Wolfgang Wein

Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention.

The fusion of tracked ultrasound with CT has benefits for a variety of clinical applications, however extensive manual effort is usually required for correct registration. We developed new methods that allow one to simulate medical ultrasound from CT in real-time, reproducing the majority of ultrasonic imaging effects. They are combined with a robust similarity measure that assesses the correlation of a combination of signals extracted from CT with ultrasound, without knowing the influence of each signal. This serves as the foundation of a fully automatic registration, that aligns a 3D ultrasound sweep with the corresponding tomographic modality using a rigid or an affine transformation model, without any manual interaction. These techniques were evaluated in a study involving 25 patients with indeterminate lesions in liver and kidney. The clinical setup, acquisition and registration workflow is described, along with the evaluation of the registration accuracy with respect to physician-defined Ground Truth. Our new algorithm correctly registers without any manual interaction in 76% of the cases, the average RMS TRE over multiple target lesions throughout the liver is 8.1mm.

Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy

The efficacy of radiation therapy treatment depends on the patient setup accuracy at each daily fraction. A significant problem is reproducing the patient position during treatment planning for every fraction of the treatment process. We propose and evaluate an intensity based automatic registration method using multiple portal images and the pre-treatment CT volume. We perform both geometric and radiometric calibrations to generate high quality digitally reconstructed radiographs (DRRs) that can be compared against portal images acquired right before treatment dose delivery. We use a graphics processing unit (GPU) to generate the DRRs in order to gain computational efficiency. We also perform a comparative study on various similarity measures and optimization procedures. Simple similarity measure such as local normalized correlation (LNC) performs best as long as the radiometric calibration is carefully done. Using the proposed method, we achieved better than 1mm average error in repositioning accuracy for a series of phantom studies using two open field (i.e., 41 cm2) portal images with 90 degrees vergence angle.

Simulation and fully automatic multimodal registration of medical ultrasound

The fusion of 3D freehand ultrasound with CT and CTA has benefits for a variety of clinical applications, however a lot of manual work is usually required for correct registration. We developed new methods that allow one to simulate medical ultrasound from CT in real-time, reproducing the majority of ultrasonic imaging effects. The second novelty is a robust similarity measure that assesses the correlation of a combination of multiple signals extracted from CT with ultrasound, without knowing the influence of each signal. This serves as the foundation of a fully automatic registration, which aligns a freehand ultrasound sweep with the corresponding 3D modality using a rigid or an affine transformation model, without any manual interaction. We also present the used initialization, global and local parameter optimization schemes, and validation on abdominal CTA and ultrasound imaging of 10 patients.

Ultrasound confidence maps using random walks

Advances in ultrasound system development have led to a substantial improvement of image quality and to an increased use of ultrasound in clinical practice. Nevertheless, ultrasound attenuation and shadowing artifacts cannot be entirely avoided and continue to challenge medical image computing algorithms. We introduce a method for estimating a per-pixel confidence in the information depicted by ultrasound images, referred to as an ultrasound confidence map, which emphasizes uncertainty in attenuated and/or shadow regions. Our main novelty is the modeling of the confidence estimation problem within a random walks framework by taking into account ultrasound specific constraints. The solution to the random walks equilibrium problem is global and takes the entire image content into account. As a result, our method is applicable to a variety of ultrasound image acquisition setups. We demonstrate the applicability of our confidence maps for ultrasound shadow detection, 3D freehand ultrasound reconstruction, and multi-modal image registration.

Automatic registration and fusion of ultrasound with CT for radiotherapy

We present a framework for rigid registration of a set of B-mode ultrasound images to a CT scan in the context of Radiotherapy planning. Our main focus is on deriving an appropriate similarity measure based on the physical properties and artifacts of ultrasound. A combination of a weighted Mutual Information term, edge correlation, clamping to the skin surface and occlusion detection is able to assess the alignment of structures in ultrasound images and simulated slices generated from the CT data. Hence a set of ultrasound images, whose relative transformations are given by a magnetic tracking device, can be registered automatically to the CT scan. We validated our methods on neck data of patients with head and neck tumors and cervical lymph node metastases.

A survey of medical image registration on graphics hardware

The rapidly increasing performance of graphics processors, improving programming support and excellent performance-price ratio make graphics processing units (GPUs) a good option for a variety of computationally intensive tasks. Within this survey, we give an overview of GPU accelerated image registration. We address both, GPU experienced readers with an interest in accelerated image registration, as well as registration experts who are interested in using GPUs. We survey programming models and interfaces and analyze different approaches to programming on the GPU. We furthermore discuss the inherent advantages and challenges of current hardware architectures, which leads to a description of the details of the important building blocks for successful implementations.

Feature emphasis and contextual cutaways for multimodal medical visualization

Dense clinical data like 3D Computed Tomography (CT) scans can be visualized together with real-time imaging for a number of medical intervention applications. However, it is difficult to provide a fused visualization that allows sufficient spatial perception of the anatomy of interest, as derived from the rich pre-operative scan, while not occluding the real-time image displayed embedded within the volume. We propose an importance-driven approach that presents the embedded data such that it is clearly visible along with its spatial relation to the surrounding volumetric material. To support this, we present and integrate novel techniques for importance specification, feature emphasis, and contextual cutaway generation. We show results in a clinical context where a pre-operative CT scan is visualized alongside a tracked ultrasound image, such that the important vasculature is depicted between the viewpoint and the ultrasound image, while a more opaque representation of the anatomy is exposed in the surrounding area.

Automatic ultrasound–MRI registration for neurosurgery using the 2D and 3D LC2 Metric

To enable image guided neurosurgery, the alignment of pre-interventional magnetic resonance imaging (MRI) and intra-operative ultrasound (US) is commonly required. We present two automatic image registration algorithms using the similarity measure Linear Correlation of Linear Combination (LC(2)) to align either freehand US slices or US volumes with MRI images. Both approaches allow an automatic and robust registration, while the three dimensional method yields a significantly improved percentage of optimally aligned registrations for randomly chosen clinically relevant initializations. This study presents a detailed description of the methodology and an extensive evaluation showing an accuracy of 2.51mm, precision of 0.85mm and capture range of 15mm (>95% convergence) using 14 clinical neurosurgical cases.

Fast deformable registration of 3d-ultrasound data using a variational approach

We present an intensity based deformable registration algorithm for 3D ultrasound data. The proposed method uses a variational approach and combines the characteristics of a multilevel algorithm and the properties of ultrasound data in order to provide a fast and accurate deformable registration method. In contrast to previously proposed approaches, we use no feature points and no interpolation technique, but compute a dense displacement field directly. We demonstrate that this approach, although it includes solving large PDE systems, reduces the computation time if implemented using efficient numerical techniques. The performance of the algorithm is tested on multiple 3D US images of the liver. Validation is performed by simulations, similarity comparisons between original and deformed images, visual inspection of the displacement fields and visual assessment of the deformed images by physicians.

Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available.