  Purpose: To develop a method for rapid sub-millimeter T1, T2, T2* and QSM
mapping in a single scan using multi-contrast Learned Acquisition and
Reconstruction Optimization (mcLARO).
  Methods: A pulse sequence was developed by interleaving inversion recovery
and T2 magnetization preparations and single-echo and multi-echo gradient echo
acquisitions, which sensitized k-space data to T1, T2, T2* and magnetic
susceptibility. The proposed mcLARO used a deep learning framework to optimize
both the multi-contrast k-space under-sampling pattern and the image
reconstruction based on image feature fusion. The proposed mcLARO method with
R=8 under-sampling was validated in a retrospective ablation study using fully
sampled data as reference and evaluated in a prospective study using separately
acquired conventionally sampled quantitative maps as reference standard.
  Results: The retrospective ablation study showed improved image sharpness of
mcLARO compared to the baseline network without multi-contrast sampling pattern
optimization or image feature fusion, and negligible bias and narrow 95% limits
of agreement on regional T1, T2, T2* and QSM values were obtained by the
under-sampled reconstructions compared to the fully sampled reconstruction. The
prospective study showed small or negligible bias and narrow 95% limits of
agreement on regional T1, T2, T2* and QSM values by mcLARO (5:39 mins) compared
to reference scans (40:03 mins in total).
  Conclusion: mcLARO enabled fast sub-millimeter T1, T2, T2* and QSM mapping in
a single scan.
