Tilman Johannes Sumpf

Model‐based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin‐echo MRI

To develop a model‐based reconstruction technique for T2 mapping based on multi‐echo spin‐echo MRI sequences with highly undersampled Cartesian data encoding.

Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis

In patients with multiple sclerosis (MS), conventional magnetic resonance imaging (MRI) provides only limited insights into the nature of brain damage with modest clinic‐radiological correlation. In this study, we applied recent advances in MRI techniques to study brain microstructural alterations in early relapsing‐remitting MS (RRMS) patients with minor deficits. Further, we investigated the potential use of advanced MRI to predict functional performances in these patients.

Fast T2 Mapping With Improved Accuracy Using Undersampled Spin-Echo MRI and Model-Based Reconstructions With a Generating Function

A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo magnetic resonance imaging (MRI) data. The technique employs an advanced signal model for T2 relaxation that accounts for contributions from indirect echoes in a train of multiple spin echoes. An iterative solution of the nonlinear inverse reconstruction problem directly estimates spin-density and T2 maps from undersampled raw data. The algorithm is validated for simulated data as well as phantom and human brain MRI at 3T. The performance of the advanced model is compared to conventional pixel-based fitting of echo-time images from fully sampled data. The proposed method yields more accurate T2 values than the mono-exponential model and allows for retrospective undersampling factors of at least 6. Although limitations are observed for very long T2 relaxation times, respective reconstruction problems may be overcome by a gradient dampening approach. The analytical gradient of the utilized cost function is included as Appendix . The source code is made available to the community.

Multicontrast connectometry: A new tool to assess cerebellum alterations in early relapsing-remitting multiple sclerosis.

Background: Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast “connectometry” approach to assess the structural and functional integrity of cerebellar networks and connectivity in early MS. Methods: We used diffusion spectrum and resting‐state functional MRI (rs‐fMRI) to establish the structural and functional cerebellar connectomes in 28 early relapsing‐remitting MS patients and 16 healthy controls (HC). We performed multicontrast “connectometry” by quantifying multiple MRI parameters along the structural tracts (generalized fractional anisotropy‐GFA, T1/T2 relaxation times and magnetization transfer ratio) and functional connectivity measures. Subsequently, we assessed multivariate differences in local connections and network properties between MS and HC subjects; finally, we correlated detected alterations with lesion load, disease duration, and clinical scores. Results: In MS patients, a subset of structural connections showed quantitative MRI changes suggesting loss of axonal microstructure and integrity (increased T1 and decreased GFA, P < 0.05). These alterations highly correlated with motor, memory and attention in patients, but were independent of cerebellar lesion load and disease duration. Neither network organization nor rs‐fMRI abnormalities were observed at this early stage. Conclusion: Multicontrast cerebellar connectometry revealed subtle cerebellar alterations in MS patients, which were independent of conventional disease markers and highly correlated with patient function. Future work should assess the prognostic value of the observed damage. Hum Brain Mapp 36:1609–1619, 2015.

Reply to: MRI resolution enhancement: How useful are shifted images obtained by changing the demodulation frequency?

Super-resolution reconstruction is a process by which a set of different low resolution images of the same object are used to create an enhanced, higher resolution image of that object. Recently there has been debate amongst researchers whether it is possible to obtain in-plane image enhancement using a set of low resolution magnetic resonance images, acquired by making small, independent changes to the demodulation frequency. We show that shifted low-resolution images contain different information that can be used to obtain denser sampling, leading to image enhancement. We conclude this from specific phantom experiments, applying signal processing sampling theory and taking into consideration the relative sampling of the point spread function with respect to the location of signal sources. Furthermore, the maximum achievable resolution for Fourier encoded MRI data at a boundary or object feature is governed by the effective width of the point spread function or the Fourier pixel size determined by the extent of k-space; this is verified experimentally.

Model-based reconstruction for T1 mapping using single-shot inversion-recovery radial FLASH

Quantitative parameter mapping in MRI is typically performed as a two‐step procedure where serial imaging is followed by pixelwise model fitting. In contrast, model‐based reconstructions directly reconstruct parameter maps from raw data without explicit image reconstruction. Here, we propose a method that determines T1 maps directly from multi‐channel raw data as obtained by a single‐shot inversion‐recovery radial FLASH acquisition with a Golden Angle view order. Joint reconstruction of a T1, spin‐density and flip‐angle map is formulated as a nonlinear inverse problem and solved by the iteratively regularized Gauss‐Newton method. Coil sensitivity profiles are determined from the same data in a preparatory step of the reconstruction. Validations included numerical simulations, in vitro MRI studies of an experimental T1 phantom, and in vivo studies of brain and abdomen of healthy subjects at a field strength of 3 T. The results obtained for a numerical and experimental phantom demonstrated excellent accuracy and precision of model‐based T1 mapping. In vivo studies allowed for high‐resolution T1 mapping of human brain (0.5–0.75 mm in‐plane, 4 mm section thickness) and liver (1.0 mm, 5 mm section) within 3.6–5 s. In conclusion, the proposed method for model‐based T1 mapping may become an alternative to two‐step techniques, which rely on model fitting after serial image reconstruction. More extensive clinical trials now require accelerated computation and online implementation of the algorithm.

Accelerated T2 mapping combining parallel MRI and model-based reconstruction: GRAPPATINI: Accelerated T2 Mapping

Quantitative T2 measurements are sensitive to intra‐ and extracellular water accumulation and myelin loss. Therefore, quantitative T2 promises to be a good biomarker of disease. However, T2 measurements require long acquisition times.