  We introduce a novel, all-in-one deep learning framework for MR image
reconstruction, enabling a single model to enhance image quality across
multiple aspects of k-space sampling and to be effective across a wide range of
clinical and technical scenarios. This DICOM-based algorithm serves as the core
of SwiftMR (AIRS Medical, Seoul, Korea), which is FDA-cleared, CE-certified,
and commercially available. We first detail the comprehensive development
process of the model, including data collection, training pair preparation,
model architecture design, and DICOM inference. We then assess the model's
capability to enhance image quality in a multi-dimensional manner, specifically
across various aspects of k-space sampling. Subsequently, we evaluate several
features of the multi-dimensional enhancement: the accuracy of tunable
denoising, the effectiveness of super-resolution in each encoding direction,
and the reduction of artifacts that become more prominent at lower spatial
resolutions. Additionally, we assess its compatibility with various scan
parameter sets and its generalizability across scanner vendors not seen during
training. Finally, we present specific cases demonstrating the model's utility
in reducing scan time across anatomical regions in conjunction with protocol
optimization. The proposed model is compatible with a broad spectrum of
scenarios, including various vendors, pulse sequences, scan parameters, and
anatomical regions. Its DICOM-based operation particularly enhances its
applicability for real-world applications. Given its demonstrated effectiveness
and versatility, we expect its use to expand in the field of clinical MRI.
