FIGURE 10.

Examples of the estimated T₁ from UTE VFA lung data (SNR = 8.7) derived using standard non-linear least-squares fitting (NNLS)²¹⁵ with five FAs, the widely used maximum likelihood variable projection method (VPM)²¹⁶ with three FAs, and a self-supervised reference-free deep learning method (Relax-MANTIS) with three FAs. The T₁ values obtained with Relax-MANTIS showed similar regional variations as seen with NNLS with five FAs and less noisy measurements (white arrows) seen from VPM with three FAs. The lung T₁ histogram comparison suggests that whole lung T₁ distribution estimated using the deep learning method Relax-MANTIS with three FAs (orange curve) conforms much better to the distribution from the NNLS with five FAs (bar graph in blue). Relax-MANTIS provides good quantitative agreement with five-FA standard NNLS while using only three FAs, indicating a 40% scan time reduction for an accurate whole lung parenchymal T₁ quantification. The computing time for each 3D lung volume is significantly lower for the deep learning method at 26 s in comparison with the conventional methods at several minutes to hours. (Image courtesy of Wei Zha, PhD)