Voxel-wise temporal decomposition of hypoxia-targeted BOLD MRI: method development and proof-of-concept application in glioblastoma

Abstract Hypoxia-targeted BOLD MRI is a novel technique, which probes oxygenation physiology in response to a controlled transient hypoxia stimulus. In glioblastoma, the signal response is spatially and temporally heterogeneous. We developed a voxel-wise temporal decomposition framework for hypoxia-targeted BOLD MRI that separates the arrival of responses, transition phases, and steady state during controlled isocapnic hypoxia. Twenty healthy controls underwent 3-T BOLD MRI during a double hypoxic step challenge to establish a normative reference. Three patients with newly diagnosed glioblastoma were included as proof-of-concept cases. For each voxel, we estimated response arrival delay (Delay corr ), delay to plateau, delay to return and an O 2 -normalized steady-state response (Hypoxia SS ). Healthy-control maps were used to construct a voxel-wise normative atlas and, for Hypoxia SS , a global-response-adjusted model for patient deviation mapping. In healthy controls, Hypoxia SS showed lower supratentorial between-subject variability than both whole-stimulus comparators (coefficient of variation: 1.77 versus 2.36 for Hypoxia avg ) and higher voxel-level step-to-step agreement (ICC(2,1): median 0.951 versus 0.792 for Hypoxia avg ). Whole-stimulus averaging exhibited a systematic step-2 signal amplification present in 19 of 20 subjects, which was absent from Hypoxia SS . A single global response scalar explained a median 72.5% of voxel-wise between-subject variance in Hypoxia SS . In proof-of-concept patient analyses, G-adjusted Hypoxia SS deviation maps and timing maps identified spatially coherent abnormalities that were partly complementary and extended beyond conventional MRI-defined lesion margins. Temporal decomposition improves the stability and interpretability of hypoxia-targeted BOLD MRI and provides a practical framework for population-referenced physiological mapping and atlas-based deviation mapping in glioblastoma.