ArticleMagnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine2025
Improving Vessel Visibility and Applying Artificial Intelligence to Autodetect Brain Metastasis for a 3D MR Imaging Sequence Capable of Simultaneous Images with and without Blood Vessel Suppression.
Article in Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe purposes of this study were 1) to improve vessel visibility of our MR sequence by modifying k-space filling and 2) to verify the usefulness of applying artificial intelligence (AI) for volume isotropic simultaneous interleaved bright- and black-blood examination (VISIBLE) with compressed sensitivity encoding (CS) in autodetecting brain metastases.
methodsWe modified 3 sequences of VISIBLE (Centric, Reversed Centric, and Startup Echo 30). The Centric sequence is a prototype. The Reversed Centric filled the k-space in a reversed centric manner to improve vessel visibility. The Startup Echo 30 implemented dummy echoes to further improve vessel visibility. Vessel visibility was evaluated in one slice at the level of the centrum semiovale. The sensitivity, specificity, the area under the curve (AUC), and false positives of detecting brain metastases using AI were evaluated among 3 sequences. Statistical comparisons were performed using a one-way analysis of variance, followed by Friedman and Dunn's multiple comparison tests.
resultsThe number of visualized vessels was significantly lower in the Centric (39.3 ± 9.7, P < 0.05) and Reversed Centric (44.2 ± 9.8, P < 0.05) methods than in the magnetization-prepared rapid gradient echo (49.3 ± 9.1) but comparable in the Startup Echo 30 method (44.9 ± 8.8, P > 0.05). No significant differences existed in sensitivity, specificity, and AUC among the 3 methods. False positives achieved using the Reversed Centric method were significantly fewer (54 false positives) than those achieved using the Centric (85 false positives) and Startup Echo 30 (68 false positives) methods (P = 0.0092).
conclusionVessel visibility was improved by modifying the k-space filling, which may reduce false positives. The AI model for VISIBLE with CS achieved good performance in autodetection of brain metastases. The AI model for VISIBLE with CS can help radiologists diagnose brain metastases in clinical practice.
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