Work

MBFormer: Microbubble Transformer for 3D Time-Series Data Processing to Improve Bound Bubble Detection in Nonde-structive Ultrasound Molecular Imaging

Year: 2026

Type: preprint

Abstract: animal model of breast cancer, with comparisons made against a prior convolutional neural network (CNN)-based UMI method and SegFormer3D, a representative 3D transformer backbone. The results indicate that MBFormer (AUC = 0.943) outperformed both CNN (AUC = 0.897) and SegFormer3D (AUC = 0.766) in detecting bound MBs. The CNN exhibited residual molecular signal from free-floating MBs in the cardiac chambers, whereas SegFormer3D baseline failed to detect fine MB textures effectively. Overall, MBFormer demonstrated enhanced detection of bound MBs while suppressing free-floating MB signals. Furthermore, MBFormer achieved a frame rate of 16.7 to 18.1 frames per second, demonstrating its potential for real-time application. We anticipate that this improved transformer-based UMI model can facilitate real-time, free-hand nondestructive UMI in clinical systems.

Source: PubMed Central

DOI: 10.48550/arxiv.2609.30618