arXiv subjects

Frank Preiswerk

Publications and source records attributed to Frank Preiswerk.

4 recordsLinked to original sources

arXiv2021

Synthesizing dynamic MRI using long-term recurrent convolutional networks

A method is proposed for converting raw ultrasound signals of respiratory organ motion into high frame rate dynamic MRI using a long-term recurrent convolutional neural network. Ultrasound signals were acquired using a single-element transducer, referred to here as `organ-configuration motion' (OCM) sensor, while sagittal MR images were simultaneously acquired. Both streams of data were used for training a cascade of convolutional layers, to extract relevant features from raw ultrasound, followed by a recurrent neural network, to learn its temporal dynamics. The network was trained with MR images on the output, and was employed to predict MR images at a temporal resolution of 100 frames per second, based on ultrasound input alone, without any further MR scanner input. The method was validated on 7 subjects.

eess.IV↗

arXiv2020

On the Applicability of Registration Uncertainty

Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as a means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.

cs.CV↗

arXiv2019

Open-source neuronavigation for multimodal non-invasive brain stimulation using 3D Slicer

In recent years, non-invasive neuro-modulation methods such as Focused Ultrasound (FUS) have gained popularity. The aim of this work is to introduce the use of existing open-source technology for surgical navigation to the field of multimodal non-invasive brain stimulation. Unlike homegrown and commercial systems, the use of well-documented, well maintained, and freely available open-source components minimizes the learning curve, maximizes technology transfer outcome, and fosters reproducible science for complex, guided neuromodulation systems. The described system significantly lowers the entry bar to clinical research and experimentation in the field of non-invasive brain stimulation. Our contribution is two-fold. First, a high-level overview of the components of the descried system is given in this manuscript. Second, all files are made available online, with a comprehensive step-by-step manual, quickly allowing researchers to build a custom system. A spatial accuracy of 0.93 mm was found through validation using a robotic positioning system.

physics.med-ph↗

arXiv2018

A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation

A reliable Ultrasound (US)-to-US registration method to compensate for brain shift would substantially improve Image-Guided Neurological Surgery. Developing such a registration method is very challenging, due to factors such as missing correspondence in images, the complexity of brain pathology and the demand for fast computation. We propose a novel feature-driven active framework. Here, landmarks and their displacement are first estimated from a pair of US images using corresponding local image features. Subsequently, a Gaussian Process (GP) model is used to interpolate a dense deformation field from the sparse landmarks. Kernels of the GP are estimated by using variograms and a discrete grid search method. If necessary, the user can actively add new landmarks based on the image context and visualization of the uncertainty measure provided by the GP to further improve the result. We retrospectively demonstrate our registration framework as a robust and accurate brain shift compensation solution on clinical data acquired during neurosurgery.

cs.CV↗