Surbeck Laboratory of Advanced Imaging, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA 94158-2330, USA.
Magn Reson Med. 2013 Apr;69(4):920-30. doi: 10.1002/mrm.24339. Epub 2012 Jun 12.
Two major difficulties encountered in implementing Magnetic Resonance Spectroscopic Imaging (MRSI) in a clinical setting are limited coverage and difficulty in prescription. The goal of this project was to automate completely the process of 3D PRESS MRSI prescription, including placement of the selection box, saturation bands and shim volume, while maximizing the coverage of the brain. The automated prescription technique included acquisition of an anatomical MRI image, optimization of the oblique selection box parameters, optimization of the placement of outer-volume suppression saturation bands, and loading of the calculated parameters into a customized 3D MRSI pulse sequence. To validate the technique and compare its performance with existing protocols, 3D MRSI data were acquired from six exams from three healthy volunteers. To assess the performance of the automated 3D MRSI prescription for patients with brain tumors, the data were collected from 16 exams from 8 subjects with gliomas. This technique demonstrated robust coverage of the tumor, high consistency of prescription and very good data quality within the T2 lesion.
在临床环境中实施磁共振波谱成像(MRSI)时遇到的两个主要困难是覆盖范围有限和难以处方。该项目的目标是完全自动化 3D PRESS MRSI 处方的过程,包括选择框、饱和带和匀场体积的放置,同时最大限度地覆盖大脑。自动化处方技术包括采集解剖学 MRI 图像、优化斜向选择框参数、优化外体积抑制饱和带的放置位置,并将计算出的参数加载到定制的 3D MRSI 脉冲序列中。为了验证该技术并将其性能与现有方案进行比较,从 3 名健康志愿者的 6 次检查中采集了 3D MRSI 数据。为了评估该技术在脑肿瘤患者的 3D MRSI 处方中的性能,从 8 名胶质瘤患者的 16 次检查中采集了数据。该技术在 T2 病变内显示出对肿瘤的强大覆盖范围、处方的高度一致性和非常好的数据质量。