Sonification of Multimodal Medical Imaging with Temporal Dynamics for Diagnostic Augmentation

In modern medical imaging, data is highly complex and extensive, and processing by the human eye is not efficient. Radiologists face a high cognitive load and are prone to fatigue when reading multimodality studies, which may include MRI, CT and DCE-MRI. Such imaging data is rich in temporal and spatial patterns that may not be recoverable through visual observation alone. This work formulates diagnostic sonification as a cross-modal encoding problem: learning a map from a high-dimensional multimodal volumetric imaging feature space to a low-dimensional perceptual audio code that preserves diagnostically relevant structure while remaining perceptually discriminable. We state the encoding objective as a composite of diagnostic, perceptual, ambiguity and smoothness terms, and give a complete, fully determined hand-specified instantiation of the encoder — covering tissue intensity, contrast enhancement, boundary gradient, texture, spatial position, and temporal dynamics including contrast wash-in and wash-out — which serves as both an interpretable baseline and an initialization for the learned encoder. This manuscript reports the design stage: problem formulation, a survey of medical and physiological sonification, a technology-gap analysis, characterization of two public imaging datasets, the full encoder specification, and an evaluation protocol for the empirical study that follows. That study is pre-registered here: ten encoder-level experiments requiring no human listeners, six directional hypotheses with explicit falsification criteria, fixed decision rules, and a perceptual-validity gate that determines how the primary result may be framed. The learned encoder is specified but not trained, and no empirical results are reported in this manuscript.