Local structure orientation descriptor based on intra-image similarity for multimodal registration of liver ultrasound and MR images

• A local structure orientation descriptor for multimodal registration is proposed.
• Intra-image similarity is used to describe local structure in high dimensional space.
• Normalized similarity distance vector is derived to extract structure orientation.
• The method shows credible performance to register liver US and MR images.
• It is useful for improve the clinical practicability of US fusion imaging.

PurposeUltrasound (US)-magnetic resonance (MR) fusion imaging is a profitable tool for image-guided abdominal diagnosis and biopsy. However, the automatic registration of liver US and MR images remains a challenging task. An effective local structure orientation descriptor (LSOD) for use in registering multimodal images is proposed in this study.MethodsLSOD utilizes a normalized similarity distance vector of intra-image patch pairs to extract intensity change orientations from intensity value changes in a local area. The multimodal similarity measure is then derived using the LSOD vector difference. Experiments were performed on simulated US and liver 2D US–3D MR images from a phantom, two healthy volunteers, and seven patients.ResultsUsing the LSOD-based method, the root-mean-square target registration errors (RMS-TREs) were 1.76±1.90 mm/2.03±0.84 mm in phantom/clinical experiments. All of the results outperformed those obtained using modality independent neighborhood descriptor (MIND)- and linear correlation of linear combination (LC2)-based methods (phantom/clinical: 5.23±3.35 mm/4.32±3.63 mm and 9.79±5.03 mm/6.29±3.85 mm, respectively). The registration cover range for all subjects of the LSOD-based method was 9.16 mm, which was larger than those of the MIND- and LC2-based methods (5.06 and 5.12 mm, respectively).ConclusionsThe results demonstrated that the LSOD-based registration method could robustly register 2D US and 3D MR images of different liver sections with acceptable accuracy for clinical requirements. This approach is useful for the practical clinical application of the US–MR fusion imaging technique.