Acad Radiol
Department of Radiology and Biomedical Imaging, Yale School of Medicine, 300 Cedar St, New Haven, CT 06519 (N.M.H.E., H.D.T., G.G.); Department of Biomedical Engineering, Yale University, New Haven, Connecticut (H.D.T., G.G.).
Published: February 2024
Rationale And Objectives: MR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T-weighted (Tw) images, reflect not only the biologically relevant T of tissue but also hardware and acquisition parameters that vary from site to site. Quantitative T mapping avoids these confounds because it quantitatively isolates the biological parameter of interest, thus representing a universal standardization across sites. However, efforts to incorporate quantitative mapping sequences into routine clinical practice have seen slow adoption. Here we show, for the first time, that the routine Tw complex raw dataset can be successfully regarded as a quantitative mapping sequence that can be reconstructed with classical optimization methods and physics-based constraints.
Materials And Methods: While previous constrained reconstruction methods are unable to reconstruct a T map based on this data, the expanding-constrained alternating minimization for parameter mapping (e-CAMP), which employs stepwise initialization, a linearized version of the exponential model and a phase conjugacy constraint, is demonstrated to provide useful quantitative maps directly from a vendor Tw single image data.
Results: This paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired Tw images taken on both phantom and brain.
Conclusion: Because Tw scans are included in nearly every protocol, this approach could open the door to creating large, standardized datasets without requiring widespread changes in clinical protocols.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10761595 | PMC |
http://dx.doi.org/10.1016/j.acra.2023.05.036 | DOI Listing |
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