Imaging-Based Diagnostic Approaches in Moyamoya Disease: A Scoping Review

TL;DR

This review summarizes various imaging techniques used to diagnose and manage Moyamoya disease, highlighting the need for a combined approach and better standardization.

Contribution

The paper provides a systematic mapping and critical appraisal of imaging-based diagnostic approaches for Moyamoya disease, emphasizing multimodal strategies.

Findings

Digital subtraction angiography is the diagnostic reference standard for Moyamoya disease.

Noninvasive imaging modalities provide complementary information for diagnosis and management.

Artificial intelligence applications show promise but require further validation.

Abstract

Moyamoya disease (MMD) is a chronic, progressive cerebrovascular disorder characterized by steno-occlusive changes in the intracranial internal carotid arteries and the development of fragile collateral networks. Imaging plays a pivotal role in diagnosis, disease staging, and management, yet the expanding range of available imaging modalities has resulted in heterogeneous evidence that remains difficult to synthesize. This scoping review aimed to systematically map and critically appraise imaging-based diagnostic approaches used in MMD, summarizing their diagnostic performance, clinical utility, and limitations. A comprehensive literature search was conducted across major databases, and original studies evaluating imaging modalities in human MMD were included. Thirty-three studies published between 1995 and 2023 were analyzed, encompassing digital subtraction angiography, magnetic resonance imaging and angiography, perfusion and functional MRI, computed tomography-based techniques, nuclear medicine, ultrasound, neurophysiological methods, and emerging artificial intelligence applications. Digital subtraction angiography remains the diagnostic reference standard, particularly for disease confirmation and surgical planning. However, noninvasive modalities provide critical complementary information. Magnetic resonance-based techniques offer multiparametric assessment of vascular morphology, hemodynamics, vessel wall pathology, and parenchymal injury. Computed tomography angiography and perfusion imaging provide accessible alternatives with high sensitivity for vascular changes, while functional and neurophysiological methods contribute additional hemodynamic and regional assessments. Artificial intelligence applications show promising diagnostic performance but remain in early validation stages. The evidence base is limited by methodological heterogeneity, inconsistent reference standards, incomplete reporting of diagnostic accuracy metrics, and a scarcity of longitudinal and multimodal studies. Collectively, the findings support a multimodal imaging strategy in MMD, integrating structural and functional information to inform diagnosis and management. Future research should prioritize standardized protocols, longitudinal designs, and clinically validated imaging biomarkers to enable evidence-based diagnostic pathways.

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