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PT  - JOURNAL ARTICLE
AU  - Wang, Mengmeng
AU  - Ma, Yue
AU  - Li, Linna
AU  - Pan, Xingchen
AU  - Wen, Yafei
AU  - Qiu, Ying
AU  - Guo, Dandan
AU  - Zhu, Yi
AU  - Lian, Jianxiu
AU  - Tong, Dan
TI  - Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time
AID  - 10.3174/ajnr.A8161
DP  - 2024 Apr 01
TA  - American Journal of Neuroradiology
PG  - 444--452
VI  - 45
IP  - 4
4099  - http://www.ajnr.org/content/45/4/444.short
4100  - http://www.ajnr.org/content/45/4/444.full
SO  - Am. J. Neuroradiol.2024 Apr 01; 45
AB  - BACKGROUND AND PURPOSE: Accelerating the image acquisition speed of MR imaging without compromising the image quality is challenging. This study aimed to evaluate the feasibility of contrast-enhanced (CE) 3D T1WI and CE 3D-FLAIR sequences reconstructed with compressed sensitivity encoding artificial intelligence (CS-AI) for detecting brain metastases (BM) and explore the optimal acceleration factor (AF) for clinical BM imaging.MATERIALS AND METHODS: Fifty-one patients with cancer with suspected BM were included. Fifty participants underwent different customized CE 3D-T1WI or CE 3D-FLAIR sequence scans. Compressed SENSE encoding acceleration 6 (CS6), a commercially available standard sequence, was used as the reference standard. Quantitative and qualitative methods were used to evaluate image quality. The SNR and contrast-to-noise ratio (CNR) were calculated, and qualitative evaluations were independently conducted by 2 neuroradiologists. After exploring the optimal AF, sample images were obtained from 1 patient by using both optimized sequences.RESULTS: Quantitatively, the CNR of the CS-AI protocol for CE 3D-T1WI and CE 3D-FLAIR sequences was superior to that of the CS protocol under the same AF (P &amp;lt; .05). Compared with reference CS6, the CS-AI groups had higher CNR values (all P &amp;lt; .05), with the CS-AI10 scan having the highest value. The SNR of the CS-AI group was better than that of the reference for both CE 3D-T1WI and CE 3D-FLAIR sequences (all P &amp;lt; .05). Qualitatively, the CS-AI protocol produced higher image quality scores than did the CS protocol with the same AF (all P &amp;lt; .05). In contrast to the reference CS6, the CS-AI group showed good image quality scores until an AF of up to 10 (all P &amp;lt; .05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter boundaries and lesion areas (P &amp;lt; .05). Compared with the reference, CS-AI10 showed reductions in scan time of 39.25% and 39.93% for CE 3D-T1WI and CE 3D-FLAIR sequences, respectively.CONCLUSIONS: CE 3D-T1WI and CE 3D-FLAIR sequences reconstructed with CS-AI for the detection of BM may provide a more effective alternative reconstruction approach than CS. CS-AI10 is suitable for clinical applications, providing optimal image quality and a shortened scan time.AFacceleration factorBMbrain metastasesCEcontrast-enhancedCNRcontrast-to-noise ratioCScompressed SENSEAIartificial intelligenceSENSEsensitivity encoding