ReviewQuantitative imaging in medicine and surgery2025
Blind spots in brain imaging: a pictorial essay.
Review in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Early Clues, critical outcomes: a pictorial review of subtle intracranial pathologies on non-contrast head CT.Emergency radiology · 2025Review
- Comparative validation of automated perfusion analysis software for ischemic penumbra estimation and EVT decision-making.Frontiers in neuroscience · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, radiologists must interpret large quantities of images and identify diseases on a daily basis. The minimization of errors is crucial for high-quality diagnostic imaging and optimal patient care. Brain imaging is frequently used in clinical practice; however, radiologists are prone to overlook some regions in brain imaging and make perceptual errors, thus leading to missed diagnoses. These regions, also known as "blind spots", comprise a number of intricate areas, including the posterior fossa, cerebral sulci and pia mater, cranial nerves (CNs), intracranial arteries, dural sinuses, sella and parasellar region, Meckel's cave, skull base, scalp, orbit, and pterygopalatine fossa (PPF). Therefore, the knowledge of normal computed tomography (CT) and magnetic resonance imaging (MRI) manifestations and common lesions in these blind spots is imperative to avoid false-negative results. This article graphically discusses and analyzes these common blind spots of brain imaging using real representative cases. It also provides comprehensive strategies to address missed diagnostic errors in radiology, including enhancing the selection of imaging protocols, implementing a multi-reviewer reporting system, adopting structured reporting templates, employing error measurement or detection strategies, and promoting the use, development, and refinement of artificial intelligence (AI) to improve diagnostic accuracy and efficiency. This article may also increase junior doctors' awareness of these blind spots and assist them in their daily work, and thus has continuing education implications.
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Registered trials
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