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Super-resolution deep learning reconstruction approach for enhanced visualization in lumbar spine MR bone imaging.

European journal of radiology
OBJECTIVES: This study aims to assess the effectiveness of super-resolution deep-learning-based reconstruction (SR-DLR), which leverages k-space data, on the image quality of lumbar spine magnetic resonance (MR) bone imaging using a 3D multi-echo in-...

Prospective Deployment of Deep Learning Reconstruction Facilitates Highly Accelerated Upper Abdominal MRI.

Academic radiology
RATIONALE AND OBJECTIVES: To compare a conventional T1 volumetric interpolated breath-hold examination (VIBE) with SPectral Attenuated Inversion Recovery (SPAIR) fat saturation and a deep learning (DL)-reconstructed accelerated VIBE sequence with SPA...

Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.

F1000Research
BACKGROUND: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blot...

Binary classification of dead detector elements in flat panel detectors using convolutional neural networks.

Biomedical physics & engineering express
Medical physicists routinely perform quality assurance on digital detection systems, part of which involves the testing of flat panel detectors. Flat panels may degrade over time as an increasing number of individual detector elements begin to malfun...

Deep learning reconstruction for lumbar spine MRI acceleration: a prospective study.

European radiology experimental
BACKGROUND: We compared magnetic resonance imaging (MRI) turbo spin-echo images reconstructed using a deep learning technique (TSE-DL) with standard turbo spin-echo (TSE-SD) images of the lumbar spine regarding image quality and detection performance...

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

Pediatric radiology
BACKGROUND: Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice.

Super-resolution deep-learning reconstruction for cardiac CT: impact of radiation dose and focal spot size on task-based image quality.

Physical and engineering sciences in medicine
This study aimed to evaluate the impact of radiation dose and focal spot size on the image quality of super-resolution deep-learning reconstruction (SR-DLR) in comparison with iterative reconstruction (IR) and normal-resolution DLR (NR-DLR) algorithm...