Faculty Bibliography

PURPOSE/OBJECTIVE:To develop a deep learning-based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue. METHODS:A physics-based unsupervised deep learning network for estimation of fatty acid composition-network (FAC-Net) is proposed to estimate the number of double bonds and number of methylene-interrupted double bonds from multi-echo bipolar gradient-echo data, which are subsequently converted to saturated, mono-unsaturated, and poly-unsaturated fatty acids. The loss function was based on a 10 fat peak signal model. The proposed network was tested with a phantom containing eight oils with different FAC and on post-menopausal women scanned using a whole-body 3T MRI system between February 2022 and January 2024. The post-menopausal women included a control group (n = 8) with average risk for breast cancer and a cancer group (n = 7) with biopsy-proven breast cancer. RESULTS: > 0.9 except chain length). The FAC values measured from scan and rescan data of the control group showed no significant difference between the two scans. The FAC measurements of the cancer group conducted before contrast and after contrast showed a significant difference in saturated fatty acid and mono-unsaturated fatty acid. The cancer group has higher saturated fatty acid than the control group, although not statistically significant. CONCLUSION/CONCLUSIONS:The results in this study suggest that the proposed FAC-Net can be used to measure the FAC of mammary adipose tissue from gradient-echo MRI data of the breast.

Despite growing awareness of problems with fairness in artificial intelligence (AI) models in radiology, evaluation of algorithmic biases, or AI biases, remains challenging due to various complexities. These include incomplete reporting of demographic information in medical imaging datasets, variability in definitions of demographic categories, and inconsistent statistical definitions of bias. To guide the appropriate evaluation of AI biases in radiology, this article summarizes the pitfalls in the evaluation and measurement of algorithmic biases. These pitfalls span the spectrum from the technical (eg, how different statistical definitions of bias impact conclusions about whether an AI model is biased) to those associated with social context (eg, how different conventions of race and ethnicity impact identification or masking of biases). Actionable best practices and future directions to avoid these pitfalls are summarized across three key areas: (a) medical imaging datasets, (b) demographic definitions, and (c) statistical evaluations of bias. Although AI bias in radiology has been broadly reviewed in the recent literature, this article focuses specifically on underrecognized potential pitfalls related to the three key areas. By providing awareness of these pitfalls along with actionable practices to avoid them, exciting AI technologies can be used in radiology for the good of all people.

The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI with case-level labels, and its public availability aims to advance fast and quantitative machine learning research.

INTRODUCTION/UNASSIGNED:The intravoxel incoherent motion (IVIM) model of diffusion weighted imaging (DWI) provides imaging biomarkers for breast tumor characterization. It has been extensively applied for both diagnostic and prognostic goals in breast cancer, with increasing evidence supporting its clinical relevance. However, variable performance exists in literature owing to the heterogeneity in datasets and quantification methods. METHODS/UNASSIGNED: RESULTS/UNASSIGNED: DISCUSSION/UNASSIGNED:

PURPOSE/OBJECTIVE:To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. METHODS: RESULTS: CONCLUSION/CONCLUSIONS:We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.