Rectum With Deep Learning Reconstruction: Assessment of Image Quality and Diagnostic Performance.

Investigative radiology Nov 17, 2023
OBJECTIVE: The aim of this study was to evaluate the impact of ultra-high-resolution acquisition and deep learning reconstruction (DLR) on the image quality and diagnostic performance of T2-weighted periodically rotated overlapping parallel lines wit...
Signal-To-Noise Ratio Rectum Adult Reproducibility of Results Rectal Neoplasms Humans Male Image Interpretation, Computer-Assisted Aged Prospective Studies Middle Aged Aged, 80 and over Female Deep Learning Magnetic Resonance Imaging Neoplasm Staging
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Searching for the grail: may machine learning be a road to clinical use of brain MRI segmentation?

European radiology Nov 17, 2023
Machine Learning Brain Magnetic Resonance Imaging Humans
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Differentiation Between Glioblastoma and Metastatic Disease on Conventional MRI Imaging Using 3D-Convolutional Neural Networks: Model Development and Validation.

Academic radiology Nov 17, 2023
RATIONALE AND OBJECTIVES: Imaging-based differentiation between glioblastoma (GB) and brain metastases (BM) remains challenging. Our aim was to evaluate the performance of 3D-convolutional neural networks (CNN) to address this binary classification p...
Imaging, Three-Dimensional Magnetic Resonance Imaging Diagnosis, Differential Brain Neoplasms Male Middle Aged Glioblastoma Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Female Reproducibility of Results Aged Adult
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Application of natural language processing to post-structuring of rectal cancer MRI reports.

Clinical radiology Nov 17, 2023
AIM: To evaluate a natural language processing (NLP) system for extracting structured information from the free-form text of rectal cancer magnetic resonance imaging (MRI) reports written in Chinese.
Rectal Neoplasms Humans Research Report Magnetic Resonance Imaging Natural Language Processing
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Editorial for "Glioblastoma and Solitary Brain Metastasis: Differentiation by Integrating Demographic-MRI and Deep-Learning Radiomics Signatures".

Journal of magnetic resonance imaging : JMRI Nov 16, 2023
Humans Brain Magnetic Resonance Imaging Diagnosis, Differential Glioblastoma Radiomics Brain Neoplasms Deep Learning
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