Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts

Purpose Ultrasound compounding is to combine sonographic information captured from different angles and produce a single image. It is important for multi-view reconstruction, but as of yet there is no consensus on best practices for compounding. Current popular methods inevitably suppress or altogether leave out bright or dark regions that are useful and potentially introduce new artifacts. In this work, we establish a new algorithm to compound the overlapping pixels from different viewpoints in ultrasound. Methods Inspired by image fusion algorithms and ultrasound confidence, we uniquely leverage Laplacian and Gaussian pyramids to preserve the maximum boundary contrast without overemphasizing noise, speckles, and other artifacts in the compounded image, while taking the direction of the ultrasound probe into account. Besides, we designed an algorithm that detects the useful boundaries in ultrasound images to further improve the boundary contrast. Results We evaluate our algorithm by comparing it with previous algorithms both qualitatively and quantitatively, and we show that our approach not only preserves both light and dark details, but also somewhat suppresses noise and artifacts, rather than amplifying them. We also show that our algorithm can improve the performance of downstream tasks like segmentation. Conclusion Our proposed method that is based on confidence, contrast, and both Gaussian and Laplacian pyramids appears to be better at preserving contrast at anatomic boundaries while suppressing artifacts than any of the other approaches we tested. This algorithm may have future utility with downstream tasks such as 3D ultrasound volume reconstruction and segmentation.

Enhanced depth of field and resolution three-dimensional integral imaging by variable spatial filtering and intermediate-view reconstruction technique

In this paper, we propose a novel three-dimensional (3D) integral imaging system to simultaneously improve the depth of field (DOF), resolution, and image quality of reconstructed images by variable spatial filtering and intermediate-view reconstruction technology (IVRT). In the proposed method, the camera performs element images acquisition on a 3D scene with objects of different depths through a 2D grid plane. The reconstructed slice image and block matching algorithm are used to extract the depth of the element images. To improve the sharpness of depth, the Laplace operator is used to perform variable depth filtering on objects of different depths, and depth-enhanced all-filtering element images are obtained through simple pixel fusion. IVRT is applied to all-filtering element images to obtain more element images to reconstruct a resolution-enhanced 3D image. According to the energy of gradient (EOG) value and the Tenengrad value, the reconstruction image quality evaluation of the proposed method is improved by 7.63 and 4.81 times compared with the traditional method, respectively. By the proposed method of generating all-filtering element images and an IVRT in 3D integral imaging system, the experimental results demonstrate that the 3D reconstructed image has extended depth of field, enhanced resolution and improved image quality.

A residual dense network assisted sparse view reconstruction for breast computed tomography

To develop and investigate a deep learning approach that uses sparse-view acquisition in dedicated breast computed tomography for radiation dose reduction, we propose a framework that combines 3D sparse-view cone-beam acquisition with a multi-slice residual dense network (MS-RDN) reconstruction. Projection datasets (300 views, full-scan) from 34 women were reconstructed using the FDK algorithm and served as reference. Sparse-view (100 views, full-scan) projection data were reconstructed using the FDK algorithm. The proposed MS-RDN uses the sparse-view and reference FDK reconstructions as input and label, respectively. Our MS-RDN evaluated with respect to fully sampled FDK reference yields superior performance, quantitatively and visually, compared to conventional compressed sensing methods and state-of-the-art deep learning based methods. The proposed deep learning driven framework can potentially enable low dose breast CT imaging.

High-Resolution Three Dimensional MRI Findings After Plugging Surgery for Superior Semicircular Canal Dehiscence: A Case Report

: Superior semicircular canal dehiscence (SSCD) is known as abnormal communication of the superior semicircular canal (SCC) to the intracranial space secondary to a bony defect in the canal. Patients who are subjected to surgical repair usually have intractable symptoms, and recently, plugging of SCC using a transmastoid approach has been widely recommended. In this report, we describe a case of incomplete plugging for SSCD in a 37-year-old woman, along with the high-resolution three dimensional magnetic resonance imaging (3D MRI) findings using Pöschl view reconstruction. Postoperative MRI of 3D T2-wieghted sampling perfection with application optimized contrasts using different flip angle evolution (SPACE) Pöschl plane demonstrated an incomplete plugging of the SCC with partially visible perilymphatic fluid in the posterior limb above the common crus. A 3D fluid-attenuated inversion recovery (FLAIR) sequence showed an enhancement involving the vestibule and SCC, suggesting labyrinthitis. Although there are few reports about incomplete plugging for SSCD, this case could demonstrate postoperative status and complication after plugging of SSCD using a high-resolution 3D MRI sequences with Pöschl view reconstruction.