AIMC Topic: Deep Learning

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Boosting Cardiac Color Doppler Frame Rates With Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Color Doppler echocardiography enables visualization of blood flow within the heart. However, the limited frame rate impedes the quantitative assessment of blood velocity throughout the cardiac cycle, thereby compromising a comprehensive analysis of ...

BEAS-Net: A Shape-Prior-Based Deep Convolutional Neural Network for Robust Left Ventricular Segmentation in 2-D Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Left ventricle (LV) segmentation of 2-D echocardiography images is an essential step in the analysis of cardiac morphology and function and-more generally-diagnosis of cardiovascular diseases (CVD). Several deep learning (DL) algorithms have recently...

Hardware-Independent Deep Signal Processing: A Feasibility Study in Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning (DL) models have emerged as alternative methods to conventional ultrasound (US) signal processing, offering the potential to mimic signal processing chains, reduce inference time, and enable the portability of processing chains across h...

Spatiotemporal Deep Learning-Based Cine Loop Quality Filter for Handheld Point-of-Care Echocardiography.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
The reliability of automated image interpretation of point-of-care (POC) echocardiography scans depends on the quality of the acquired ultrasound data. This work reports on the development and validation of spatiotemporal deep learning models to asse...

Automatic Segmentation of Abdominal Aortic Aneurysms From Time-Resolved 3-D Ultrasound Images Using Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Abdominal aortic aneurysms (AAAs) are rupture-prone dilatations of the aorta. In current clinical practice, the maximal diameter of AAAs is monitored with 2-D ultrasound to estimate their rupture risk. Recent studies have shown that 3-D and mechanica...

Deep-Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images Using Multiscale and Higher-Order Processing.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Autonomous ultrasound image quality assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultras...

Automatic 3-D Lamina Curve Extraction From Freehand 3-D Ultrasound Data Using Sequential Localization Recurrent Convolutional Networks.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Freehand 3-D ultrasound imaging is emerging as a promising modality for regular spine exams due to its noninvasiveness and affordability. The laminae landmarks play a critical role in depicting the 3-D shape of the spine. However, the extraction of t...

AI-Assisted Post Contrast Brain MRI: Eighty Percent Reduction in Contrast Dose.

Academic radiology
OBJECTIVES: In the context of growing safety concerns regarding the use of gadolinium-based contrast agents in contrast-enhanced MRI, there is a need for dose reduction without compromising diagnostic accuracy. A deep learning (DL) method is proposed...

Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI.

Medical image analysis
In medical image analysis, the utilization of biophysical models for signal analysis offers valuable insights into the underlying tissue types and microstructural processes. In diffusion-weighted magnetic resonance imaging (DWI), a major challenge li...

Evaluating the reproducibility of a deep learning algorithm for the prediction of retinal age.

GeroScience
Recently, a deep learning algorithm (DLA) has been developed to predict the chronological age from retinal images. The Retinal Age Gap (RAG), a deviation between predicted age from retinal images (Retinal Age, RA) and chronological age, correlates wi...