  Accurate segmentation of thalamic nuclei, crucial for understanding their
role in healthy cognition and in pathologies, is challenging to achieve on
standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image
contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic
contrast but are not part of clinical protocols or extant databases. Here, we
introduce Histogram-based polynomial synthesis (HIPS), a fast preprocessing
step that synthesizes WMn-like image contrast from standard T1w MRI using a
polynomial approximation. HIPS was incorporated into our Thalamus Optimized
Multi-Atlas Segmentation (THOMAS) pipeline, developed and optimized for WMn
MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based
segmentation method and THOMAS modified for T1w images (T1w-THOMAS). The
robustness and accuracy of the three methods were tested across different image
contrasts, scanner manufacturers, and field strength. HIPS-synthesized images
improved intra-thalamic contrast and thalamic boundaries, and their
segmentations yielded significantly better mean Dice, lower percentage of
volume error, and lower standard deviations compared to both the CNN method and
T1w-THOMAS. Finally, using THOMAS, HIPS-synthesized images were as effective as
WMn images for identifying thalamic nuclei atrophy in alcohol use disorders
subjects relative to healthy controls, with a higher area under the ROC curve
compared to T1w-THOMAS (0.79 vs 0.73).
