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临床环境下乳腺的质子磁共振波谱分析

Proton MRS of the breast in the clinical setting.

作者信息

Mountford Carolyn, Ramadan Saadallah, Stanwell Peter, Malycha Peter

机构信息

Centre for Clinical Spectroscopy, Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.

出版信息

NMR Biomed. 2009 Jan;22(1):54-64. doi: 10.1002/nbm.1301.

DOI:10.1002/nbm.1301
PMID:19086012
Abstract

Information for determining whether a primary breast lesion is invasive and its receptor status and grade can be obtained before surgery by performing proton MRS on a fine-needle aspiration biopsy (FNAB) specimen and analyzing the MRS information by a pattern recognition method. Two-dimensional MRS, on either specimens or cells, allows the unambiguous assignment of most resonances. When correlated with the spectral regions selected by the pattern recognition method, there are strong indications for the biochemical markers responsible for prognostic information of invasive capacity and metastatic spread. Spectral assignments and biological correlations can be made using cell models. In vivo MRS can distinguish invasive from benign lesions. This pathological distinction can be made from the presence of resonances at discrete frequencies. To achieve this level of spectral resolution and signal-to-noise ratio, there are stringent requirements when acquiring and processing the data. The challenge now is to implement two-dimensional MRS in vivo. Until this is realized, the combination of in vivo MR, for diagnosis and spatial location, and MRS, for image-guided biopsy to provide information on tumor spread, promises to provide a higher level of preoperative diagnosis than previously achieved.

摘要

通过对细针穿刺活检(FNAB)标本进行质子磁共振波谱(MRS)检查,并采用模式识别方法分析MRS信息,可在手术前获取有关原发性乳腺病变是否为浸润性及其受体状态和分级的信息。对标本或细胞进行二维MRS检查,可明确识别大多数共振峰。当与模式识别方法选择的光谱区域相关联时,有强烈迹象表明某些生化标志物与浸润能力和转移扩散的预后信息有关。可使用细胞模型进行光谱识别和生物学关联分析。体内MRS能够区分浸润性病变和良性病变。这种病理区分可依据离散频率处共振峰的存在来进行。为达到这种光谱分辨率和信噪比水平,在采集和处理数据时存在严格要求。当前的挑战是在体内实现二维MRS检查。在实现这一点之前,将用于诊断和空间定位的体内磁共振成像(MR)与用于图像引导活检以提供肿瘤扩散信息的MRS相结合,有望提供比以往更高水平的术前诊断。

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