AIMC Topic: Contrast Media
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Improved Resolution and Image Quality of Musculoskeletal Magnetic Resonance Imaging using Deep Learning-based Denoising Reconstruction: A Prospective Clinical Study.
Skeletal radiology
Apr 24, 2024
OBJECTIVE: To prospectively evaluate a deep learning-based denoising reconstruction (DLR) for improved resolution and image quality in musculoskeletal (MSK) magnetic resonance imaging (MRI).
Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer.
Computer methods and programs in biomedicine
Apr 22, 2024
BACKGROUND AND OBJECTIVE: Accurate identification of molecular biomarker statuses is crucial in cancer diagnosis, treatment, and prognosis. Studies have demonstrated that medical images could be utilized for non-invasive prediction of biomarker statu...
Deep learning-based arterial subtraction images improve the detection of LR-TR algorithm for viable HCC on extracellular agents-enhanced MRI.
Abdominal radiology (New York)
Apr 20, 2024
PURPOSE: To determine the role of deep learning-based arterial subtraction images in viability assessment on extracellular agents-enhanced MRI using LR-TR algorithm.
Subtraction Technique
Magnetic Resonance Imaging
Image Interpretation, Computer-Assisted
Image Enhancement
Humans
Aged, 80 and over
Angiography, Digital Subtraction
Sensitivity and Specificity
Deep Learning
Algorithms
Adult
Male
Liver Neoplasms
Retrospective Studies
Middle Aged
Carcinoma, Hepatocellular
Female
Contrast Media
Aged
Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.
Academic radiology
Apr 18, 2024
RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP...
A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.
Academic radiology
Apr 17, 2024
RATIONALE AND OBJECTIVES: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes.
Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.
Medical physics
Apr 17, 2024
BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is us...
SPINNED: Simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast MRI perfusion data.
Magnetic resonance in medicine
Apr 16, 2024
PURPOSE: To propose the simulation-based physics-informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.
The value of CT radiomics combined with deep transfer learning in predicting the nature of gallbladder polypoid lesions.
Acta radiologica (Stockholm, Sweden : 1987)
Apr 16, 2024
BACKGROUND: Computed tomography (CT) radiomics combined with deep transfer learning was used to identify cholesterol and adenomatous gallbladder polyps that have not been well evaluated before surgery.
Toward Precision Diagnosis: Machine Learning in Identifying Malignant Orbital Tumors With Multiparametric 3 T MRI.
Investigative radiology
Apr 11, 2024
BACKGROUND: Orbital tumors present a diagnostic challenge due to their varied locations and histopathological differences. Although recent advancements in imaging have improved diagnosis, classification remains a challenge. The integration of artific...
Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study.
Magnetic resonance imaging
Apr 9, 2024
PURPOSE: To assess whether diffusion-weighted imaging (DWI) with Compressed SENSE (CS) and deep learning (DL-CS-DWI) can improve image quality and lesion detection in patients at risk for hepatocellular carcinoma (HCC).
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