Oliver Schütz

Spatial second-derivative image processing: an application to optical mammography to enhance the detection of breast tumors

We present an image-processing method that enhances the detection of regions of higher absorbance in optical mammograms. At the heart of this method lies a second-derivative operator that is commonly employed in edge-detection algorithms. The resulting images possess a high contrast, an automatic display scale, and a greater sensitivity to smaller departures from the local background absorbance. Moreover, the images are free of artifacts near the breast edge. This second-derivative method enhances the display of structural information in optical mammograms and may be used to robustly select areas of interest to be further analyzed spectrally to determine the oxygenation level of breast lesions.

Camera-Augmented Mobile C-arm (CAMC) Application: 3D Reconstruction Using a Low-Cost Mobile C-arm

High-end X-ray C-arm gantries have recently been used for 3D reconstruction. Low-cost mobile C-arms enjoy the advantage of being readily available and are often used as interventional imaging device, but do not guarantee the reproducibility of their motion. The calibration and reconstruction process used for high-end C-arms cannot be applied to them. Camera-Augmented Mobile C-arm (CAMC) is the solution we propose. A CCD camera is attached to the (motorized) mobile C-arm in order to calibrate the C-arm’s projection geometry on-line. The relationship between X-ray and camera projection geometry is characterized in an off-line calibration process. We propose the notion of Virtual Detector (VD), which enables us to describe both optical and X-ray geometry as pinhole cameras with fixed intrinsic parameters. We have conducted experiments in order to compare the results of CAMC calibration with the calibration method used for high-end C-arms and using an optical tracking system (Polaris from Northern Digital, Inc.).

Metal Artifact Reduction in Cone Beam Computed Tomography using Forward Projected Reconstruction Information

In this work we present a new method to reduce artifacts, produced by high-density objects, especially metal implants, in X-ray cone beam computed tomography (CBCT). These artifacts influence clinical diagnostics and treatments using CT data, if metal objects are located in the field of view (FOV). Our novel method reduces metal artifacts by virtually replacing the metal objects with tissue objects of the same shape. First, the considered objects must be segmented in the original 2D projection data as well as in a reconstructed 3D volume. The attenuation coefficients of the segmented voxels are replaced with adequate attenuation coefficients of tissue (or water), then the required parts of the volume are projected onto the segmented 2D pixels, to replace the original information. This corrected 2D data can then be reconstructed with reduced artifacts, i. e. all metal objects virtually vanished. After the reconstruction, the segmented 3D metal objects were re-inserted into the corrected 3D volume. Our method was developed for mobile C-arm CBCTs; as it is necessary that they are of low weight, the C-arm results in unpredictable distortion. This misalignment between the original 2D data and the forward projection of the reconstructed 3D volume must be adjusted before the correction of the segmented 2D pixels. We applied this technique to clinical data and will now present the results.

Using Near-Infrared Light to Detect Breast Cancer

Frequency-domain optical mammography is a safe and painless technique that may one day become a valuable tool for the detection and diagnosis of breast cancer.

Metal Artifact Reduction in CBCT Using Forward Projected Reconstruction Information and Mutual Information Realignment

High-density objects, especially metal parts, generate streak-like artifacts in cone-beam computed tomography (CBCT) images much like in computed tomography images. We present a novel method for metal artifact reduction in cone-beam computed tomography images via virtual replacement of the metal objects in the 3D volume with objects of identical geometry but water like x-ray attenuation coefficient. As the 3D scan procedure for CBCT of mobile C-arm systems is not completely reproducible in its projection geometry and computed forward projections are elementary for this method an additional correction based on mutual information must account for these misalignments. A reconstruction of the adapted 2D projection images generates a second 3D volume, where the originally metal objects now have a density like water and the streak-like artifacts are clearly reduced. Last step is to transfer the segmented metal parts of the first 3D volume into the metal and artifact free 3D volume of the second reconstruction. The proposed method is applied to clinical images and shows superior performance. The resulting reconstructed images show much reduced streak-like artifacts and related shadows.

Alignment correction during metal artifact reduction for CBCT using mutual information and edge filtering

In cone-beam computed tomography (CBCT) images, much like in computed tomography (CT) images, high-density objects, especially metal parts, generate streak-like artifacts. We present a novel method for metal artifact reduction in CBCT images via virtual replacement of the metal objects in the 3D volume with objects of identical geometry but water like x-ray attenuation coefficient. The algorithm, described in this article, is designed for mobile C-arm systems. As the 3D scan procedure for CBCT of mobile C-arm systems is not completely reproducible in its projection geometry and computed forward projections are elementary for this method an additional correction based on mutual information, using an edge filter as preprocessing step, must account for these misalignments. A reconstruction of the adapted 2D projection images generates a second 3D volume, where the originally metal objects now have a density like water and the streak-like artifacts are clearly reduced. Last step is to transfer the segmented metal parts of the first 3D volume into the metal and artifact free 3D volume of the second reconstruction. The proposed method is applied to clinical images and shows superior performance. The resulting reconstructed images show much reduced streak-like artifacts and related shadows.