From Materials to Medical Images: Translating Perovskite-Based X-Ray Detectors Toward Clinical Imaging.

Authors

Sibin Wang, Yongrui Yang, Kun Zhang, Xiaojuan Lu, Hong Yin, Yali Qiao, Xiaojia Zheng, Xiao Zhang, Yanlin Song

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e76156. Jun 18, 2026. Epub Jun 18, 2026.

Abstract

As an indispensable part of modern diagnosis and image-guided therapy, medical X-ray imaging requires detector materials that combine low-dose operation, high spatial resolution, fast response, energy-resolved capability, long-term stability, and scalable integration. Metal halide perovskites are promising candidates for high-performance and low-cost X-ray absorbers, because their strong X-ray attenuation, tunable composition, defect tolerance, favorable charge transport, and low-temperature processability enable both direct- and indirect-conversion detector designs. However, clinical translation cannot be assessed only by sensitivity or detection limit. Practical medical detectors must also satisfy requirements in modulation transfer function, noise power spectrum, detective quantum efficiency, lag/ghosting, dynamic range, pixel uniformity, readout compatibility, radiation durability, and safety. These requirements define a key gap between laboratory demonstrations and practical clinical applications. This review examines perovskite-based X-ray detectors from a materials-to-medical-images perspective. Conventional detector materials are first considered as reference platforms for defining clinical imaging requirements. Recent progress in perovskite-based X-ray detectors is then discussed with emphasis on their relevance to clinical imaging. Finally, the key barriers to clinical implementation are analyzed. By linking material properties, detector architectures, imaging metrics, and modality-specific requirements, this review outlines a translation roadmap for perovskite-based X-ray detectors toward future low-dose, high-resolution, and energy-resolved clinical imaging.

PMID:
42314053
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

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