AIMC Topic: Imaging, Three-Dimensional

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Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae.

International journal of environmental research and public health
Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. ...

Multi-mode information fusion navigation system for robot-assisted vascular interventional surgery.

BMC surgery
BACKGROUND: Minimally invasive vascular intervention (MIVI) is a powerful technique for the treatment of cardiovascular diseases, such as abdominal aortic aneurysm (AAA), thoracic aortic aneurysm (TAA) and aortic dissection (AD). Navigation of tradit...

3D Ultrasound Reconstructions of the Carotid Artery and Thyroid Gland Using Artificial-Intelligence-Based Automatic Segmentation-Qualitative and Quantitative Evaluation of the Segmentation Results via Comparison with CT Angiography.

Sensors (Basel, Switzerland)
The aim of this study was to evaluate the feasibility of a noninvasive and low-operator-dependent imaging method for carotid-artery-stenosis diagnosis. A previously developed prototype for 3D ultrasound scans based on a standard ultrasound machine an...

Deep learning techniques and mathematical modeling allow 3D analysis of mitotic spindle dynamics.

The Journal of cell biology
Time-lapse microscopy movies have transformed the study of subcellular dynamics. However, manual analysis of movies can introduce bias and variability, obscuring important insights. While automation can overcome such limitations, spatial and temporal...

Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning.

The international journal of cardiovascular imaging
Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can ...

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

Nature methods
Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micromete...

Prospective intraindividual comparison of a standard 2D TSE MRI protocol for ankle imaging and a deep learning-based 2D TSE MRI protocol with a scan time reduction of 48.

La Radiologia medica
PURPOSE: Magnetic resonance imaging (MRI) scan time remains a limited and valuable resource. This study evaluates the diagnostic performance of a deep learning (DL)-based accelerated TSE study protocol compared to a standard TSE study protocol in ank...

Ultrasound Volume Reconstruction From Freehand Scans Without Tracking.

IEEE transactions on bio-medical engineering
Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volume reconstruction is often desired. Current methods for 3D reconstruction from freehand ultrasound scans require external tracking devices to provide ...

Unsupervised learning reveals interpretable latent representations for translucency perception.

PLoS computational biology
Humans constantly assess the appearance of materials to plan actions, such as stepping on icy roads without slipping. Visual inference of materials is important but challenging because a given material can appear dramatically different in various sce...

Motion compensated self supervised deep learning for highly accelerated 3D ultrashort Echo time pulmonary MRI.

Magnetic resonance in medicine
PURPOSE: To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions.