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

Abstract

Purpose 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. Theory and Methods Multi-echo data are acquired using a motion-robust radial stack-of-stars 3D GRE sequence with bipolar readout. To obtain fat/water maps, a model-based reconstruction is employed that accounts for the off-resonant blurring of fat and integrates both compressed sensing and parallel imaging. The approach additionally enables generation of respiration-resolved fat/water maps by detecting motion from k-space data and reconstructing different respiration states. Furthermore, an extension is described for dynamic contrast-enhanced fat-water-separated measurements. Results Uniform and robust fat/water separation is demonstrated in several clinical applications, including free-breathing non-contrast abdominal examination of adults and a pediatric subject with both motion-averaged and motion-resolved reconstructions, as well as in a non-contrast breast exam. Furthermore, dynamic contrast-enhanced fat/water imaging with high temporal resolution is demonstrated in the abdomen and breast. Conclusion The described framework provides a viable approach for motion-robust fat/water separation and promises particular value for clinical applications that are currently limited by the breath-holding capacity or cooperation of patients.

Compressed sensing for body MRI

“…One promising example is the incorporation of a specific signal model into the reconstruction problem, in which parameter maps can be estimated directly from the acquired k-space data. For example, separated water and fat maps can be directly estimated from the acquired k-space data in chemical shift imaging (96–99). Perfusion maps with parameters such as K trans and V e , can be estimated directly by including perfusion tracer-kinetic models into the reconstruction process (100).…”

Hybrid T2‐ and T1‐weighted radial acquisition for free‐breathing abdominal examination

“…This results in a non‐continuous curve for which applying additional smoothing to filter out residual biasing components from other sources may not work. To overcome this, the combination of a PCA with SOBI has been introduced for estimation of the signal, which worked well in all acquired data sets and might be promising also for related applications, such as XD‐GRASP or XD‐Dixon‐RAVE . However, the robustness of the approach still needs to be evaluated in a larger cohort of subjects.…”

“…To generate water and fat maps, Eq. () was solved using the previously described model‐based approach . The respiratory signal for motion weighting was extracted from the first Dixon‐RAVE echo, which was also used for motion‐weighted reconstruction of the T 2 ‐weighted FSE data by solving Eq.…”

Comprehensive Dynamic Contrast-Enhanced 3D Magnetic Resonance Imaging of the Breast With Fat/Water Separation and High Spatiotemporal Resolution Using Radial Sampling, Compressed Sensing, and Parallel Imaging

“…In each repetition time, three echoes were sampled with a blipped bipolar readout. 15 The proposed Dixon-RAVE acquisition was added to the routine breast protocol as follows. Before injection of contrast agent, both a non-fat-suppressed and a fat-suppressed T1-weighted Cartesian VIBE scan were performed as part of the conventional protocol.…”

“…11–13 However, using a TWIST-type approach can introduce errors in the signal-intensity time course due to the use of a view-shared reconstruction. 13,14 An alternative approach is the recently described Dixon-RAVE (radial volumetric encoding) method, 15 which combines radial sampling, fat/water separation, compressed sensing, and parallel imaging to achieve DCE imaging with robust fat suppression and high spatial as well as high temporal resolution. Besides contrast-enhanced images, pre-contrast fat-suppressed and non-fat-suppressed images can be extracted from the same dataset, thus enabling comprehensive T1-weighted DCE-MRI with reduced overall scan time.…”