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Optimal Phased-Array Signal Combination For Polyunsaturated Fatty Acids Measurement In Breast Cancer Using Multiple Quantum Coherence MR Spectroscopy At 3T

机译:最佳相控阵信号组合用于多不饱和脂肪酸在乳腺癌中的多重量子相干MR光谱在3T测量

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摘要

Polyunsaturated fatty acid (PUFA), a key marker in breast cancer, is non-invasively quantifiable using multiple quantum coherence (MQC) magnetic resonance spectroscopy (MRS) at the expense of losing half of the signal. Signal combination for phased array coils provides potential pathways to enhance the signal to noise ratio (SNR), with current algorithms developed for conventional brain MRS. Since PUFA spectra and the biochemical environment in the breast deviate significantly from those in the brain, we set out to identify the optimal algorithm for PUFA in breast cancer. Combination algorithms were compared using PUFA spectra from 17 human breast tumour specimens, 15 healthy female volunteers, and 5 patients with breast cancer on a clinical 3 T MRI scanner. Adaptively Optimised Combination (AOC) yielded the maximum SNR improvement in specimens (median, 39.5%; interquartile range: 35.5–53.2%, p < 0.05), volunteers (82.4 ± 37.4%, p < 0.001), and patients (median, 61%; range: 34–105%, p < 0.05), while independent from voxel volume (rho = 0.125, p = 0.632), PUFA content (rho = 0.256, p = 0.320) or water/fat ratio (rho = 0.353, p = 0.165). Using AOC, acquisition in patients is 1.5 times faster compared to non-noise decorrelated algorithms. Therefore, AOC is the most suitable current algorithm to improve SNR or accelerate the acquisition of PUFA MRS from breast in a clinical setting.
机译:多不饱和脂肪酸(PUFA)是乳腺癌的关键标志,它可以使用多量子相干(MQC)磁共振波谱(MRS)进行非侵入式定量分析,但会损失一半的信号。相控阵线圈的信号组合提供了潜在的途径,可以利用为常规脑部MRS开发的当前算法来增强信噪比(SNR)。由于乳腺中的PUFA光谱和生化环境与大脑中的显着不同,因此我们着手确定乳腺癌中PUFA的最佳算法。在临床3T MRI扫描仪上使用PUFA光谱从17个人类乳腺肿瘤标本,15个健康女性志愿者和5个乳腺癌患者中比较了组合算法。自适应优化组合(AOC)在标本(中位数,39.5%;四分位间距:35.5–53.2%,p <0.05),志愿者(82.4%±37.4%,p <0.001)和患者(中位数61)中产生了最大的SNR改善%;范围:34–105%,p <0.05),而与体素体积(rho = 0.125,p = 0.632),PUFA含量(rho = 0.256,p = 0.320)或水/脂肪比(rho = 0.353, p = 0.165)。与无噪声去相关算法相比,使用AOC可以使患者的采集速度提高1.5倍。因此,在临床环境中,AOC是目前最合适的算法,可以提高SNR或加速从乳房获取PUFA MRS。

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