Rasmus Ramsbøl Jensen

In vivo imaging of cortical pathology in multiple sclerosis using ultra-high field MRI

Objective: We used ultra-high field MRI to visualize cortical lesion types described by neuropathology in 16 patients with multiple sclerosis (MS) compared with 8 age-matched controls; to characterize the contrast properties of cortical lesions including T2*, T2, T1, and phase images; and to investigate the relationship between cortical lesion types and clinical data. Methods: We collected, on a 7-T scanner, 2-dimensional fast low-angle shot (FLASH)-T2*-weighted spoiled gradient-echo, T2-weighted turbo spin-echo (TSE) images (0.33 × 033 × 1 mm3), and a 3-dimensional magnetization-prepared rapid gradient echo. Results: Overall, 199 cortical lesions were detected in patients on both FLASH-T2* and T2-TSE scans. Seven-tesla MRI allowed for characterization of cortical plaques into type I (leukocortical), type II (intracortical), and type III/IV (subpial extending partly or completely through the cortical width) lesions as described histopathologically. Types III and IV were the most frequent type of cortical plaques (50.2%), followed by type I (36.2%) and type II (13.6%) lesions. Each lesion type was more frequent in secondary progressive than in relapsing–remitting MS. This difference, however, was significant only for type III/IV lesions. T2*-weighted images showed the highest, while phase images showed the lowest, contrast-to-noise ratio for all cortical lesion types. In patients, the number of type III/IV lesions was associated with greater disability (p < 0.02 by Spearman test) and older age (p < 0.04 by Spearman test). Conclusions: Seven-tesla MRI detected different histologic cortical lesion types in our small multiple sclerosis (MS) sample, suggesting, if validated in a larger population, that it may prove a valuable tool to assess the contribution of cortical MS pathology to clinical disability.

Large-Scale Data for Multiple-View Stereopsis

The seminal multiple-view stereo benchmark evaluations from Middlebury and by Strecha et al. have played a major role in propelling the development of multi-view stereopsis (MVS) methodology. The somewhat small size and variability of these data sets, however, limit their scope and the conclusions that can be derived from them. To facilitate further development within MVS, we here present a new and varied data set consisting of 80 scenes, seen from 49 or 64 accurate camera positions. This is accompanied by accurate structured light scans for reference and evaluation. In addition all images are taken under seven different lighting conditions. As a benchmark and to validate the use of our data set for obtaining reasonable and statistically significant findings about MVS, we have applied the three state-of-the-art MVS algorithms by Campbell et al., Furukawa et al., and Tola et al. to the data set. To do this we have extended the evaluation protocol from the Middlebury evaluation, necessitated by the more complex geometry of some of our scenes. The data set and accompanying evaluation framework are made freely available online. Based on this evaluation, we are able to observe several characteristics of state-of-the-art MVS, e.g. that there is a tradeoff between the quality of the reconstructed 3D points (accuracy) and how much of an object’s surface is captured (completeness). Also, several issues that we hypothesized would challenge MVS, such as specularities and changing lighting conditions did not pose serious problems. Our study finds that the two most pressing issues for MVS are lack of texture and meshing (forming 3D points into closed triangulated surfaces).

Analyzing Gait Using a Time-of-Flight Camera

An algorithm is created, which performs human gait analysis using spatial data and amplitude images from a Time-of-flight camera. For each frame in a sequence the camera supplies cartesian coordinates in space for every pixel. By using an articulated model the subject pose is estimated in the depth map in each frame. The pose estimation is based on likelihood, contrast in the amplitude image, smoothness and a shape prior used to solve a Markov random field. Based on the pose estimates, and the prior that movement is locally smooth, a sequential model is created, and a gait analysis is done on this model. The output data are: Speed, Cadence (steps per minute), Step length, Stride length (stride being two consecutive steps also known as a gait cycle), and Range of motion (angles of joints). The created system produces good output data of the described output parameters and requires no user interaction.

Analysis of Gait Using a Treadmill and a Time-of-Flight Camera

We present a system that analyzes human gait using a treadmill and a Time-of-flight camera. The camera provides spatial data with local intensity measures of the scene, and data are collected over several gait cycles. These data are then used to model and analyze the gait. For each frame the spatial data and the intensity image are used to fit an articulated model to the data using a Markov random field. To solve occlusion issues the model movement is smoothened providing the missing data for the occluded parts. The created model is then cut into cycles, which are matched and through Fourier fitting a cyclic model is created. The output data are: Speed, Cadence, Step length and Range-of-motion . The described output parameters are computed with no user interaction using a setup with no requirements to neither background nor subject clothing.

Genus zero graph segmentation

Fully automatic segmentation of intracranial volume.Equidistant subsampling with Delaunay tetrahedralisation for dense neighborhood connectivity without blocky grid-bias.Node clamping forces the segmentation to be consistent and of genus 0.Dice scores above 98% compared to manual reference. The intracranial volume (ICV) in children with premature fusion of one or more sutures in the calvaria is of interest due to the risk of increased intracranial pressure. Challenges for automatic estimation of ICV include holes in the skull e.g. the foramen magnum and fontanelles. In this paper, we present a fully automatic 3D graph-based method for segmentation of the ICV in non-contrast CT scans. We reformulate the ICV segmentation problem as an optimal genus 0 segmentation problem in a volumetric graph. The graph is the result of a volumetric spherical subsampling. The equidistantly sampled data points are connected using Delaunay tetrahedralisation creating a highly connected neighborhood. A Markov Random Field (MRF) is constructed on the graph with probabilities learned from an Expectation Maximisation algorithm matching a Mixture of Gaussians to the data. The result of the MRF segmentation is compared to manual segmentations performed by an expert. We have achieved very high Dice scores ranging from 98.14% to 99.00%, while volume deviation from the manual segmentation ranges from 0.7% to 3.7%. The Hausdorff distance, which shows the maximum error from automatic to manual segmentation, ranges from 4.73 to 9.81mm. Since this is sensitive to single error, we have also found the 95% Hausdorff distance, which ranges from 1.10 to 3.65mm. The segmentation is very consistent with the reference and differs only in difficult areas, where it seems that our method is much more slice-wise consistent than a manual segmentation. The proposed method is expected to perform well for other volumetric segmentations.

3D Surface Realignment Tracking for Medical Imaging: A Phantom Study with PET Motion Correction

We present a complete system for motion correction in high resolution brain positron emission tomography (PET) imaging. The system is based on a compact structured light scanner mounted above the patient tunnel of the Siemens High Resolution Research Tomograph (HRRT) PET brain scanner. The structured light system is equipped with a near infrared diode and uses phase-shift interferometry (PSI) to compute 3D point clouds of the forehead of the patient. These 3D point clouds are progressively aligned to a reference surface, thereby giving the head pose changes. The estimated pose changes are used to reposition a sequence of reconstructed PET frames. To align the structured light system with the PET coordinate system, a novel registration algorithm based on the PET transmission scan and an initial surface has been developed. The performance of the complete setup has been evaluated using a custom-made phantom, based on a plastic mannequin head equipped with two positron-emitting line sources. Two experiments were performed. The first simulates rapid and short head movements, while the second simulates slow and continuous movements. In both cases, the system was able to produce PET scans with focused PET reconstructions. The system is nearly ready for clinical testing.

Fast and practical head tracking in brain imaging with Time-of-Flight camera

This paper investigates the potential use of Time-of-Flight cameras (TOF) for motion correction in medical brain scans. TOF cameras have previously been used for tracking purposes, but recent progress in TOF technology has made it relevant for high speed optical tracking in high resolution medical scanners. Particularly in MRI and PET, the newest generation of TOF cameras could become a method of tracking small and large scale patient movement in a fast and user friendly way required in clinical environments. We present a novel methodology for fast tracking from TOF point clouds without the need of expensive triangulation and surface reconstruction. Tracking experiments with a motion controlled head phantom were performed with a translational tracking error below 2mm and a rotational tracking error below 0.5°.

Statistical surface recovery: a study on ear canals

We present a method for surface recovery in partial surface scans based on a statistical model. The framework is based on multivariate point prediction, where the distribution of the points are learned from an annotated data set. The training set consist of surfaces with dense correspondence that are Procrustes aligned. The average shape and point covariances can be estimated from this set. It is shown how missing data in a new given shape can be predicted using the learned statistics. The method is evaluated on a data set of 29 scans of ear canal impressions. By using a leave-one-out approach we reconstruct every scan and compute the point-wise prediction error. The evaluation is done for every point on the surface and for varying hole sizes. Compared to state-of-the art surface reconstruction algorithm, the presented methods gives very good prediction results.

Wearable Gaze Trackers: Mapping Visual Attention in 3D

The study of visual attention in humans relates to a wide range of areas such as: psychology, cognition, usability, and marketing. These studies have been limited to fixed setups with respondents sitting in front of a monitor mounted with a gaze tracking device. The introduction of wearable mobile gaze trackers allows respondents to move freely in any real world 3D environment, removing the previous restrictions.

Photon rectify: undistort any footage on the timeline

Action cameras have allowed footage from previously unseen points of view, drones allow for innovative aerial shots, which a few years ago, were either impossible or required a helicopter, and finally DSLR video and lens adaptors have made any lens available for video production. These advances provide especially low-budget movie productions with completely new flexibility and potential.