Correction to: Deep learning reconstruction improves image quality of abdominal ultra-high-resolution
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OBJECTIVE. The purpose of this study was to increase the allowed number of acquired
slices per unit time (ie, time efficiency) for high-power deposition breath-hold abdominal …

The advantages of magnetic resonance imaging (MRI) as a medical imaging modality are
well documented, including the lack of ionizing radiation, volumetric capabilities, superior …

Purpose In an ultrahigh‐resolution CT (U‐HRCT), deep learning‐based reconstruction
(DLR) is expected to drastically reduce image noise without degrading spatial resolution …

Objective To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted
(T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality …

Purpose: To evaluate the quality of deep learning reconstruction for prospectively
accelerated intraoperative magnetic resonance imaging (iMRI) during resective brain tumor …

Respiratory motion in abdomen generates motion artifacts during Dynamic Contrast
Enhanced MRI (DCE-MRI) data acquisition and it is clinically challenging to minimize the …

Objectives Deep learning reconstruction (DLR) is a new reconstruction method; it introduces
deep convolutional neural networks into the reconstruction flow. This study was conducted …

Results: An example of a generated pattern is given in Fig. 1 with a maximum reduction
factor of R= 8 and 40 fractions with N= 347 samples each. A set of results of abdominal …

Objectives To evaluate image quality, diagnostic acceptability, and lesion conspicuity in
abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) …