AIMC Topic: Imaging, Three-Dimensional

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A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics.

STAR protocols
Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capa...

Application of deep learning-based super-resolution to T1-weighted postcontrast gradient echo imaging of the chest.

La Radiologia medica
OBJECTIVES: A deep learning-based super-resolution for postcontrast volume-interpolated breath-hold examination (VIBE) of the chest was investigated in this study. Aim was to improve image quality, noise, artifacts and diagnostic confidence without c...

Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction.

Skeletal radiology
BACKGROUND AND PURPOSE: Three-dimensional (3D) imaging of the spine, augmented with AI-enabled image enhancement and denoising, has the potential to reduce imaging times without compromising image quality or diagnostic performance. This work evaluate...

Deep-Learning-Based Contrast Synthesis From MRF Parameter Maps in the Knee Joint.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Magnetic resonance fingerprinting (MRF) is a method to speed up acquisition of quantitative MRI data. However, MRF does not usually produce contrast-weighted images that are required by radiologists, limiting reachable total scan time imp...

Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study.

EBioMedicine
BACKGROUND: Anthropomorphic phantoms are used in surgical planning and intervention. Ideal accuracy and high efficiency are prerequisites for its clinical application. We aimed to develop a fully automated artificial intelligence-based three-dimensio...

Development of Gestational Age-Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gest...

BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.

NPJ biofilms and microbiomes
Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for observing individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-base...

Fast Near-Field Frequency-Diverse Computational Imaging Based on End-to-End Deep-Learning Network.

Sensors (Basel, Switzerland)
The ability to sculpt complex reference waves and probe diverse radiation field patterns have facilitated the rise of metasurface antennas, while there is still a compromise between the required wide operation band and the non-overlapping characteris...