Publications by authors named "Linda Moy"
The fastMRI breast dataset is the first large-scale dataset of radial k-space and DICOM data for breast dynamic contrast-enhanced MRI with case-level labels. Its public availability aims to advance fast and quantitative machine learning research. ©RSNA, 2025.
View Article and Find Full Text PDFPurpose: 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.
Deep learning techniques hold immense promise for advancing medical image analysis, particularly in tasks like image segmentation, where precise annotation of regions or volumes of interest within medical images is crucial but manually laborious and prone to interobserver and intraobserver biases. As such, deep learning approaches could provide automated solutions for such applications. However, the potential of these techniques is often undermined by challenges in reproducibility and generalizability, which are key barriers to their clinical adoption.
View Article and Find Full Text PDFACR Appropriateness Criteria® Female Breast Cancer Screening: 2023 Update.
J Am Coll Radiol
June 2024
Early detection of breast cancer from regular screening substantially reduces breast cancer mortality and morbidity. Multiple different imaging modalities may be used to screen for breast cancer. Screening recommendations differ based on an individual's risk of developing breast cancer.
View Article and Find Full Text PDFArticle Synopsis
- - The authors introduce a new guideline called the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) to keep up with the fast changes in AI technology in healthcare.
- - The 2024 Update emphasizes the necessary standards and recommendations for integrating AI tools into medical imaging practices effectively.
- - This initiative aims to ensure that AI applications are safe, reliable, and beneficial for improving patient care in medical imaging.
Article Synopsis
- A digital reference object (DRO) toolkit has been created to simulate realistic breast DCE-MRI data, aiding in the quantitative evaluation of image reconstruction techniques and data analysis methods.
- The toolkit utilizes data from a study involving 53 women with malignant and benign breast lesions, segments anatomical areas, and performs pharmacokinetic analysis to establish parameter ranges.
- Findings indicate that the new toolkit aligns with existing pharmacokinetic data, revealing significant underestimations in reconstruction methods and highlighting the impact of field inhomogeneity on parameter accuracy.
Article Synopsis
- The study focuses on enhancing the use of deep learning (DL) in medical imaging by creating a comprehensive checklist to improve reproducibility and reliability in research.
- Researchers developed this checklist using the Delphi method, which involved rounds of surveys among 11 experts to refine items and achieve consensus on their importance.
- The final checklist consists of 26 essential items that promote transparent reporting of DL applications in medicine, facilitating better understanding and replication of studies.
According to the World Health Organization, climate change is the single biggest health threat facing humanity. The global health care system, including medical imaging, must manage the health effects of climate change while at the same time addressing the large amount of greenhouse gas (GHG) emissions generated in the delivery of care. Data centers and computational efforts are increasingly large contributors to GHG emissions in radiology.
View Article and Find Full Text PDFPurpose: There are insufficient large-scale studies comparing the performance of screening mammography in women of different races. This study aims to compare the screening performance metrics across racial and age groups in the National Mammography Database (NMD).
Methods: All screening mammograms performed between January 1, 2008, and December 31, 2021, in women aged 30-100 years from 746 mammography facilities in 46 U.
Purpose: The impact of opportunistic screening mammography in the United States is difficult to quantify, partially due to lack of inclusion regarding method of detection (MOD) in national registries. This study sought to determine the feasibility of MOD collection in a multicenter community registry and to compare outcomes and characteristics of breast cancer based on MOD.
Methods: We conducted a retrospective study of breast cancer patients from a multicenter tumor registry in Missouri from January 2004 - December 2018.