Sensitive imaging biomarkers are urgently needed for screening of high-risk subjects, determine
early disease progression, and assess response to therapies in neurodegenerative disorders.
The atrophy of several brain regions is an established biomarker in AD, which strongly
correlates with AD neuropathology. The accuracy of subfield volumes and cortical thickness
estimated from current MRI methods is limited because of the vulnerability to motion, low
spatial resolution, low contrast between brain sub-structures, and dependence of current
segmentation frameworks on image quality. Short motion-compensated MRI protocols to map
the human brain at high spatial resolution with multiple contrasts, along with accurate and
computationally efficient segmentation algorithms, are urgently needed tor early detection and
management of subjects with neurodegenerative disorders.
We propose to introduce a 15-minute motion-robust 3-D acquisition and reconstruction
scheme to recover whole-brain MRI data with 0.2 mm isotropic resolution with several
different inversion times on 7T, along with segmentation algorithms that are robust to
acceleration. The key difference of this framework from current approaches, which rely on MRI
data 1 mm resolution, is the quite significant increase in spatial resolution to 0.2 mm as well as
the availability of multiple conteasts. This improvement is enabled by innovations in all areas of
the data-processing pipeline, including acquisition, reconstruction, and analysis. These
innovations are facilitated and integrated by the model based deep learning framework
(MoDL); this framework facilitates the joint exploitation the available prior information,
including motion and models for magnetization evolution, with convolutional neural network
blocks that learn anatomical information from exemplar data. The successful completion of this
framework will yield sensitive biomarkers, which will be considerably less expensive than PET
and does not involve radiation exposure. As 7T clinical scanners become more common, this
framework can emerge as a screening tool for high-risk subjects (e.g. APOE, PSEN mutations)
and assess progression in patients with short follow-up duration.