Compressed Sensitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time

• Published in: American journal of neuroradiology : AJNR Vol. 45; no. 4; pp. 444 - 452
• Main Authors: Wang, Mengmeng, Ma, Yue, Li, Linna, Pan, Xingchen, Wen, Yafei, Qiu, Ying, Guo, Dandan, Zhu, Yi, Lian, Jianxiu, Tong, Dan
• Format: Journal Article
• Language: English
• Published: United States 08.04.2024
• Subjects: Artificial Intelligence; Brain Neoplasms - diagnostic imaging; Gray Matter; Humans; Imaging, Three-Dimensional; Magnetic Resonance Imaging - methods; White Matter
• ISSN: 0195-6108; 1936-959X; 1936-959X
• DOI: 10.3174/ajnr.A8161

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. 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. 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 ( < .05). Compared with reference CS6, the CS-AI groups had higher CNR values (all < .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 < .05). Qualitatively, the CS-AI protocol produced higher image quality scores than did the CS protocol with the same AF (all < .05). In contrast to the reference CS6, the CS-AI group showed good image quality scores until an AF of up to 10 (all < .05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter boundaries and lesion areas ( < .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. 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.