A Physics-Based Algorithm to Universally Standardize Routinely Obtained Clinical T2-Weighted Images.

MR images can be challenging for machine learning and other large-scale analyses because most clinical images, for example, T 2 -weighted (T 2 w) images, reflect not only the biologically relevant T 2 of tissue but also hardware and acquisition parameters that vary from site to site. Quantitative T 2 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 T 2 w complex raw dataset can be successfully regarded as a quantitative mapping sequence that can be reconstructed with classical optimization methods and physics-based constraints. While previous constrained reconstruction methods are unable to reconstruct a T 2 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 T 2 w single image data. This paper introduces the method and demonstrates its performance using simulations, retrospectively undersampled brain images, and prospectively acquired T 2 w images taken on both phantom and brain. Because T 2 w 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. [ABSTRACT FROM AUTHOR]