Tim Sprenger

Real valued diffusion-weighted imaging using decorrelated phase filtering.

Because of the intrinsic low signal‐to‐noise ratio in diffusion‐weighted imaging (DWI), magnitude processing often causes an overestimation of the signals amplitude. This results in low‐estimation accuracy of diffusion models and reduced contrast because of a superposition of the image signal and the noise floor. We adopt a new phase correction (PC) technique that yields real valued diffusion data while maintaining a Gaussian noise distribution.

Model-free novelty-based diffusion MRI

Many limitations of diffusion MRI are due to the instability of the model fitting procedure. Major shortcomings of the model-based approach are a partial information loss due to model simplicity, long scan time requirements due to fitting instability, and the lack of knowledge about how the parameters of a given model would respond to previously unseen microstructural changes, possibly failing to detect certain previously unseen pathologies. Here we show that diffusion MRI pathology detection is feasible without any models and without any prior knowledge of specific pathological changes whatsoever. Instead, raw q-space measurements are used directly without a model, only healthy population data is used for reference, and any deviations in a patient dataset from the healthy reference database are detected using novelty detection methods. This is done in each voxel independently, i.e. without spatial bias.

Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compressed Sensing

Magnetic resonance fingerprinting (MRF) is a novel technique that allows for the fast and simultaneous quantification of multiple tissue properties, progressing from qualitative images, such as T1- or T2-weighted images commonly used in clinical routines, to quantitative parametric maps. MRF consists of two main elements: accelerated pseudorandom acquisitions that create unique signal evolutions over time and the voxel-wise matching of these signals to a dictionary simulated using the Bloch equations. In this study, we propose to increase the performance of MRF by not only considering the simulated temporal signal, but a full spatiotemporal neighborhood for parameter reconstruction. We achieve this goal by first training a dictionary from a set of spatiotemporal image patches and subsequently coupling the trained dictionary with an iterative projection algorithm consistent with the theory of compressed sensing (CS). Using data from BrainWeb, we show that the proposed patch-based reconstruction can accurately recover T1 and T2 maps from highly undersampled k-space measurements, demonstrating the added benefit of using spatiotemporal dictionaries in MRF.

Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging.

Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized.

Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate

Recently, super-resolution methods for diffusion MRI capable of retrieving high-resolution diffusion-weighted images were proposed, yielding a resolution beyond the scanner hardware limitations. These techniques rely on acquiring either one isotropic or several anisotropic low-resolution versions of each diffusion-weighted image. In the present work, a variational formulation of joint super-resolution of all diffusion-weighted images is presented which takes advantage of interrelations between similar diffusion-weighted images. These interrelations allow to use only one anisotropic low-resolution version of each diffusion-weighted image and to retrieve its missing high-frequency components from other images which have a similar q-space coordinate but a different resolution-anisotropy orientation. An acquisition scheme that entails complementary resolution-anisotropy among neighboring q-space points is introduced. High-resolution images are recovered at reduced scan time requirements compared to state-of-the-art anisotropic super-resolution methods. The introduced principles of joint super-resolution thus have the potential to further improve the performance of super-resolution methods.

Bias and Precision Analysis of Diffusional Kurtosis Imaging for Different Acquisition Schemes

Diffusional kurtosis imaging (DKI) is an approach to characterizing the non‐Gaussian fraction of water diffusion in biological tissue. However, DKI is highly susceptible to the low signal‐to‐noise ratio of diffusion‐weighted images, causing low precision and a significant bias due to Rician noise distribution. Here, we evaluate precision and bias using weighted linear least squares fitting of different acquisition schemes including several multishell schemes, a diffusion spectrum imaging (DSI) scheme, as well as a compressed sensing reconstruction of undersampled DSI scheme.

Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation

A joint reconstruction framework for multi-contrast MR images is presented and evaluated. The evaluation takes place in function of quality criteria based on reconstruction results and performance in the automatic segmentation of Multiple Sclerosis (MS) lesions. We show that joint reconstruction can effectively recover artificially corrupted images and is robust to noise.

Model‐based denoising in diffusion‐weighted imaging using generalized spherical deconvolution

Diffusion MRI often suffers from low signal‐to‐noise ratio, especially for high b‐values. This work proposes a model‐based denoising technique to address this limitation.

Rekonstruktion zerebraler Gefässnetzwerke aus in-vivo μMRA mittels physiologischem Vorwissen zur lokalen Gefässgeometrie

In diesem Beitrag adressieren wir die Rekonstruktion zerebrovaskul arer Netzwerke mit einem Ansatz, der es erlaubt, Vorwissen uber physiologisch plausible Strukturen zu berucksichtigen und gegen- uber Bildinformation abzuwagen. Ausgehend von einem uberkonnektierten Netzwerk wird in einer globalen Optimierung – unter Berucksichtigung von geometrischer Konstellation, globaler Konnektivitat und Bildintensit aten – das plausibelste Netzwerk bestimmt. Ein statistisches Modell zur Bewertung geometrischer Beziehungen zwischen Segmenten und Bifurkationen wird anhand eines hochaufgelosten Netzwerks gelernt, welches aus einem μCT (Mikrocomputertomographie) eines zerebrovaskularen Korrosionspraparats einer Maus gewonnen wird. Die Methode wird experimentell auf in-vivo μMRA (Magnetresonanzmikroangiographie) Datensatze von Mausgehirnen angewandt und Eigenschaften der resultierenden Netzwerke im Vergleich zu Standardverfahren diskutiert.