Diagnostic value of automated 3D ultrasound for incisional hernia

Comparison of the Effectiveness of Ultrasound Imaging and Perioperative Measurement in the Diagnosis and Characterization of Incisional Hernia

Background

Incisional hernia (IH) is a common complication after abdominal surgery, and there is no gold standard imaging modality for its diagnosis. Although computed tomography is frequently used in clinical practice, it has limitations such as radiation exposure and relatively high cost. The aim of this study is to establish standardization and hernia typing by comparing preoperative ultrasound (US) measurements and perioperative measurements in IH cases.

Methods

The patients who were operated for IH in our institution between January 2020 and March 2021 were reviewed, retrospectively. In result, 120 patients were included in the study, and the cases had preoperative US images and perioperative hernia measurements. IH was divided into three subtypes as omentum (Type I), intestinal (Type II), and mixed (Type III) according to the defect content.

Results

Type I IH was detected in 91 cases, Type II IH in 14 cases, and Type III IH in 15 cases. When the diameters of IH types were compared for preoperative US and perioperative measurements, respectively, there was no statistical significance (P = 0.185 and P = 0.262). According to Spearman correlation, there was a positive very strong correlation between preoperative US measurements and perioperative measurements (ρ = 0.861 and P < 0.001).

Conclusion

As stated by our results, US imaging can be performed easily and quickly, providing a reliable way to accurately detect and characterize an IH. It can also facilitate the planning of surgical intervention in IH by providing anatomical information.

Pore texture analysis in automated 3D breast ultrasound images for implanted lightweight hernia mesh identification: a preliminary study

Background

Precise visualization of meshes and their position would greatly aid in mesh shrinkage evaluation, hernia recurrence risk assessment, and the preoperative planning of salvage repair. Lightweight (LW) meshes are able to preserve abdominal wall compliance by generating less post-implantation fibrosis and rigidity. However, conventional 3D imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) cannot visualize the LW meshes. Patients sometimes have to undergo a second-look operation for visualizing the mesh implants. The goal of this work is to investigate the potential advantages of Automated 3D breast ultrasound (ABUS) pore texture analysis for implanted LW hernia mesh identification.

Methods

In vitro, the appearances of four different flat meshes in both ABUS and 2D hand-held ultrasound (HHUS) images were evaluated and compared. In vivo, pore texture patterns of 87 hernia regions were analyzed both in ABUS images and their corresponding HHUS images.

Results

In vitro studies, the imaging results of ABUS for implanted LW meshes are much more visualized and effective in comparison to HHUS. In vivo, the inter-class distance of 40 texture features was calculated. The texture features of 2D sectional plans (axial and sagittal plane) have no significant contribution to implanted LW mesh identification. Significant contribution was observed in coronal plane. However, since the mesh may have spatial variation such as shrinkage after implantation surgery, the inter-class distance of 3D coronal plane pore texture features are bigger than 2D coronal plane, so the contribution of 3D coronal plane pore texture features are more valuable than 2D coronal plane for implanted LW mesh identification. The use of 3D pore texture features significantly improved the robustness of the identification method in distinguishing between LW mesh and fascia.

Conclusions

An innovative new ABUS provides additional pore texture visualization, by separating the LW mesh from the fascia tissues. Therefore, ABUS has the potential to provides more accurate features to characterize pore texture patterns, and ultimately provide more accurate measures for implanted LW mesh identification.

Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images

A novel 3-D filtering method is presented for speckle reduction and detail preservation in automated 3-D ultrasound images. First, texture features of an image are analyzed by using the improved quadtree (QT) decomposition. Then, the optimal homogeneous and the obvious heterogeneous regions are selected from QT decomposition results. Finally, diffusion parameters and diffusion process are automatically decided based on the properties of these two selected regions. The computing time needed for 2-D speckle reduction is very short. However, the computing time required for 3-D speckle reduction is often hundreds of times longer than 2-D speckle reduction. This may limit its potential application in practice. Because this new filter can adaptively adjust the time step of iteration, the computation time is reduced effectively. Both synthetic and real 3-D ultrasound images are used to evaluate the proposed filter. It is shown that this filter is superior to other methods in both practicality and efficiency.