期刊
RADIOLOGY-ARTIFICIAL INTELLIGENCE
卷 4, 期 2, 页码 -出版社
RADIOLOGICAL SOC NORTH AMERICA (RSNA)
DOI: 10.1148/ryai.210059
关键词
MR Imaging; CNS; Brain/Brain Stem; Reconstruction Algorithms
This study evaluated the application of AI-based image enhancement in clinical brain MRI and found that the AI-enhanced scans were noninferior to standard-of-care scans in terms of image quality. Quantitative analysis showed that the AI software restored the high spatial resolution of small structures. The study demonstrates that AI-based software can improve patient experience and scanner efficiency without sacrificing diagnostic quality.
Artificial intelligence (AI)-based image enhancement has the potential to reduce scan times while improving signal-to-noise ratio (SNR) and maintaining spatial resolution. This study prospectively evaluated AI-based image enhancement in 32 consecutive patients undergoing clinical brain MRI. Standard-of-care (SOC) three-dimensional (3D) T1 precontrast, 3D T2 fluid-attenuated inversion recovery, and 3D T1 post-contrast sequences were performed along with 45% faster versions of these sequences using half the number of phase-encoding steps. Images from the faster sequences were processed by a Food and Drug Administration-cleared AI-based image enhancement software for resolution enhancement. Four board-certified neuroradiologists scored the SOC and AI-enhanced image series independently on a five-point Likert scale for image SNR, anatomic conspicuity, overall image quality, imaging artifacts, and diagnostic confidence. While interrater k was low to fair, the AI-enhanced scans were noninferior for all metrics and actually demonstrated a qualitative SNR improvement. Quantitative analyses showed that the AI software restored the high spatial resolution of small structures, such as the septum pellucidum. In conclusion, AI-based software can achieve noninferior image quality for 3D brain MRI sequences with a 45% scan time reduction, potentially improving the patient experience and scanner efficiency without sacrificing diagnostic quality. (C)RSNA, 2022
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