Impact of physiological noise correction on detecting blood oxygenation level-dependent contrast in the breast.

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Wallace, TE 
Graves, MJ 
Patterson, AJ 
Gilbert, FJ 

Physiological fluctuations are expected to be a dominant source of noise in blood oxygenation level-dependent (BOLD) magnetic resonance imaging (MRI) experiments to assess tumour oxygenation and angiogenesis. This work investigates the impact of various physiological noise regressors: retrospective image correction (RETROICOR), heart rate (HR) and respiratory volume per unit time (RVT), on signal variance and the detection of BOLD contrast in the breast in response to a modulated respiratory stimulus. BOLD MRI was performed at 3 T in ten volunteers at rest and during cycles of oxygen and carbogen gas breathing. RETROICOR was optimized using F-tests to determine which cardiac and respiratory phase terms accounted for a significant amount of signal variance. A nested regression analysis was performed to assess the effect of RETROICOR, HR and RVT on the model fit residuals, temporal signal-to-noise ratio, and BOLD activation parameters. The optimized RETROICOR model accounted for the largest amount of signal variance ([Formula: see text]  =  3.3  ±  2.1%) and improved the detection of BOLD activation (P  =  0.002). Inclusion of HR and RVT regressors explained additional signal variance, but had a negative impact on activation parameter estimation (P  <  0.001). Fluctuations in HR and RVT appeared to be correlated with the stimulus and may contribute to apparent BOLD signal reactivity.

Adult, Artifacts, Breast, Female, Heart Rate, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Oxygen, Regression Analysis, Respiration, Retrospective Studies, Signal-To-Noise Ratio, Young Adult
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Phys Med Biol
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IOP Publishing
Cambridge University Hospitals NHS Foundation Trust (CUH) (unknown)
This work was supported by the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre and the CRUK-EPSRC Cancer Imaging Centre in Cambridge and Manchester (C197/A16465 and C8742/A18097).