首页|Data on Artificial Intelligence Reported by Yue Ma and Colleagues (Compressed Se nsitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time)
Data on Artificial Intelligence Reported by Yue Ma and Colleagues (Compressed Se nsitivity Encoding Artificial Intelligence Accelerates Brain Metastasis Imaging by Optimizing Image Quality and Reducing Scan Time)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Changchun, People 's Republic of China, by NewsRx journalists, research stated, "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." The news correspondents obtained a quote from the research, "Fifty-one patients with cancer with suspected BM were included. Fifty participants underwent differ ent customized CE 3D-T1WI or CE 3D-FLAIR sequence scans. Compressed SENSE encodi ng acceleration 6 (CS6), a commercially available standard sequence, was used as the reference standard. Quantitative and qualitative methods were used to evalu ate image quality. The SNR and contrast-to-noise ratio (CNR) were calculated, an d qualitative evaluations were independently conducted by 2 neuroradiologists. A fter exploring the optimal AF, sample images were obtained from 1 patient by usi ng both optimized sequences. Quantitatively, the CNR of the CS-AI protocol for C E 3D-T1WI and CE 3D-FLAIR sequences was superior to that of the CS protocol unde r the same AF (<.05). Compared with reference CS6, the CS- AI groups had higher CNR values (all <.05), with the CS-AI 10 scan having the highest value. The SNR of the CS-AI group was better than tha t 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 th an did the CS protocol with the same AF (all <.05). In con trast to the reference CS6, the CS-AI group showed good image quality scores unt il an AF of up to 10 (all <.05). The CS-AI10 scan provided the optimal images, improving the delineation of normal gray-white matter bound aries 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-FLA IR sequences reconstructed with CS-AI for the detection of BM may provide a more effective alternative reconstruction approach than CS."
ChangchunPeople's Republic of ChinaA siaArtificial IntelligenceBrain Diseases and ConditionsBrain MetastasisE merging TechnologiesMachine Learning
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