Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms

Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugular vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on cervical sonograms, followed by a non-linear transformation to achieve a more precise registration. Voxelmorph (VM), a learning-based library for deformable image registration using a convolutional neural network (CNN) with U-Net structure was used for non-linear transformation. The impact of principal component analysis (PCA)-based pre-denoising of patient individual images, as well as the impact of modified net structures with differing complexities on registration results were examined visually and quantitatively, the latter using metrics for deformation and image similarity. Using the PCA-approximated cervical sonograms resulted in decreased mean deformation lengths between 18% and 66% compared to their original image counterparts, depending on net structure. In addition, reducing the number of convolutional layers led to improved image similarity with PCA images, while worsening in original images. Despite a large reduction of network parameters, no overall decrease in registration quality was observed, leading to the conclusion that the original net structure is oversized for the task at hand.Comment: 8 pages, 7 figures Presented at WSCG 202

Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms

Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugu- lar vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on cervical sonograms, followed by a non-linear transformation to achieve a more precise registration. Voxelmorph (VM), a learning-based library for deformable image registration using a convolutional neural network (CNN) with U-Net structure was used for non-linear transformation. The impact of principal component analysis (PCA)-based pre-denoising of patient individual images, as well as the impact of modified net structures with differing complexities on registration results were examined visually and quan- titatively, the latter using metrics for deformation and image similarity. Using the PCA-approximated cervical sonograms resulted in decreased mean deformation lengths between 18% and 66% compared to their original image counterparts, depending on net structure. In addition, reducing the number of convolutional layers led to improved image similarity with PCA images, while worsening in original images. Despite a large reduction of network parameters, no overall decrease in registration quality was observed, leading to the conclusion that the original net structure is oversized for the task at hand

A COM-based toolkit for real time visualization

Collaborative software development in different languages is not unusual, but leads to minor resource utilization during collaboration as a result of porting or reprogramming needs. Additionally in cooperative projects, frequently legal and market economic issues prohibit an exchange of source code between the project partners. Combining modules from different languages is possible using the Component Object Model (COM). Additionally COM offers an efficient way to combine modules from several development teams. To solve the common issues of collaborative software development and to fulfil the needs of a real time visualization toolkit, “RTVCOM” was designed and realized. To demonstrate the capability of this approach an example client was developed that combines COM components written in OpenCL C, OpenGLSL, C++ and C#. It that processes 3D+t ultrasound data at 45.2 MB/s reconstructs the associated volume data and visualizes them in real time. The visualization is fully interactive, and different pre- and post-processing filters can be applied

Estimation of mitral valve hinge point coordinates - deep neural net for echocardiogram segmentation

Cardiac image segmentation is a powerful tool in regard to diagnostics and treatment of cardiovascular diseases. Purely feature-based detection of anatomical structures like the mitral valve is a laborious task due to specifically required feature engineering and is especially challenging in echocardiograms, because of their inherently low contrast and blurry boundaries between some anatomical structures. With the publication of further annotated medical datasets and the increase in GPU processing power, deep learning-based methods in medical image segmentation became more feasible in the past years. We propose a fully automatic detection method for mitral valve hinge points, which uses a U-Net based deep neural net to segment cardiac chambers in echocardiograms in a first step, and subsequently extracts the mitral valve hinge points from the resulting segmentations in a second step. Results measured with this automatic detection method were compared to reference coordinate values, which with median absolute hinge point coordinate errors of 1.35 mm for the x- (15-85 percentile range: [0.3 mm; 3.15 mm]) and 0.75 mm for the y- coordinate (15-85 percentile range: [0.15 mm; 1.88 mm])

Using smart glasses for ultrasound diagnostics

Ultrasound has been established as a diagnostic tool in a wide range of applications. Especially for beginners, the alignment of sectional images to patient’s spatial anatomy can be cumbersome. A direct view onto the patient’s anatomy while regarding ultrasound images may help to overcome unergonomic examination

Measurement of needle susceptibility artifacts in magnetic resonance images

The success of minimally invasive procedures under MR-guidance can be increased by the knowledge of the current needle pose. We hypothesize that a one-toone mapping exists between the needle orientation with respect to the static magnetic field and the cross-sectional shape of the needle’s susceptibility artifact. For this purpose, we derived a mathematical model, which describes the cross sectional geometry of the needle artifact. It is approximated by two ellipses. Certain parameters of these ellipses can be utilized for mapping the geometry of the needle artifact onto the needle orientation. The relation between the two ellipse parameters α (inclination of the semi-major axis) and b (length of the semi-minor axis) and the needle’s azimuth angle can be approximated by linear regression in a certain angle interval. A combination of these two ellipse parameters is suitable for estimating the needle’s azimuth angle within a range between 0° and 60°