Kai Tobias Block

Golden‐angle radial sparse parallel MRI: Combination of compressed sensing, parallel imaging, and golden‐angle radial sampling for fast and flexible dynamic volumetric MRI

To develop a fast and flexible free‐breathing dynamic volumetric MRI technique, iterative Golden‐angle RAdial Sparse Parallel MRI (iGRASP), that combines compressed sensing, parallel imaging, and golden‐angle radial sampling.

XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing

To develop a novel framework for free‐breathing MRI called XD‐GRASP, which sorts dynamic data into extra motion‐state dimensions using the self‐navigation properties of radial imaging and reconstructs the multidimensional dataset using compressed sensing.

Rapid and accurate T2 mapping from multi–spin-echo data using Bloch-simulation-based reconstruction

Quantitative T2‐relaxation‐based contrast has the potential to provide valuable clinical information. Practical T2‐mapping, however, is impaired either by prohibitively long acquisition times or by contamination of fast multiecho protocols by stimulated and indirect echoes. This work presents a novel postprocessing approach aiming to overcome the common penalties associated with multiecho protocols, and enabling rapid and accurate mapping of T2 relaxation values.

Self-gated MRI motion modeling for respiratory motion compensation in integrated PET/MRI

Accurate localization and uptake quantification of lesions in the chest and abdomen using PET imaging is challenged by respiratory motion occurring during the exam. This work describes how a stack-of-stars MRI acquisition on integrated PET/MRI systems can be used to derive a high-resolution motion model, how many respiratory phases need to be differentiated, how much MRI scan time is required, and how the model is employed for motion-corrected PET reconstruction. MRI self-gating is applied to perform respiratory gating of the MRI data and simultaneously acquired PET raw data. After gated PET reconstruction, the MRI motion model is used to fuse the individual gates into a single, motion-compensated volume with high signal-to-noise ratio (SNR). The proposed method is evaluated in vivo for 15 clinical patients. The gating requires 5-7 bins to capture the motion to an average accuracy of 2mm. With 5 bins, the motion-modeling scan can be shortened to 3-4 min. The motion-compensated reconstructions show significantly higher accuracy in lesion quantification in terms of standardized uptake value (SUV) and different measures of lesion contrast compared to ungated PET reconstruction. Furthermore, unlike gated reconstructions, the motion-compensated reconstruction does not lead to SNR loss.

Multiparametric imaging with heterogeneous radiofrequency fields.

Magnetic resonance imaging (MRI) has become an unrivalled medical diagnostic technique able to map tissue anatomy and physiology non-invasively. MRI measurements are meticulously engineered to control experimental conditions across the sample. However, residual radiofrequency (RF) field inhomogeneities are often unavoidable, leading to artefacts that degrade the diagnostic and scientific value of the images. Here we show that, paradoxically, these artefacts can be eliminated by deliberately interweaving freely varying heterogeneous RF fields into a magnetic resonance fingerprinting data-acquisition process. Observations made based on simulations are experimentally confirmed at 7 Tesla (T), and the clinical implications of this new paradigm are illustrated with in vivo measurements near an orthopaedic implant at 3T. These results show that it is possible to perform quantitative multiparametric imaging with heterogeneous RF fields, and to liberate MRI from the traditional struggle for control over the RF field uniformity.

Respiratory Motion-Resolved Compressed Sensing Reconstruction of Free-Breathing Radial Acquisition for Dynamic Liver Magnetic Resonance Imaging.

ObjectiveThis study aimed to demonstrate feasibility of free-breathing radial acquisition with respiratory motion-resolved compressed sensing reconstruction [extra-dimensional golden-angle radial sparse parallel imaging (XD-GRASP)] for multiphase dynamic gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced liver imaging, and to compare image quality to compressed sensing reconstruction with respiratory motion-averaging (GRASP) and prior conventional breath-held Cartesian-sampled data sets [BH volume interpolated breath-hold examination (VIBE)] in same patients. Subjects and MethodsIn this Health Insurance Portability and Accountability Act–compliant prospective study, 16 subjects underwent free-breathing continuous radial acquisition during Gd-EOB-DTPA injection and had prior BH-VIBE available. Acquired data were reconstructed using motion-averaging GRASP approach in which consecutive 84 spokes were grouped in each contrast-enhanced phase for a temporal resolution of approximately 14 seconds. Additionally, respiratory motion-resolved reconstruction was performed from the same k-space data by sorting each contrast-enhanced phase into multiple respiratory motion states using compressed sensing algorithm named XD-GRASP, which exploits sparsity along both the contrast-enhancement and respiratory-state dimensions.Contrast-enhanced dynamic multiphase XD-GRASP, GRASP, and BH-VIBE images were anonymized, pooled together in a random order, and presented to 2 board-certified radiologists for independent evaluation of image quality, with higher score indicating more optimal examination. ResultsThe XD-GRASP reconstructions had significantly (all P < 0.05) higher overall image quality scores compared to GRASP for early arterial (reader 1: 4.3 ± 0.6 vs 3.31 ± 0.6; reader 2: 3.81 ± 0.8 vs 3.38 ± 0.9) and late arterial (reader 1: 4.5 ± 0.6 vs 3.63 ± 0.6; reader 2: 3.56 ± 0.5 vs 2.88 ± 0.7) phases of enhancement for both readers. The XD-GRASP also had higher overall image quality score in portal venous phase, which was significant for reader 1 (4.44 ± 0.5 vs 3.75 ± 0.8; P = 0.002). In addition, the XD-GRASP had higher overall image quality score compared to BH-VIBE for early (reader 1: 4.3 ± 0.6 vs 3.88 ± 0.6; reader 2: 3.81 ± 0.8 vs 3.50 ± 1.0) and late (reader 1: 4.5 ± 0.6 vs 3.44 ± 0.6; reader 2: 3.56 ± 0.5 vs 2.94 ± 0.9) arterial phases. ConclusionFree-breathing motion-resolved XD-GRASP reconstructions provide diagnostic high-quality multiphase images in patients undergoing Gd-EOB-DTPA–enhanced liver examination.

Compressed sensing for body MRI

The introduction of compressed sensing for increasing imaging speed in magnetic resonance imaging (MRI) has raised significant interest among researchers and clinicians, and has initiated a large body of research across multiple clinical applications over the last decade. Compressed sensing aims to reconstruct unaliased images from fewer measurements than are traditionally required in MRI by exploiting image compressibility or sparsity. Moreover, appropriate combinations of compressed sensing with previously introduced fast imaging approaches, such as parallel imaging, have demonstrated further improved performance. The advent of compressed sensing marks the prelude to a new era of rapid MRI, where the focus of data acquisition has changed from sampling based on the nominal number of voxels and/or frames to sampling based on the desired information content. This article presents a brief overview of the application of compressed sensing techniques in body MRI, where imaging speed is crucial due to the presence of respiratory motion along with stringent constraints on spatial and temporal resolution. The first section provides an overview of the basic compressed sensing methodology, including the notion of sparsity, incoherence, and nonlinear reconstruction. The second section reviews state‐of‐the‐art compressed sensing techniques that have been demonstrated for various clinical body MRI applications. In the final section, the article discusses current challenges and future opportunities.

Accelerated and motion‐robust in vivo T2 mapping from radially undersampled data using bloch‐simulation‐based iterative reconstruction

Development of a quantitative transverse relaxation time (T2)‐mapping platform that operates at clinically feasible timescales by employing advanced image reconstruction of radially undersampled multi spin‐echo (MSE) datasets.

Influence of temporal regularization and radial undersampling factor on compressed sensing reconstruction in dynamic contrast enhanced MRI of the breast

To evaluate the influence of temporal sparsity regularization and radial undersampling on compressed sensing reconstruction of dynamic contrast‐enhanced (DCE) MRI, using the iterative Golden‐angle RAdial Sparse Parallel (iGRASP) MRI technique in the setting of breast cancer evaluation.

Free‐breathing volumetric fat/water separation by combining radial sampling, compressed sensing, and parallel imaging

Conventional fat/water separation techniques require that patients hold breath during abdominal acquisitions, which often fails and limits the achievable spatial resolution and anatomic coverage. This work presents a novel approach for free‐breathing volumetric fat/water separation.