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Magn Reson Med ; 83(3): 920-934, 2020 03.
Article in English | MEDLINE | ID: mdl-31532006

ABSTRACT

PURPOSE: The application of amide proton transfer (APT) CEST MRI for diagnosis of breast cancer is of emerging interest. However, APT imaging in the human breast is affected by the ubiquitous fat signal preventing a straightforward application of existing acquisition protocols. Although the spectral region of the APT signal does not coincide with fat resonances, the fat signal leads to an incorrect normalization of the Z-spectrum, and therefore to distorted APT effects. In this study, we propose a novel normalization for APT-CEST MRI that corrects for fat signal-induced artifacts in the postprocessing without the need for application of fat saturation schemes or water-fat separation approaches. METHODS: The novel normalization uses the residual signal at the spectral position of the direct water saturation to estimate the fat contribution. A comprehensive theoretical description of the normalization for an arbitrary phase relation of the water and fat signal is provided. Functionality and applicability of the proposed normalization was demonstrated by in vitro and in vivo experiments. RESULTS: In vitro, an underestimation of the conventional APT contrast of approximately -1.2% per 1% fat fraction was observed. The novel normalization yielded an APT contrast independent of the fat contribution, which was also independent of the water-fat phase relation. This allowed APT imaging in patients with mamma carcinoma corrected for fat signal contribution, field inhomogeneities, spillover dilution, and water relaxation effects. CONCLUSION: The proposed normalization increases the specificity of APT imaging in tissues with varying fat content and represents a time-efficient and specific absorption rate-efficient alternative to fat saturation and water-fat separation approaches.


Subject(s)
Adipose Tissue/diagnostic imaging , Breast Neoplasms/diagnostic imaging , Magnetic Resonance Imaging , Adipose Tissue/pathology , Adult , Algorithms , Artifacts , Body Mass Index , Female , Healthy Volunteers , Humans , Hydrogen-Ion Concentration , Image Processing, Computer-Assisted , In Vitro Techniques , Middle Aged , Normal Distribution , Sunflower Oil , Temperature
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