  In neuroimaging, MRI tissue properties characterize underlying neurobiology,
provide quantitative biomarkers for neurological disease detection and
analysis, and can be used to synthesize arbitrary MRI contrasts. Estimating
tissue properties from a single scan session using a protocol available on all
clinical scanners promises to reduce scan time and cost, enable quantitative
analysis in routine clinical scans and provide scan-independent biomarkers of
disease. However, existing tissue properties estimation methods - most often
$\mathbf{T_1}$ relaxation, $\mathbf{T_2^*}$ relaxation, and proton density
($\mathbf{PD}$) - require data from multiple scan sessions and cannot estimate
all properties from a single clinically available MRI protocol such as the
multiecho MRI scan. In addition, the widespread use of non-standard acquisition
parameters across clinical imaging sites require estimation methods that can
generalize across varying scanner parameters. However, existing learning
methods are acquisition protocol specific and cannot estimate from heterogenous
clinical data from different imaging sites. In this work we propose an
unsupervised deep-learning strategy that employs MRI physics to estimate all
three tissue properties from a single multiecho MRI scan session, and
generalizes across varying acquisition parameters. The proposed strategy
optimizes accurate synthesis of new MRI contrasts from estimated latent tissue
properties, enabling unsupervised training, we also employ random acquisition
parameters during training to achieve acquisition generalization. We provide
the first demonstration of estimating all tissue properties from a single
multiecho scan session. We demonstrate improved accuracy and generalizability
for tissue property estimation and MRI synthesis.
