首页|人工智能辅助压缩感知与并行采集技术在肩关节MRI中的对比研究

人工智能辅助压缩感知与并行采集技术在肩关节MRI中的对比研究

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目的通过与并行采集(parallel imaging,PI)对比,探讨人工智能辅助压缩感知(artificial intelligence-assisted compressed sensing,ACS)技术对肩关节MRI扫描时间和图像质量的影响,并优化扫描方案.材料与方法前瞻性纳入2023年11月至2024年2月在我院行肩关节MRI检查的70例患者,扫描序列采用快速自旋回波序列包括斜冠状位T1加权成像(oblique coronal T1-weighted,OCor T1WI)、斜冠状位T2加权频率选择脂肪抑制成像(oblique coronal T2-weighted with fat saturation,OCor T2WI-fs)、斜矢状位质子密度(proton density,PD)加权频率选择脂肪抑制成像(oblique sagittal PD-weighted with fat saturation,OSag PDWI-fs)、横断面PD加权频率选择脂肪抑制成像(transverse PD-weighted with fat saturation,Tra PDWI-fs),分别采用ACS和PI两种加速采集技术.比较两种技术的扫描时间.测量冈上肌肌腹和肱骨头的信号强度及背景标准差,并计算信噪比(signal-to-noise ratio,SNR).采用李克特量表对图像质量进行评分.结果相较于PI,采用ACS缩短了33.5%的扫描时间.采用ACS采集的图像伪影更少,骨骼肌肉的噪声更小,在图像质量主观评分上均高于采用PI的图像,差异均有统计学意义(P均<0.05).OCor T1WI、OCor T2WI-fs和Tra PDWI-fs序列中采用ACS的图像在冈上肌和肱骨头的SNR均高于采用PI的图像,差异均有统计学意义(P均<0.001).OSag PDWI-fs序列中图像冈上肌的SNR采用ACS与PI差异无统计学意义(P>0.05),图像肱骨头的SNR采用ACS采集的图像高于PI的图像,差异有统计学意义(P均<0.001).结论与传统的PI相比,采用ACS在肩关节MRI中可实现更高效且稳定的快速成像方案,提高图像质量,缩短扫描时间,提高患者耐受程度,具有较好的临床应用价值.
Comparative use of artificial intelligence-assisted compressed sensing and parallel imaging for shoulder magnetic resonance imaging
Objective:By comparing with parallel imaging (PI),to explore the impact of artificial intelligence-assisted compressed sensing (ACS) technology on the scanning time and image quality of shoulder joint MRI,and optimizes the scanning scheme. Materials and Methods:A total of 70 patients who underwent shoulder MRI in our hospital from November 2023 to February 2024 were prospectively enrolled. The scanning sequences used fast spin echo including oblique coronal T1-weighted (OCor T1WI),oblique coronal T2-weighted with fat saturation (OCor T2WI-fs),oblique sagittal proton density (PD)-weighted with fat saturation (OSag PDWI-fs),and transverse PD-weighted with fat saturation (Tra PDWI-fs),respectively,using two accelerated acquisition technologies:ACS and PI. Compare the scanning time of two technologies. Measure the signal intensity and background standard deviation of the supraspinatus muscle and humeral head,and calculate the signal-to-noise ratio (SNR). Use the Likert scale to rate image quality. Results:Compared to PI,using ACS reduced scanning time by 33.5%. The images obtained using ACS have few artifacts and low noise. The subjective image quality scores are higher than those obtained using PI,and the differences are statistically significant (all P<0.05). The SNR of images using ACS in OCor T1WI,OCor T2WI-fs,and Tra PDWI-fs sequences were higher than those using PI in the supraspinatus muscle and humeral head,and the differences were statistically significant (all P<0.001). The SNR of the supraspinatus muscle in the OSag PDWI-fs sequence using ACS was not significantly different from that of PI (P>0.05),while the SNR of the humeral head in the images obtained using ACS was higher than that of PI,and the difference was statistically significant (all P<0.001). Conclusions:Compared with PI,using ACS in shoulder MRI can achieve a more efficient and stable rapid imaging,improve image quality,shorten scanning time,and increase patient tolerance,which has clinical application value.

artificial intelligencecompressed sensingparallel imagingmagnetic resonance imagingshoulder

杨泽铖、詹艺、施楠楠、商爱、单飞、沈杰

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上海市(复旦大学附属)公共卫生临床中心放射科,上海 201508

人工智能 压缩感知 并行成像 磁共振成像 肩关节

上海市公共卫生临床中心院内课题

KY-GW-2024-28

2024

磁共振成像
中国医院协会 首都医科大学附属北京天坛医院

磁共振成像

CSTPCD北大核心
影响因子:1.38
ISSN:1674-8034
年,卷(期):2024.15(8)