Chemical Exchange Saturation Transfer (CEST) imaging can generate contrast that is sensitive to amide protons associated with proteins and peptides (termed amide proton transfer, APT). In breast cancer, APT contrast may report on underlying changes in microstructural tissue composition. However, to date, there have been no developments or applications of APT CEST to breast cancer. As a result, the aims of this study were to i) experimentally explore optimal scan parameters for breast CEST near the amide resonance at 3 T, ii) establish the reliability of APT imaging of healthy fibroglandular tissue, and iii) demonstrate preliminary results on APT changes in locally advanced breast cancer observed during the course of neoadjuvant chemotherapy. CEST measurements were experimentally optimized on cross-linked bovine serum albumin phantoms and the reliability of APT imaging was assessed in ten women with no history of breast disease. The mean difference between test-retest APT values was not significantly different from zero, and the individual difference values were not dependent upon the average APT value. The 95% confidence interval limits were ± 0.70% (α = 0.05) and the repeatability was 1.91. APT measurements were also performed in three women before and after one cycle of chemotherapy. Following therapy, APT increased in the one patient with progressive disease and decreased in the two patients with a partial or complete response. Together, these results suggest that APT imaging may report on treatment response in these patients.

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Image synthesis learns a transformation from the intensity features of an input image to yield a different tissue contrast of the output image. This process has been shown to have application in many medical image analysis tasks including imputation, registration, and segmentation. To carry out synthesis, the intensities of the input images are typically scaled—i.e., normalized—both in training to learn the transformation and in testing when applying the transformation, but it is not presently known what type of input scaling is optimal. In this paper, we consider seven different intensity normalization algorithms and three different synthesis methods to evaluate the impact of normalization. Our experiments demonstrate that intensity normalization as a preprocessing step improves the synthesis results across all investigated synthesis algorithms. Furthermore, we show evidence that suggests intensity normalization is vital for successful deep learning-based MR image synthesis.

BACKGROUND AND PURPOSE:Cortical lesions are common and often extensive in multiple sclerosis but are difficult to visualize by MRI, leaving important questions about their clinical implications and response to therapy unanswered. Our aim was to determine whether cortical lesions are better visualized using magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) than T2*-weighted imaging on 7T MR imaging.